INTEGRATED RESOURCE PLANNING REPORT
TO THE
LOUISIANA PUBLIC SERVICE COMMISSION
February 6, 2015
ii
TABLE OF CONTENTS
EXECUTIVE SUMMARY ........................................................................................................................................... 1 1.0 INTRODUCTION ................................................................................................................................................... 1 1.1 OVERVIEW .............................................................................................................................................................. 1 1.2 IRP PROCESS ........................................................................................................................................................... 1 1.3 INTRODUCTION TO SWEPCO ..................................................................................................................................... 2 2.0 LOAD FORECAST AND FORECASTING METHODOLOGY ....................................................................... 5 2.1 SUMMARY OF SWEPCO LOAD FORECAST ..................................................................................................................... 5 2.1.1 Forecast Assumptions .................................................................................................................................. 5 2.1.2 Forecast Highlights ...................................................................................................................................... 6 2.2 OVERVIEW OF FORECAST METHODOLOGY ..................................................................................................................... 7 2.3 FORECAST METHODOLOGY FOR INTERNAL ENERGY REQUIREMENTS ................................................................................... 9 2.3.1 General ......................................................................................................................................................... 9 2.3.2 Short‐term Forecasting Models.................................................................................................................. 10 2.3.2.1 Residential and Commercial Energy Sales ............................................................................................................ 11 2.3.2.2 Industrial Energy Sales .......................................................................................................................................... 11 2.3.2.3 All Other Energy Sales .......................................................................................................................................... 11 2.3.3 Long‐term Forecasting Models .................................................................................................................. 12 2.3.3.1 Supporting Models ............................................................................................................................................... 12 2.3.3.2 Residential Energy Sales ....................................................................................................................................... 13 2.3.3.3 Commercial Energy Sales ..................................................................................................................................... 15 2.3.3.4 Industrial Energy Sales .......................................................................................................................................... 16 2.3.3.5 All Other Energy Sales .......................................................................................................................................... 16 2.3.4.1 Blending Short and Long‐Term Sales .................................................................................................................... 16 2.3.4.2 Billed/Unbilled Analysis ........................................................................................................................................ 17 2.3.4.3 Losses and Unaccounted‐For Energy .................................................................................................................... 17 2.4 FORECAST METHODOLOGY FOR SEASONAL PEAK INTERNAL DEMAND ............................................................................... 17 2.5 LOAD FORECAST RESULTS AND ISSUES ........................................................................................................................ 18 2.5.1 Load Forecast ............................................................................................................................................. 18 2.5.4 Prior Load Forecast Evaluation .................................................................................................................. 19 2.5.5 Weather Normalization ............................................................................................................................. 19 2.5.6 Significant Determinant Variables ............................................................................................................. 19 2.5.7 Changing Usage Patterns ........................................................................................................................... 20 2.5.8 DSM Impacts on the Load Forecast ............................................................................................................ 21 2.5.9 Losses and Unaccounted for Energy .......................................................................................................... 21 2.5.10 Interruptible Load .................................................................................................................................... 22 2.5.11 Blended Load Forecast ............................................................................................................................. 22 2.5.12 Large Customer Changes ......................................................................................................................... 22 2.6 LOAD FORECAST SCENARIOS ..................................................................................................................................... 23 3.0 RESOURCE EVALUATION ............................................................................................................................... 25 3.1 CURRENT RESOURCES .............................................................................................................................................. 25 3.2 EXISTING SWEPCO GENERATING RESOURCES ............................................................................................................. 25 3.3 CAPACITY IMPACTS OF ENVIRONMENTAL COMPLIANCE PLAN .......................................................................................... 28 3.4 EXISTING UNIT DISPOSITION ..................................................................................................................................... 28 3.5 ENVIRONMENTAL COMPLIANCE ................................................................................................................................. 31 3.5.1 Introduction ............................................................................................................................................... 31 iii
3.5.2 Air Emissions Compliance ........................................................................................................................... 32 3.5.3 Environmental Compliance Programs ........................................................................................................ 32 3.5.3.1 Clean Air Interstate Rule (CAIR) and Cross‐State Air Pollution Rule (CSAPR) ....................................................... 32 3.5.3.2 Mercury and Air Toxics Standard (MATS) Rule ..................................................................................................... 34 3.5.3.3 Coal Combustion Residuals (CCR) Rule ................................................................................................................. 35 3.5.3.4 Clean Water Act “316(b)” Rule ............................................................................................................................. 35 3.5.4 Future Environmental Rules ....................................................................................................................... 35 3.5.4.1 Effluent Limitation Guidelines and Standards (ELG) ............................................................................................. 35 3.5.4.2 National Ambient Air Quality Standards (NAAQS) ................................................................................................ 36 3.5.4.3 Carbon and GHG Regulations ............................................................................................................................... 36 3.5.4.4 Regional Haze Rule ............................................................................................................................................... 40 3.5.4.5 SWEPCO Environmental Compliance ................................................................................................................... 43 3.6 SWEPCO CURRENT DEMAND SIDE PROGRAMS ........................................................................................................... 43 3.6.1 Background ................................................................................................................................................ 43 3.6.2 Existing Demand Reduction/Energy Efficiency Mandates and Goals......................................................... 44 3.6.3 Current DR/EE Programs ............................................................................................................................ 47 3.6.4 Demand Reduction ..................................................................................................................................... 47 3.6.4.1 Demand Reduction ‐‐ Base Amounts .................................................................................................................... 48 3.6.3 Energy Efficiency ........................................................................................................................................ 49 3.6.3.1 Energy Conservation ............................................................................................................................................ 51 3.6.4 Smart Grid Technologies and Opportunities .............................................................................................. 51 3.6.4.1 Distributed Generation ......................................................................................................................................... 51 3.6.4.3 Volt VAR Optimization (VVO) ............................................................................................................................... 54 3.7 AEP‐SPP TRANSMISSION ........................................................................................................................................ 55 3.7.1 Transmission System Overview .................................................................................................................. 55 3.7.2 Current AEP‐SPP Transmission System Issues ............................................................................................ 55 3.7.2.1 The SPP Transmission Planning Process ............................................................................................................... 56 3.7.2.2 PSO‐SWEPCO Interchange Capability ................................................................................................................... 57 3.7.2.3 AEP‐SPP Import Capability .................................................................................................................................... 58 3.7.2.4 SPP Studies that may Provide Import Capability .................................................................................................. 59 3.7.3 Recent AEP‐SPP Bulk Transmission Improvements .................................................................................... 59 3.7.4 Impacts of New Generation ....................................................................................................................... 61 3.7.5 Summary of Transmission Overview .......................................................................................................... 62 4.0 MODELING PARAMETERS .............................................................................................................................. 65 4.1 MODELING AND PLANNING PROCESS – AN OVERVIEW .................................................................................................. 65 4.2 METHODOLOGY ..................................................................................................................................................... 66 4.3 FUNDAMENTAL MODELING INPUT PARAMETERS........................................................................................................... 66 4.3.1 Commodity Pricing Scenarios ..................................................................................................................... 68 4.3.2 Long‐Term CO2 Forecast “Proxies” ............................................................................................................. 72 4.3.3 DSM Program Screening & Evaluation Process .......................................................................................... 72 4.3.3.1 Overview .............................................................................................................................................................. 72 4.3.3.2 Technologies Considered But Not Evaluated ....................................................................................................... 73 4.3.3.3 Achievable Potential ............................................................................................................................................. 74 4.3.4 Determining Future Demand Side Programs for the IRP ............................................................................ 76 4.3.4.1 “Incremental” Energy Efficiency ........................................................................................................................... 76 4.3.4.2 VVO ...................................................................................................................................................................... 79 4.3.4.3 Demand Response ................................................................................................................................................ 80 4.3.4.4 Distributed Generation ......................................................................................................................................... 80 4.3.5 Evaluating Incremental Demand‐Side Resources ....................................................................................... 80 4.3.5.1 Incremental Energy Efficiency Modeled ............................................................................................................... 80 iv
4.3.5.2 VVO Modeled ....................................................................................................................................................... 83 4.3.5.3 Demand Response Modeled ................................................................................................................................ 83 4.3.5.4 Customer‐Owned (Distributed) Solar Modeled .................................................................................................... 84 4.3.5.5 Optimizing Incremental Demand‐side Resources ................................................................................................ 84 4.3.5.6 Discussion and Conclusion ................................................................................................................................... 84 4.4 IDENTIFY AND SCREEN SUPPLY‐SIDE RESOURCE OPTIONS ................................................................................................ 84 4.4.1 Capacity Resource Options ......................................................................................................................... 84 4.4.2 New Supply‐side Capacity Alternatives ...................................................................................................... 85 4.4.3 Baseload/Intermediate Alternatives .......................................................................................................... 86 4.4.3.1 Natural Gas Combined Cycle (NGCC) .................................................................................................................... 87 4.4.4 Peaking Alternatives .................................................................................................................................. 88 4.4.4.1 Simple Cycle Natural Gas Combustion Turbines (NGCT) ...................................................................................... 88 4.4.4.2 Aeroderivatives (AD) ............................................................................................................................................ 88 4.4.4.3 Reciprocating Engines (RE) ................................................................................................................................... 89 4.4.5 Renewable Alternatives ............................................................................................................................. 90 4.4.5.1 Utility‐Scale Solar ................................................................................................................................................. 90 4.4.5.2 Wind ..................................................................................................................................................................... 93 4.4.5.3 Hydro .................................................................................................................................................................... 96 4.4.5.4 Biomass ................................................................................................................................................................ 96 4.4.5 Cogeneration & Combined Heat & Power (CHP) ........................................................................................ 96 4.5 INTEGRATION OF SUPPLY‐SIDE AND DEMAND‐SIDE OPTIONS WITHIN PLEXOS® MODELING .................................................... 97 4.5.1 Optimize Expanded DSM Programs ........................................................................................................... 97 4.5.2 Optimize Other Demand‐Side Resources ................................................................................................... 98 4.5.3 Analysis and Review ................................................................................................................................... 98 5.0 RESOURCE PORTFOLIO MODELING........................................................................................................... 99 5.1 THE PLEXOS® MODEL ‐ AN OVERVIEW ........................................................................................................................ 99 5.1.1 Key Input Parameters ............................................................................................................................... 100 5.2 PLEXOS® OPTIMIZATION ......................................................................................................................................... 101 5.2.1 Modeling Options and Constraints .......................................................................................................... 101 5.2.2 Optimized Portfolios................................................................................................................................. 102 5.2.2.2 Load and Unit Retirement Sensitivity Case Modeling Results ............................................................................ 106 5.2.2.3 Base Portfolio EE, VVO and DG Results .............................................................................................................. 107 5.2.2.4 EE/VVO/DG Discussion ....................................................................................................................................... 111 5.2.3 Proposed CPP Rule – Impact on IRP ......................................................................................................... 112 5. 3 RISK ANALYSIS ..................................................................................................................................................... 113 5.3.1 Stochastic Modeling Process and Results ................................................................................................ 115 5.4 PREFERRED PORTFOLIO SELECTION .......................................................................................................................... 118 6.0 CONCLUSIONS AND RECOMMENDATIONS ..............................................................................................121 6.1 CAPACITY AND ENERGY PLAN .................................................................................................................................. 121 6.2 PLAN SUMMARY .................................................................................................................................................. 122 6.2.1 SWEPCO Five Year Action Plan ................................................................................................................. 129 6.3 CONCLUSION ....................................................................................................................................................... 129 APPENDIX .................................................................................................................................................................131 EXHIBIT A: STAKEHOLDER COMMENTS AND RESPONSES ..................................................................................................... 133 EXHIBIT B: PROJECTED DR AND EE ................................................................................................................................ 139 EXHIBIT C: LONG‐TERM COMMODITY PRICE FORECAST ..................................................................................................... 140 EXHIBIT D: CAPABILITY, DEMAND AND RESERVE (CDR) “GOING‐IN” ................................................................................. 141 v
EXHIBIT E: CAPABILITY, DEMAND AND RESERVE (CDR) “FINAL” ........................................................................................ 143 EXHIBIT F: IRP SCREENED SUPPLY‐SIDE RESOURCES.......................................................................................................... 145 EXHIBIT G: SECTION 2 TABLES ...................................................................................................................................... 147 EXHIBIT H PLAN SUMMARY CHARTS .............................................................................................................................. 169 EXHIBIT I EE ANNUAL ENERGY & CAPACITY PROGRAM SAVINGS TABLES ............................................................................... 175 vi
DRAFT 2015 Integrated Resource Plan
Executive Summary
The Integrated Resource Plan (IRP or Plan) is based upon the best available information at the
time of preparation. However, changes that may impact this plan can, and do, occur without
notice. Therefore this plan is not a commitment to a specific course of action, since the future is
highly uncertain, particularly in light of the current economic conditions, access to capital, the
movement towards increasing use of renewable generation and end-use efficiency, as well as
current and future environmental regulations, including proposals to control greenhouse gases.
The implementation action items as described herein are subject to change as new information
becomes available or as circumstances warrant.
An IRP explains how a utility company plans to meet the projected capacity (i.e.,
peak demand) and energy requirements of its customers. By Louisiana rule, Southwestern
Electric Power Company (SWEPCO or Company) is required to provide an IRP that
encompasses a 20-year forecast period (2016-2035). SWEPCO’s 2015 IRP has been
developed using the Company’s current assumptions for:




Customer load requirements – peak demand and energy;
Commodity prices – coal, natural gas, on-peak and off-peak power prices,
capacity and emission prices;
Supply-side alternative costs – including fossil fuel and renewable generation
resources; and
Demand-side program costs and analysis.
In addition, SWEPCO must consider the impact of proposed environmental rules,
specifically associated with greenhouse gas (GHG) emissions that, in their current form,
would add significant costs and operational challenges. These rules are still being
developed, and individual State Implementation Plans to implement these rules may not
be finalized and approved for a number of years. Even so, SWEPCO has considered a
portfolio of resources that will provide a path to reduce the intensity of its carbon
emissions.
To meet its customers’ future energy requirements, SWEPCO has carefully
considered the continued operation and the ongoing level of investment in its existing
fleet of fossil-fueled assets including its very efficient base-load coal plants, its newer
combined cycle and combustion turbine plants, and its older gas-steam plants. Another
consideration in this 2015 IRP is the increased adoption of distributed rooftop solar
ES-1
DRAFT 2015 Integrated Resource Plan
resources by SWEPCO’s customers. While SWEPCO does not have control over how
and to the extent this resource is deployed, it recognizes that distributed solar will be a
contributor to meeting SWEPCO’s capacity and energy requirements. Keeping these
considerations in mind, SWEPCO has developed a plan to provide adequate supply and
demand resources to meet its peak load obligations for the next twenty years. The key
components of this plan are for SWEPCO to:

Invest in environmental control equipment to make the Welsh Units 1 & 3,
Flint Creek, Pirkey and Dolet Hills solid-fuel units compliant under known or
anticipated environmental regulation;

Begin the process of retiring approximately 1,750 MW of older gas-steam
units;

Retire the solid-fuel 528 MW Welsh Unit 2;

Acquire an optimal mix of supply-side resources in the form of additional
wind resources, utility-scale solar, and natural gas-fired generation resources;

Implement demand-side resources in the form of additional energy efficiency
programs;

Recognize that residential and commercial customers will add distributed
resources, primarily in the form of residential and commercial rooftop solar.
Environmental Compliance Issues
This 2015 IRP considers the impacts of final and proposed EPA regulations to
SWEPCO generating facilities. In addition, the IRP development process conservatively
assumes there may be future regulation of GHG/carbon dioxide (CO2) emissions which
would, if established, become effective at some point in the 2020-2025 timeframe.
Environmental compliance requirements have a major influence on the consideration of
new supply-side resources for inclusion in the IRP because of the potential significant
effects on both capital and operational costs. While a proposed GHG/CO2 rule applicable
to existing fossil-fired units has been published by the U.S. Environmental Protection
Agency (EPA), there is significant uncertainty in “if, how and when” the final rule will
be implemented by the states. As a reasonable proxy for the final rule, it is assumed that
the resulting economic impact would be equivalent to a CO2 “tax” applicable to each ton
of carbon emitted from fossil-fired generation sources which would take effect beginning
in 2022. The cost of such CO2 emissions is expected to stay within the $15-$20/metric
ES-2
DRAFT 2015 Integrated Resource Plan
ton range over the long-term analysis period.
Louisiana IRP Stakeholder Process
This draft report is the first SWEPCO IRP to be developed using the State of
Louisiana’s process and is the result of analysis performed by SWEPCO that includes
consideration of stakeholder input. Various stakeholders, including Commission staff
were presented information in early 2014 and provided useful feedback which has been
considered and incorporated in the analysis assumptions, where warranted. For example,
comments regarding renewable energy costs were used in developing pricing for future
tranches of wind resources. Also, SWEPCO addresses stakeholder comments pertaining
to energy efficiency by providing transparency to its assumptions and modeling energy
efficiency programs on the same basis as supply resources. Stakeholder comments and
SWEPCO’s responses are documented in the Appendix, Exhibit A. SWEPCO looks
forward to receiving additional feedback on this draft report for use in preparing the final
2015 IRP. Key dates related to the IRP Process are shown below:
 SWEPCO submits request to initiate IRP Process
Jan. 2014
 SWEPCO holds first Stakeholder meeting
March 2014
 Draft IRP is published
Feb. 2015
 SWEPCO holds second Stakeholder meeting
March 2015
 Stakeholders file comments
May 2015
 Staff files comments
June 2015
 SWEPCO files Final IRP
Sept. 2015
 Staff submits recommendations to the Commission
Dec. 2015
 Commission Order acknowledging the IRP
Feb. 2016
Summary of SWEPCO Resource Plan
SWEPCO’s total internal energy requirements are forecasted to decrease at a
compound average growth rate (CAGR) of 0.2% over the IRP planning period (through
2035). This decrease is primarily caused by certain wholesale--municipal and
cooperative--customers that are expected to reduce or eliminate energy purchases from
SWEPCO due to expiring supply contracts. When these specific wholesale customer
loads are omitted from the load forecast, SWEPCO’s total internal energy requirements
ES-3
DRAFT 2015 Integrated Resource Plan
increase at a CAGR of 0.7% over the same period. SWEPCO’s corresponding summer
and winter peak internal demands are forecasted to decrease at CAGRs of 0.2% and
0.1%, respectively, with annual peak demand expected to continue to occur in the
summer season through 2035. Likewise, when the peak demand for the wholesale
customers that have expiring contracts are omitted, the summer and winter peak loads
increase at CAGRs of 0.7% and 1.1%, respectively. Over the next ten years, the net
impact of load growth, plant retirements and plant deratings leaves SWEPCO with a
“going-in” (i.e. before resource additions) capacity deficit as shown in Figure ES-1. As
can be seen from Figure ES-1, in 2021 SWEPCO is anticipated to experience a capacity
shortfall, which is evident from the gap between stacked bar of available resources and
the black line representing SWEPCO’s load demand plus reserve requirements.
Figure ES-1
SWEPCO “Going-In” SPP Capacity Position
Projected SWEPCO Resources
7,000
6,000
5,000
177
Cleco PPA Expiration (2014) 495
586
528
528
78
122
444
(Net) NTEC + (Net) TexLa/ETEC Position Expirations (2017)
589
600
108
122
126
108
122
126
108
122
126
121
126
121
383
383
383
383
383
LB 1 Retire; KL4 Derate 4,000
1,327
Rayburn Country Load Expiration (2020)
1,327
126
120
120
217
273
LB 2, LS, KL4 Retire 120
217
KL2&3 Retire 1,327
1,327
1,327
1,327
1,327
1,327
1,327
120
108
LB3 Retire 120
108
1,327
1,327
120
LB4 Retire 1,327
MW
Gas‐Steam 1,771 MW
3,000
812
812
812
812
812
812
812
812
812
812
812
812
2634
2635
2591
2591
2591
2591
2590
2590
2590
2590
2590
2590
2020
2021
2022
2023
2024
Gas‐Steam All Other
2025
2,000
1,000
0
2014
2015
2016
2017
Coal & Lignite Units, excl. Welsh 2
2018
2019
Stall & Mattison
Gas‐Steam, LB1&2, LS,KL2‐4, LB3&4
Other Self‐Supply + Purch, incl. Wind
ETEC/TexLa Self‐Supply
Exelon Purchase
Welsh 2
NTEC Self‐Supply
CLECO PPA
Demand (June '14 Fcst) + Reserves
To determine the appropriate level and mix of incremental supply and demand-side
resources required to offset such going-in capacity deficiencies, SWEPCO utilized the
Plexos® Linear Program (LP) optimization model to develop a “least-cost” resource plan.
ES-4
DRAFT 2015 Integrated Resource Plan
Although the IRP planning period is limited to 20 years (through 2035), the Plexos®
modeling was performed through the year 2040 so as to properly consider various costbased “end-effects” for the resource alternatives being considered.
SWEPCO used the results of the modeling to develop a “Preferred Portfolio”.
SWEPCO’s Preferred Portfolio

Maintains, upon recognition of required and potentially-emerging
environmental control investments, SWEPCO’s solid-fuel units at Welsh
Units 1 & 3, Flint Creek and Pirkey, in addition to its share of energy and
capacity from the non-SWEPCO operated Dolet Hills unit.

Retires Welsh Unit 2 in 2016.

Retires 722 MW of older gas-steam units beginning in 2016 through 2030.

Adds 200 MW per year of wind energy beginning in 2021, reduced to 100
MW per year for 2027 through 2031; for a total of 1,700 MW (nameplate) of
wind over the 20-year planning period.

Implements customer and grid energy efficiency, including Volt VAR
Optimization (VVO) programs so as to reduce energy requirements by 1,121
GWh (or 5% of projected energy needs) and capacity requirements by 410
MW by 2035.

Adds 50 MW per year of utility-scale solar energy beginning in 2020; for a
total of 800 MW (nameplate) of utility-scale solar over the 20-year planning
period.

Recognizes additional distributed solar capacity will be added by SWEPCO’s
customers, starting in 2015, of about 1 MW (nameplate) and ramping up to a
total of about 50 MW (nameplate) by 2035.

Adds 207 MW of natural gas peaking resources over the 20-year planning
period; 50 MW of reciprocating engine technology in 2023 and 157 MW of
small frame technology in 2025.

Continues operation of SWEPCO’s newest plant additions – the
environmentally-compliant Turk solid-fuel unit, as well as the Stall natural
gas combined cycle (CC) and Mattison natural gas simple cycle (SC)
facilities.
ES-5
DRAFT 2015 Integrated Resource Plan
To arrive at the Preferred Portfolio composition, SWEPCO developed Plexos®derived, “optimum” portfolios under five long-term commodity price forecasts. The
Preferred Portfolio is intended to provide the lowest reasonable cost of incrementallyrequired (peak) demand and energy to SWEPCO’s customers which would meet
environmental and resource adequacy constraints, while also reflecting emerging
preference for, and the viability of customer self-generation.
Specific SWEPCO capacity and energy production changes over the 20-year planning
period associated with the Preferred Portfolio are shown in Figures ES-2a and ES-2b,
respectively, and their relative impacts to SWEPCO’s annual capacity and energy position
are shown in Figures ES-3a and ES-3b respectively.
Figures ES-2a and ES-2b indicate that this Preferred Portfolio would reduce
SWEPCO’s reliance on solid fuel-based and gas-steam generation as part of its portfolio
of resources, and increase reliance on demand-side and renewable resources, thereby
enhancing resource supply technology and resource supply fuel diversity. Specifically,
over the 20-year planning period the Company’s capacity mix attributable to solid fuelfired assets would decline from 46% to 31%, and gas-steam assets decline from 25% to
13%. Newer combined cycle and peaking gas assets edge up from 12% at 13%,
renewables (wind, utility and distributed solar, based on nameplate ratings) increase from
6.9% to 37%, and, similarly, demand-side and energy-efficiency measures increase from
1% to 6% over the planning period. SWEPCO’s energy output attributable to solid fuelfired generation shows a significant decrease from 81% to 49% over the period, while
energy attributable to renewable sources and energy efficiency grows from 7.7% to
38.6%. The Preferred Portfolio highlights the fact that the Company has the ability to meet
its future requirements without adding capital intensive baseload generation, relying
instead on utility and customer-owned renewable resources, demand-side activity and a
modest amount of new peaking capacity. Moreover, the layers of carbon-free energy
resources being added as part of this planning process would serve to hedge SWEPCO’s
exposure to Southwest Power Pool (SPP) energy market volatility, while producing a
lower-cost solution than one that relies more heavily on market purchases or new gas
assets.
ES-6
DRAFT 2015 Integrated Resource Plan
Figure ES-2a
SWEPCO SPP Capacity Changes
ES-7
DRAFT 2015 Integrated Resource Plan
Figure ES-2b
SWEPCO Energy Production Changes
Figures ES-3a and ES-3b show the changes in capacity and energy mix on an annual
basis, relative to capacity and energy requirements. The capacity contribution from
renewable resources is fairly modest; however, those resources provide a significant
ES-8
DRAFT 2015 Integrated Resource Plan
volume of energy, specifically attributed to wind resources. SWEPCO’s model selected
those wind resources because they add more value (lowered SWEPCO’s cost) than
alternative resources.
Figure ES-3a SWEPCO SPP Capacity Position
7,000
SWEPCO Preferred Plan Capacity Position
*DSM shown as a resource, not a load reduction
*Renewables capacity at SPP rating, not nameplate
6,000
5,000
DSM/EE/Inter
4,000
Net Purchases
MW
Renewables/DG
Gas Steam
Gas CT/CC
3,000
Solid Fuel
Load Obligation (Excl DSM)
2,000
1,000
0
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Figure ES-3b SWEPCO Energy Position
40,000
SWEPCO Preferred Plan Energy Position (GWh)
*DSM shown as a resource, not a load reduction
35,000
30,000
25,000
EE/VVO
GWh
Solar (utility +DG)
Wind
20,000
Gas CC/CT
Gas Steam
Solid Fuel
Load w/o EE
15,000
10,000
5,000
0
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
The following Table ES-1 provides a summary of the Preferred Portfolio, which
was the result of resource optimization modeling under the “Base” case commodity
pricing scenario:
ES-9
ES-10
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
(F)
(1,026)
(1,026)
(1,026)
(1,026)
(1,194)
(1,194)
(1,194)
(1,194)
(1,194)
(1,194)
(L)
(K)
(J)
(I)
(H)
(G)
157
157
157
157
157
157
157
157
157
157
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
157
MW
MW
‐
(61)
(589)
(589)
(589)
(589)
(699)
(699)
(699)
(808)
(808)
(916)
(Frame) CTs
Coal & Gas‐
Steam
RETIREMENTS
(Cumulative)
(2)
50
50
50
50
101
101
101
101
101
101
‐
‐
‐
‐
‐
‐
‐
‐
‐
50
50
50
MW
Recip Engines
New‐Build
(3)
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
MW
NGCC
(4)
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
126
126
126
126
126
‐
‐
‐
‐
‐
MW
(ST) PPA
(5)
(7)
(8)
(9)
6
12
17
22
25
28
31
33
34
35
35
35
MW
‐
‐
‐
8
18
28
36
56
77
97
103
109
MW
New
‐
‐
‐
26
38
38
38
38
51
51
51
51
VVO
331 35 128 63
342 35 145 73
352 35 168 83
363 35 196 92
376 35 221 100
387 35 240 100
397 35 256 100
409 35 274 100
419 35 292 100
429 35 314 100
567
337
'TOTAL' Energy Efficiency (2016‐3035)
183
198
208
220
231
242
253
267
281
295
307
319
MW
'Embedded' Federal EE Existing DSM Regulations (B) Programs(C) Energy Efficiency (EE)
(C) 81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
MW
Programs
Existing DSM DR
(10)
120
130
140
150
160
170
170
170
170
170
‐
‐
‐
‐
‐
‐
‐
20
40
60
80
100
MW
Wind (D)
(11)
(12)
Solar(E)
(13)
148
170
191
212
233
255
276
297
318
339
‐
‐
‐
‐
‐
‐
21
42
64
85
106
127
MW
2.4
2.6
2.9
3.1
3.4
3.7
4.0
4.3
4.7
5.0
0.1
0.4
0.5
0.7
0.8
1.0
1.1
1.3
1.5
1.7
1.9
2.1
MW
Utility‐Scale Distributed
Preferred Portfolio
(Cumulative) Firm Capacity Resource ADDITIONS
(6)
(241)
(183)
(118)
(49)
(103)
(52)
(15)
24
64
107
87
32
(365)
(326)
(300)
(287)
(364)
(427)
(351)
(347)
(300)
(203)
MW
CHANGE
(P) (Cumul.)
NET
'RESOURCE' (14)=(1)to(13), ex(6)
(16)
90
118
165
188
110
134
159
158
168
194
784
602
121
105
299
224
262
91
127
83
108
157
MW
Above
SPP Minimum Rqrmnt(Q) 15.7%
16.3%
17.4%
17.9%
16.1%
16.7%
17.2%
17.2%
17.4%
18.0%
29.9%
25.6%
16.0%
15.7%
20.5%
18.7%
19.7%
15.7%
16.6%
15.5%
16.1%
17.2%
Margin %
SWEPCO Reserves
Resulting
(15)
(Q)
Excludes cumulative annual changes in SWEPCO SPP 'Load Responsibility' (coincident peak demand) and 3rd‐party resources… which also impacts relative capacity resource position.
SPP minimum criterion @ 13.6% as a function of peak demand.
(P)
(C)
(B)
SPP Planning Year is effective 6/1/XXXX.
Represents estimated energy efficiency levels already 'embedded' into SWEPCO's long‐term load & peak demand forecast based on emergence of PRIOR‐ESTABLISHED Federal efficiency standareds (EPAct 2005; 2007 EISA, 2009 ARRA). Represents estimated contribution from current/known SWEPCO DSM‐EE and Demand Response (Interruptible, DLC/ELM) program activity also reflected in the Company's long‐term load and demand forecast (from 'Going‐In' SWEPCO CDR) .
(D)
Due to the intermittency of wind resources, only 10% of wind resource 'nameplate' MW rating are included for capacity resource determination purposes.
(E)
Due to the intermittency of solar resources, only 42.4% of solar resource 'nameplate' MW rating are included for capacity resource determination purposes.
RETIREMENTS:
(F)
(L)
Lieberman 1 retirement & Knox Lee 4 50% derate assumed 12/2014.
Wilkes 1 retirement assumed 12/2029.
(G)
(M)
Welsh Unit 2 retirement effective approximately June 1, 2016.
Wilkes 2 retirement assumed 12/2035.
(H)
Lieberman 2, Lone Star & Knox Lee 4 retirement assumed 12/2019.
(I)
Lieberman 3 retirement assumed 12/2022.
(J)
Lieberman 4 retirement assumed 12/2024.
(K)
Arsenal Hill 5 retirement assumed 12/2025.
(A)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
(LA) IRP Period
Yr.
SPP Planning Year(A)
MW
(1)
(DRAFT) 2015 Integrated Resource Plan
Cumulative Resource Changes
Southwestern Electric Power Company
(18)
(19)
1,200
1,300
1,400
1,500
1,600
1,700
1,700
1,700
1,700
1,700
‐
‐
‐
‐
‐
‐
‐
200
400
600
800
1,000
MW
MW
1.0
3.5
5.0
6.5
8.0
9.5
11.0
13.0
15.0
17.0
19.0
21.0
350 23.5
400 26.0
450 28.5
500 31.0
550 34.0
600 37.0
650 40.0
700 43.0
750 46.5
800 50.0
847
'TOTAL' Solar (2016‐2035)
‐
‐
‐
‐
‐
‐
51
100
150
200
250
300
MW
Utility‐Scale Distributed
(Cumulative) 'NAMEPLATE' ADDITIONS
(D)
Wind
Solar(E)
(17)
DRAFT 2015 Integrated Resource Plan
Table ES-1
DRAFT 2015 Integrated Resource Plan
Clean Power Plan Implications
The EPA published proposed rules in June 2014 to address how states may reduce
GHG/CO2 emissions. The proposed rule, known as the Clean Power Plan (CPP), sets
state-specific CO2 emission interim rate targets beginning in 2020, with final target
achievement by 2030. Targets were set based on four building blocks that included plant
efficiency improvements, the re-dispatch of natural gas combined cycle plants, additional
renewable (and at-risk nuclear) resources, and incremental energy efficiency. The
comment period for the CPP ran through December 1, 2014, and EPA now expects to
issue a final rule in mid-summer, 2015. EPA has received over two million comments on
the rule, including critical comments from various public agencies in the states served by
SWEPCO, reliability organizations including SPP, the Electric Reliability Council of
Texas (ERCOT), and the Midcontinent Independent System Operator (MISO), and
research organizations such as the Electric Power Research Institute (EPRI), in addition to
SWEPCO’s parent American Electric Power (AEP). These criticisms question the scope,
timing and legality of the CPP based on EPA’s authority under the Clean Air Act, the
reliability impacts associated with the timeline to implement the rule, and technical errors
made by EPA in calculating the state-specific achievable CO2 rate reductions from each of
the four building blocks.
SWEPCO cannot predict how these comments and others will help EPA shape the
final rule, nor can it predict what requirements will be placed on SWEPCO in the State
Implementation Plans (SIPs) that may ultimately be developed and approved by the EPA.
Until SIPs are proposed in the states SWEPCO serves, there are few, if any, additional
actions that SWEPCO can undertake to reduce CO2 intensity beyond those that have been
proposed in the Preferred Portfolio. Because it is unlikely that SWEPCO would receive
regulatory approval of any such actions prior to the states defining and receiving approval
for their compliance strategies, it is not practical for SWEPCO to identify a CPP
compliance strategy at this time.
Conclusion
This IRP provides for reliable electric utility service, at reasonable cost, through a
combination of supply-side resources, renewable supply- and demand-side programs and
ES-11
DRAFT 2015 Integrated Resource Plan
serves as a roadmap for SWEPCO to provide adequate capacity resources to serve its
customers' peak demand and required SPP reserve margin needs throughout the forecast
period.
Moreover, this IRP also recognizes SWEPCO’s energy position prospectively. The
highlighted Preferred Portfolio offers incremental resources that will provide—in
addition to the needed SPP installed capacity to achieve mandatory SPP (summer) peak
demand requirements—additional energy so as to protect the Company’s customers from
being overly exposed to SPP energy markets that could be influenced by many external
factors, including the impact of carbon, going-forward.
The IRP process is a continuous activity; assumptions and plans are continually
reviewed as new information becomes available and modified as appropriate. Indeed, the
capacity and energy resource plan reported herein reflects, to a large extent, assumptions
that are subject to change; it is simply a snapshot of the future at this time. This IRP is not
a commitment to a specific course of action, as the future is highly uncertain. The
resource planning process is becoming increasingly complex when considering pending
regulatory restrictions, technology advancement, changing energy supply pricing
fundamentals, uncertainty of demand and EE advancements. These complexities
necessitate the need for flexibility and adaptability in any ongoing planning activity and
resource planning processes. Lastly, the ability to invest in extremely capital-intensive
generation infrastructure is increasingly challenged in light of current economic
conditions and the impact of all these factors on SWEPCO’s customers will be a primary
consideration in this report.
ES-12
DRAFT 2015 Integrated Resource Plan
1.0 Introduction
1.1 Overview
This report presents the Integrated Resource Plan for SWEPCO including descriptions of
assumptions, study parameters, and methodologies. The results incorporate the integration of
supply-side resources and demand-side management (DSM) activity.
The goal of the IRP process is to identify the amount, timing and type of resources required to
ensure a reliable supply of power and energy to customers at the least reasonable cost.
In addition to developing a long-term strategy for achieving reliability/reserve margin
requirements as set forth by the SPP, capacity resource planning is critical to SWEPCO due to its
impact on:

Determining Capital Expenditure Requirements—which represents one of the
basic elements of the Company’s long-term business plan.

Rate Case Planning—operating in three state retail jurisdictions as well as having
wholesale contracts which fall under the auspices of the Federal Energy Regulatory
Commission (FERC), this planning process is a critical component of recovery
filings that will reflect input based on a prudent planning process.

Integration with other Strategic Business Initiatives—generation/capacity
resource planning is naturally integrated with the Company’s current and anticipated
environmental compliance, transmission planning, and other corporate planning
initiatives.
1.2 IRP Process
This IRP briefly covers the processes and assumptions required to develop the
recommended Plan for SWEPCO. The IRP process consists of the following components/steps:

Description of the Company, the resource planning process in general, and the
implications of current issues as they relate to resource planning.

Provide projected growth in demand and energy which serves as the underpinning
of the plan.

Identify and evaluate demand-side options such as energy efficiency measures,
demand response and distributed generation.

Identify current supply resources, including projected changes to those resources
(e.g., de-rates or retirements), and transmission system integration issues.
1
DRAFT 2015 Integrated Resource Plan

Identify and evaluate supply-side resource options.

Describe the analysis and assumptions that will be used to develop the plan such as
Regional Transmission Organization (RTO) reserve margin criteria, and
fundamental modeling parameters.

Solicit input from stakeholders regarding assumptions and analyses to be
performed.

Perform resource modeling and use the results to develop portfolios, including the
selection of the preferred plan.

Present the draft findings and recommendations to stakeholders, receive and
consider their input, then develop the final preferred plan, and near term action
plan.
1.3 Introduction to SWEPCO
SWEPCO is an affiliate company of AEP. With more than five million customers and
serving parts of 11 states, AEP is one of the country’s largest investor-owned utilities. AEP’s
service territory covers 197,500 square miles in Louisiana, Arkansas, Texas, Oklahoma, Indiana,
Michigan, Kentucky, Ohio, Tennessee, Virginia and West Virginia.
AEP owns and/or operates one of the largest generation portfolios in the United States, with
approximately 37,600 megawatts of generating capacity in three Regional Transmission
Organizations. AEP’s customers are served by one of the world’s largest transmission and
distribution systems. System-wide there are approximately 40,000 circuit miles of transmission
lines and more than 222,000 miles of distribution lines.
The operating companies in AEP's SPP zone collectively serve a population of about 4.18
million, which includes over 1 million retail customers in a 36,000 square mile area in parts of
Arkansas, Louisiana, Oklahoma, and Texas.
SWEPCO’s customers consist of both retail and sales-for-resale (wholesale) customers
located in the states of Arkansas, Louisiana and Texas (see Figure 1-1). Currently, SWEPCO
serves approximately 525,000 retail customers in those states; including over 228,000 in the state
of Louisiana. The peak load requirement of SWEPCO’s total retail and wholesale customers is
seasonal in nature, with distinctive peaks occurring in the summer and winter seasons.
SWEPCO’s historical all-time highest recorded peak demand was 5,554 MW, which occurred in
2
DRAFT 2015 Integrated Resource Plan
August 2011; and the highest recorded winter peak was 4,919 MW, which occurred in January
2014.
The most recent (2014) actual SWEPCO summer and winter peak demands were
significant at 4,830 MW and 4,919 MW, occurring on August 25th and January 7th, respectively.
Figure 1-1: SWEPCO Service Territory
This IRP is based upon the best available information at the time of preparation. However,
changes that may impact this plan can, and do, occur without notice. Therefore this plan is not a
commitment to a specific course of action, since the future, now more than ever before, is highly
uncertain, particularly in light of the current economic conditions, access to capital, the
movement towards increasing use of renewable generation and end-use efficiency, as well as
legislative proposals to control greenhouse gases.
The implementation action items as described herein are subject to change as new
information becomes available or as circumstances warrant.
3
DRAFT 2015 Integrated Resource Plan
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4
DRAFT 2015 Integrated Resource Plan
2.0 Load Forecast and Forecasting Methodology
2.1 Summary of SWEPCO Load Forecast
The SWEPCO load forecast was finalized in June 2014.1 SWEPCO’s energy consumption
forecasts for the major customer classes were developed from both short-term and long-term
econometric models by the AEP Economic Forecasting organization.
These energy forecasts were determined in part by forecasts of the regional economy,
which, in turn, are based on the January 2014 national economic forecast of Moody’s Analytics.
Customer service engineers also provide valuable feedback on large customer load changes. The
forecasts of seasonal peak demands were developed using an analysis of energy, load shapes and
load factor that estimates hourly demand.
Some of the key assumptions on which the load forecast is based include:





Moderate economic growth characterized by Gross Domestic Product (GDP)
growth averaging 1.9% per year over the forecast horizon;
Federal and state energy efficiency legislation will have an impact on energy
consumption;
Electricity prices are based on company analytics and an Energy Information
Administration (EIA) long-term outlook;
Generally moderate (0.7% per year) population growth in the Company’s
service-area; and
Normal weather.
2.1.1 Forecast Assumptions
The load forecasts for SWEPCO and the other operating companies in the AEP System
incorporate a forecast of U.S. and regional economic growth provided by Moody’s Analytics.
The load forecasts utilized Moody’s Analytics economic forecast issued in January 2014.
Moody’s Analytics projects moderate growth in the U.S. economy during the 2015-2035 forecast
period, characterized by a 1.9% annual rise in real GDP, and moderate inflation as well, with the
1
The load forecasts (as well as the historical loads) presented in this report reflect the traditional concept of internal load, i.e., the
load that is directly connected to the utility’s transmission and distribution system and that is provided with bundled generation
and transmission service by the utility. Such load serves as the starting point for the load forecasts used for generation planning.
Internal load is a subset of connected load, which also includes directly connected load for which the utility serves only as a
transmission provider. Connected load serves as the starting point for the load forecasts used for transmission planning.
5
DRAFT 2015 Integrated Resource Plan
implicit GDP price deflator expected to rise by 1.4% per year. Industrial output, as measured by
the Federal Reserve Board's (FRBs) index of industrial production, is expected to grow at 1.8%
per year during the same period. Moody’s projected employment growth of 0.9% per year during
the forecast period and real regional income per-capita annual growth of 1.9% for the SWEPCO
service area.
The Company utilizes an internally developed service area electricity price forecast. This
forecast incorporates information from the Company’s financial plan for the near term and EIA
outlook for the West South Central Census Region for the longer term. These price forecasts are
incorporated into the Company’s energy sales models, where appropriate.
SWEPCO’s customer service engineers are in frequent touch with industrial and
commercial customers about their needs and activities. From these discussions, expected load
additions or deletions are relayed to the Company. The load forecast includes information on
these large load changes in the final energy sales outlook.
Where appropriate, the Company includes weather as an explanatory variable in its energy
sales models. These models reflect historical weather for the model estimation period and
normal weather for the forecast period.
Inherent in the load forecasts are the impacts of past customer energy conservation and load
management activities, including known or anticipated DSM reductions based on current
company-sponsored DSM activity that is effectively “embedded” in the load forecast. New or
“incremental” DSM resources over-and-above those levels are analyzed and projected separately
as part of the IRP development process.
2.1.2 Forecast Highlights
SWEPCO’s total internal energy requirements are forecasted to decrease at an average
annual rate of 0.2% for the 20-year period from 2015 to 2035. The load decrease is associated
with certain wholesale customer contracts expiring in 2018 and 2020. Otherwise, the load growth
is primarily occurring in the industrial and (other) wholesale customer sectors, while growth in
residential and commercial sectors is dampened by shifting demographics, weaker economic
6
DRAFT 2015 Integrated Resource Plan
outlook, and appliance/equipment efficiency increases through 2020.
Total internal energy
requirements excluding those wholesale customers with expiring contracts are projected to
increase by 0.7 percent per year over the forecast period. The approved DSM programs reflect
those existing DSM program activities that the Company expects to continue to engage at a level
similar to currently approved programs. Any incremental DSM activity associated with the
Company’s long-term modeling will be discussed in Section 4.
Between 2015 and 2035, the corresponding summer and winter peak internal demands are
forecasted to decline at an average annual rate of 0.2% and 0.1%, respectively. Excluding the
wholesale customers with expiring contracts, SWEPCO’s summer and winter peak internal
demands are projected to grow at an annual rate of 0.7% and 1.1%, respectively. SWEPCO's
annual peak demand is expected to continue to occur in the summer season.
2.2 Overview of Forecast Methodology
SWEPCO's load forecasts are based mostly on econometric, state-of-the-art statistically
adjusted end-use and analyses of time-series data. This is helpful when analyzing future
scenarios and developing confidence bands in addition to objective model verification by using
standard statistical criteria.
SWEPCO's energy requirements forecast is derived from two sets of econometric models:
1) a set of monthly short-term models and 2) a set of monthly long-term models. The forecast
methodology leverages the relative analytical strengths of both the short- and long-term methods
to produce a reasonable and reliable forecast that is used for various planning purposes.
For the first full year of the forecast, the forecast values are generally governed by the shortterm models. The short term models are regression models with time series errors which analyze
the latest sales and weather data to better capture the monthly variation in energy sales for shortterm applications like capital budgeting and resource allocation. While these models produce
extremely accurate forecasts in the short run, without logical ties to economic factors, they are
less capable of capturing structural trends in electricity consumption that are more important for
longer term resource planning applications.
7
DRAFT 2015 Integrated Resource Plan
The long term models are econometric, and statistically adjusted end-use models which are
specifically equipped to account for structural changes in the economy as well as changes in
customer consumption due to increased energy efficiency.
The long term forecast models
incorporate Moody’s Analytics regional economic forecast data for income, employment,
households, output, and population.
The short-term and long-term forecasts are then blended to ensure a smooth transition from
the short-term to the long-term forecast horizon for each major revenue class. The class level
sales are then summed and adjusted for losses to produce monthly net internal energy sales for
the system.
The demand forecast model utilizes a series of algorithms to allocate the monthly net
internal energy to hourly demand. The inputs into forecasting hourly demand are internal energy,
weather, 24-hour load profiles and calendar information.
A flow chart depicting the structure of the models used in projecting SWEPCO’s electric
load requirements is in Figure 2-1, which depicts the stages in the development of the
Company's short-term and long-term internal energy requirements forecasts. Figure 2-1 also
presents a schematic of the sequential steps for the peak demand and internal energy
requirements forecasting.
8
DRAFT 2015 Integrated Resource Plan
Figure 2-1
Southwestern Electric Power Company
Internal Energy Requirements and Peak Demand
Forecasting Method
Monthly
Energy Sales
& Weather
History
Moody’s
Analytics
National &
Regional
Economic
Forecasts
DOE/EIA
Annual
Energy
Outlook
Electricity
Price
Forecast
Monthly
Model
Input
History
State Gas
Price
Forecast
Residential &
Commercial
SAE Models
Long-Term
Energy
Models
Short-Term
Energy
Models
Blending
Process
Hourly Demand Models
(Load Shapes)
Losses, Billed/Unbilled
Adjustments
Peak Demand and
Internal Energy
Requirements Forecast
2.3 Forecast Methodology for Internal Energy Requirements
2.3.1 General
This section provides a more detailed description of the short-term and long-term models
employed in producing the forecasts of SWEPCO’s energy consumption, by customer class. For
the purposes of the load forecast, the short term is defined as the first 12 to 24 months, and the
long term as the forecast years beyond the short term.
Conceptually, the difference between short and long term energy consumption relates to
changes in the stock of electricity-using equipment and economic influences, rather than the
9
DRAFT 2015 Integrated Resource Plan
passage of time. In the short term, electric energy consumption is considered to be a function of
an essentially fixed stock of equipment. For residential and commercial customers, the most
significant factor influencing the short term is weather. For industrial customers, economic
forces that determine inventory levels and factory orders also influence short-term utilization
rates. The short-term models recognize these relationships and use weather and recent load
growth trends as the primary variables in forecasting monthly energy sales.
Over time, demographic and economic factors such as population, employment, income,
and technology influence the nature of the stock of electricity-using equipment, both in size and
composition. Long-term forecasting models recognize the importance of these variables and
include all or most of them in the formulation of long-term energy forecasts.
Relative energy prices also have an impact on electricity consumption. One important
difference between the short-term and long-term forecasting models is their treatment of energy
prices, which are only included in long-term forecasts. This approach makes sense because
although consumers may suffer sticker shock from energy price fluctuations, there is little they
can do to impact them in the short-term. They already own a refrigerator, furnace or industrial
equipment that may not be the most energy-efficient model available. In the long term, however,
these constraints are lessened as durable equipment is replaced and as price expectations come to
fully reflect price changes.
2.3.2 Short-term Forecasting Models
The goal of SWEPCO's short-term forecasting models is to produce an accurate load
forecast for the first full year into the future. To that end, the short-term forecasting models
generally employ a combination of monthly and seasonal binaries, time trends, and monthly
heating cooling degree-days in their formulation. The heating and cooling degree-days are
measured at weather stations in the Company's service area. The forecasts relied on
autoregressive integrated moving average (ARIMA) models.
There are separate models for the Arkansas, Louisiana and Texas Jurisdictions of the
Company. The estimation period for the short-term models was January 2004 through January
2014.
10
DRAFT 2015 Integrated Resource Plan
2.3.2.1 Residential and Commercial Energy Sales
Residential and commercial energy sales are developed using ARIMA models to forecast
usage per customer and number of customers. The usage models relate usage to lagged usage,
lagged error terms, heating and cooling degree-days and binary variables. The customer models
relate customers to lagged customers, lagged error terms and binary variables. The energy sales
forecasts are a product of the usage and customer forecasts.
2.3.2.2 Industrial Energy Sales
Short-term industrial energy sales are forecast separately for 19 large industrial customers in
SWEPCO and for the remainder of industrial energy. These short-term industrial energy sales
models relate energy sales to lagged energy sales, lagged error terms and binary variables for
each of the Company’s jurisdictions. The industrial models are estimated using ARIMA models.
The short-term industrial energy sales forecast is a sum of the forecasts for the 19 large industrial
customers and the forecasts for the remainder of the manufacturing customers. Customer service
engineers also provide input into the forecast for specific large customers.
2.3.2.3 All Other Energy Sales
The All Other Energy Sales category for SWEPCO includes public street and highway
lighting (or other retail sales) and sales to municipals. SWEPCO wholesale requirements
customers include the cities of Bentonville, Hope and Prescott in Arkansas, City of Minden in
Louisiana, East Texas Electric Cooperative, Northeast Texas Electric Cooperative, Rayburn
County Electric Coop, and Tex-La Electric Reliability Coop.
These wholesale loads are
generally longer term, full requirements, and cost-of-service based contracts.
Both the other retail and municipal models are estimated using ARIMA models. SWEPCO's
short-term forecasting model for Public Street and highway lighting energy sales includes
binaries, and lagged energy sales. The sales-for-resale model includes binaries, heating and
cooling degree-days, lagged error terms and lagged energy sales.
Off-system sales and/or sales of opportunity are not relevant to the net energy requirements
forecast as they are not requirements load or part of the IRP process.
11
DRAFT 2015 Integrated Resource Plan
2.3.3 Long-term Forecasting Models
The goal of the long-term forecasting models is to produce a reasonable load outlook for up
to 30 years in the future. Given that goal, the long-term forecasting models employ a full range
of structural economic and demographic variables, electricity and natural gas prices, weather as
measured by annual heating and cooling degree-days, and binary variables to produce load
forecasts conditioned on the outlook for the U.S. economy, for the SWEPCO service-area
economy, and for relative energy prices.
Most of the explanatory variables enter the long-term forecasting models in a
straightforward, untransformed manner. In the case of energy prices, however, it is assumed,
consistent with economic theory, that the consumption of electricity responds to changes in the
price of electricity or substitute fuels with a lag, rather than instantaneously. This lag occurs for
reasons having to do with the technical feasibility of quickly changing the level of electricity use
even after its relative price has changed, or with the widely accepted belief that consumers make
their consumption decisions on the basis of expected prices, which may be perceived as
functions of both past and current prices.
There are several techniques, including the use of lagged price or a moving average of price
that can be used to introduce the concept of lagged response to price change into an econometric
model. Each of these techniques incorporates price information from previous periods to
estimate demand in the current period.
The general estimation period for the long-term load forecasting models was 1995-2013.
The long-term energy sales forecast is developed by blending of the short-term forecast with the
long-term forecast. The energy sales forecast is developed by making a billed/unbilled
adjustment to derive billed and accrued values, which are consistent with monthly generation.
2.3.3.1 Supporting Models
In order to produce forecasts of certain independent variables used in the internal energy
requirements forecasting models, several supporting models are used, including a natural gas
price model for SWEPCO’s Arkansas, Louisiana and Texas service areas. These models are
discussed below.
12
DRAFT 2015 Integrated Resource Plan
2.3.3.1.1 Consumed Natural Gas Pricing Model
The forecast price of natural gas used in the Company's energy models comes from a model
of state natural gas prices for four primary consuming sectors: residential, commercial, industrial
and electric utilities. In the state natural gas price models sectoral prices are related to East North
Central Census region’s sectoral prices. The natural gas price model is based upon 1980-2013
historical data.
2.3.3.2 Residential Energy Sales
Residential energy sales for SWEPCO are forecasted using two models, the first of which
projects the number of residential customers, and the second of which projects kWh usage per
customer. The residential energy sales forecast is calculated as the product of the corresponding
customer and usage forecasts.
2.3.3.2.1 Residential Customer Forecasts
The long-term residential customer forecasting models are typically monthly. The
explanatory economic and demographic variables include gross regional product, population and
households are used in various combinations in the Company’s three jurisdictions. Each
jurisdiction’s model employs a lagged dependent variable to capture the adjustment of customer
growth to changes in the economy. There are also binary variables to capture monthly variations
in customers, unusual data points and special occurrences.
The customer forecast is blended with the short-term residential customer forecast to
produce a final forecast.
2.3.3.2.2 Residential Energy Usage Per Customer
The residential usage model is estimated using a Statistically Adjusted End-Use model
(SAE), which was developed by Itron, a consulting firm with expertise in energy modeling. This
model assumes that use will fall into one of three categories: heat, cool and other. The SAE
model constructs variables to be used in an econometric equation where residential usage is a
function of Xheat, Xcool and Xother variables.
13
DRAFT 2015 Integrated Resource Plan
The Xheat variable is derived by multiplying a heating index variable by a heating use
variable. The heating index incorporates information about heating equipment saturation; heating
equipment efficiency standards and trends; and thermal integrity and size of homes. The heating
use variable is derived from information related to billing days, heating degree-days, household
size, personal income, gas prices and electricity prices.
The Xcool variable is derived by multiplying a cooling index variable by a cooling use
variable. The cooling index incorporates information about cooling equipment saturation;
cooling equipment efficiency standards and trends; and thermal integrity and size of homes. The
cooling use variable is derived from information related to billing days, heating degree-days,
household size, personal income, gas prices and electricity prices.
The Xother variable estimates the non-weather sensitive sales and is similar to the Xheat
and Xcool variables. This variable incorporates information on appliance and equipment
saturation levels; average number of days in the billing cycle each month; average household
size; real personal income; gas prices and electricity prices.
The appliance saturations are based on historical trends from SWEPCO’s residential
customer survey. The saturation forecasts are based on U.S. Department of Energy (DOE) EIA
forecasts and analysis by Itron. The efficiency trends are based on DOE forecasts and Itron
analysis. The thermal integrity and size of homes are for the East North Central Census Region
and are based on DOE and Itron data.
The number of billing days is from internal data. Economic and demographic forecasts are
from Moody’s Analytics and the electricity price forecast is developed internally.
The SAE residential models are estimated using linear regression models. These monthly
models are typically for the period January 1995 through January 2014. It is important to note, as
will be discussed later in this document, that this modeling has incorporated the reductive effects
of the Energy Policy Act of 2005 (EPAct), the Energy Independence and Security Act of 2007
(EISA), American Recovery and Reinvestment Act of 2009 (ARRA) and Energy Improvement
and Extension Act of 2008 (EIEA2008) on the residential (and commercial) energy usage.
14
DRAFT 2015 Integrated Resource Plan
The long-term residential energy sales forecast is derived by multiplying the “blended”
customer forecast by the usage forecast from the SAE model.
Separate residential SAE models are estimated for the Company’s Arkansas, Louisiana and
Texas jurisdictions.
2.3.3.3 Commercial Energy Sales
Long-term commercial energy sales are forecast using a SAE model. These models are
similar to the residential SAE models, where commercial usage is a function of Xheat, Xcool and
Xother variables.
As with the residential model, Xheat is determined by multiplying a heating index by a heat
use variable. The variables incorporate information on heating degree-days, heating equipment
saturation, heating equipment operating efficiencies, square footage, average number of days in a
billing cycle, commercial output and electricity price.
The Xcool variable uses measures similar to the Xheat variable, except it uses information
on cooling degree-days and cooling equipment, rather than those items related to heating load.
The Xother variable measures the non-weather sensitive commercial load. It uses nonweather sensitive equipment saturations and efficiencies, as well as billing days, commercial
output and electricity price information.
The saturation, square footage and efficiencies are from the Itron base of DOE data and
forecasts. The saturations and related items are from EIA’s 2013 Annual Energy Outlook. Billing
days and electricity prices are developed internally. The commercial output measure is real
commercial gross regional product from Moody’s Analytics. The equipment stock and square
footage information are for the East North Central Census Region.
The SAE is a linear regression for the period which is typically January 2001 through
January 2014. As with the residential SAE model, the effects of EPAct, EISA, ARRA and
EIEA2008 are captured in this model. Separate commercial SAE models are estimated for the
Company’s Arkansas, Louisiana and Texas jurisdictions.
15
DRAFT 2015 Integrated Resource Plan
2.3.3.4 Industrial Energy Sales
The Company uses some combination of the following economic and pricing explanatory
variables: service area gross regional product manufacturing, service area manufacturing
employment, FRB industrial production indexes, service area industrial electricity prices and
state industrial natural gas price. In addition binary variables for months are special occurrences
and are incorporated into the models. Based on information from customer service engineers
they may be load added or subtracted from the model results to reflect plant openings, closures
or load adjustments. Separate models are estimated for the Company’s Arkansas, Louisiana and
Texas jurisdiction. The last actual data point for the industrial energy sales models is January
2014.
2.3.3.5 All Other Energy Sales
The forecast of public-street and highway lighting relates energy sales to either service area
employment or service area population and binary variables.
The municipal energy sales model is specified linear with the dependent and independent
variables in linear form. Wholesale energy sales are modeled relating energy sales to economic
variables such as service area gross regional product, heating and cooling degree-days and binary
variables. Binary variables are necessary to account for discrete changes in energy sales that
result from events such as the addition of new customers. The long-term forecast reflects the
effects of three wholesale contracts being terminated by 2018 and one contract being terminated
by 2020.
2.3.4.1 Blending Short and Long-Term Sales
Forecast values for 2014 and 2015 are taken from the short-term process. Forecast values
for 2016 are obtained by blending the results from the short-term and long-term models. The
blending process combines the results of the short-term and long-term models by assigning
weights to each result and systematically changing the weights so that by July of 2016 the entire
forecast is from the long-term models. The goal of the blending process is to leverage the relative
strengths of the short-term and long-term models to produce the most reliable forecast
possible. However, at times the short-term models may not capture structural changes in the
16
DRAFT 2015 Integrated Resource Plan
economy as well as the long-term models, which may result in the long-term forecast being used
for the entire forecast horizon.
2.3.4.2 Billed/Unbilled Analysis
Unbilled energy sales are forecast using the same methodology that is used by the Company
to compute actual unbilled sales each month as part of its closing process. The Company starts
with the projected monthly internal energy requirements forecast, subtracts the forecasted billed
sales and estimate for line losses to derive the forecasted unbilled sales.
2.3.4.3 Losses and Unaccounted-For Energy
Energy is lost in the transmission and distribution of the product. This loss of energy from
the source of production to consumption at the premise is measured as the average ratio of all
FERC revenue class energy sales measured at the premise meter to the net internal energy
requirements metered at the source. In modeling, Company loss study results are incorporated to
apply losses to each revenue class.
2.4 Forecast Methodology for Seasonal Peak Internal Demand
The demand forecast model is a series of algorithms for allocating the monthly internal
energy sales forecast to hourly demands. The inputs into forecasting hourly demand are blended
revenue class sales, energy loss multipliers, weather, 24-hour load profiles and calendar
information.
The weather profiles are developed from representative weather stations in the service area.
Twelve monthly profiles of average daily temperature that best represent the cooling and heating
degree-days of the specific geography are taken from the last 30 years of historical values. The
consistency of these profiles ensures the appropriate diversity of the company loads.
The 24-hour load profiles are developed from historical hourly company or jurisdictional
load and end-use or revenue class hourly load profiles. The load profiles were developed from
segregating, indexing and averaging hourly profiles by season, day types (weekend, midweek
and Monday/Friday) and average daily temperature ranges.
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DRAFT 2015 Integrated Resource Plan
In the end, the profiles are benchmarked to the aggregate energy and seasonal peaks
through the adjustments to the hourly load duration curves of the annual 8,760 hourly values.
These 8,760 hourly values per year are the forecast load of SWEPCO and the individual
companies of AEP that can be aggregated by hour to represent load across the spectrum from
end-use or revenue classes to total AEP-East, AEP-West (SPP), or total AEP system. Net
internal energy requirements are the sum of these hourly values to a total company energy need
basis. Company peak demand is the maximum of the hourly values from a stated period (month,
season or year).
2.5 Load Forecast Results and Issues
All tables referenced in this section of the report can be found in the appendix of this report
in Exhibit G: Section 2 Tables.
2.5.1 Load Forecast
Table 2-1 presents SWEPCO's annual internal energy requirements, disaggregated by major
category (residential, commercial, industrial, other retail and wholesale sales, as well as losses)
on an actual basis for the years 2004-2013, 2014 data are nine months actual and three months
forecast and on a forecast basis for the years 2015-2034. The exhibit also shows annual growth
rates for both the historical and forecast periods. Corresponding retail sales information for the
Company’s Louisiana Retail service area is given on Table 2-2.
2.5.2 Peak Demand and Load Factor
Table 2-3 provides SWEPCO’s seasonal peak demands, annual peak demand, internal
energy requirements and annual load factor on an actual basis for the years 2004-2013, 2014 data
are nine months actual and three months forecast and on a forecast basis for the year 2015-2034.
The table also shows annual growth rates for both the historical and forecast periods.
2.5.3. Monthly Data
Table 2-4 provides historical monthly sales data for SWEPCO by customer class
(residential, commercial, industrial, other retail and wholesale) for the period January 2004
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DRAFT 2015 Integrated Resource Plan
through September 2014. Table 2-5 provides forecast SWEPCO monthly sales data by customer
class for October 2014 through December 2034.
2.5.4 Prior Load Forecast Evaluation
Table 2-6 presents a comparison of SWEPCO’s energy sales and peak demand forecasts
in the 2012 IRP with the actual and weather normal data for 2012 and 2013. The primary reason
for the forecast differences is that the economy did not rebound as quickly as was expected when
the load forecast used in the previous (2012) IRP was developed. On a national level, real GDP
was expected to grow at 4.3% and 3.9% in 2012 and 2013, respectively. Meanwhile, real GDP
grew at 2.9% and 1.9% in 2012 and 2013, respectively. For the SWEPCO service area real
personal income per capita was projected to grow 2.6% and 2.7% in 2012 and 2013,
respectively. However, service area real personal income actually grew at 1.8% and 0.3% in
2012 and 2013, respectively. As the sluggish economy was seen as the primary reason for the
forecast differences, there were no significant changes to the forecast model structures. But,
there is a constant monitoring of the modelling process to seek improvement in forecast
accuracies. Table 2-7 provides the impact of demand-side management on the 2012 IRP.
2.5.5 Weather Normalization
The load forecast presented in this report assumes normal weather. To the extent that
weather is included as an explanatory variable in various short- and long-term models, the
weather drivers are assumed to be normal for the forecast period.
2.5.6 Significant Determinant Variables
Table 2-8 provides significant economic and demographic variables incorporated in the
various residential long-term energy sales models for the Company.
Table 2-9 provides
significant economic variables utilized in the various SWEPCO jurisdictional commercial energy
sales models. Table 2-10 presents significant economic variables that the Company employed in
its jurisdictional industrial models. Table 2-11 depicts the significant economic variables the
Company incorporated in its other retail and wholesale energy sales models.
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DRAFT 2015 Integrated Resource Plan
2.5.7 Changing Usage Patterns
Figure 2-2 presents the normal residential and commercial usage per customer between
1991 and 2003. Usage per residential customer trended upward through this period. As seen on
Figure 2-3, the more recent residential usage trend has been relatively flat. Any gains in usage
per customer due to economic growth in this period have been offset by efficiency mandates in
various pieces of federal legislation. Commercial usage-per-customer patterns are similar with
growth through 2002 and usage per customer has declined over the latter period due to efficiency
gains. These changing usage patterns have had an influence on the forecast of energy sales in the
residential and commercial sectors.
Figure 2-2
Southwestern Electric Power Company
Normal Usage per Customer
16,000
85,000
80,000
14,000
12,000
70,000
65,000
10,000
60,000
8,000
55,000
50,000
6,000
45,000
4,000
40,000
1991
1992
1993
1994
1995
1996
1997
Residential
20
1998
Commercial
1999
2000
2001
2002
Commercial Usage (kWh/Customer)
Residential Usage (kWh/Customer)
75,000
DRAFT 2015 Integrated Resource Plan
Figure 2-3
17,000
100,000
15,000
95,000
13,000
90,000
11,000
85,000
9,000
80,000
7,000
75,000
5,000
Commercial Usage (kWh/Customer)
Residential Usage (kWh/Customer)
Southwestern Electric Power Company
Normal Usage per Customer
70,000
2003
2004
2005
2006
2007
2008
Residential
2009
2010
2011
2012
2013
Commercial
2.5.8 DSM Impacts on the Load Forecast
Table 2-12 provides the DSM/Energy Efficiency impacts incorporated in SWEPCO’s
load forecast provided in this report. Annual energy and seasonal peak demand impacts are
provided for the Company and its Louisiana jurisdiction.
2.5.9 Losses and Unaccounted for Energy
Actual and forecast losses and unaccounted for energy are provided in Table 2-13. See
section 2.3.3.8 for a discussion of loss estimation. At this time the Company does not have any
planned loss reduction programs.
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DRAFT 2015 Integrated Resource Plan
2.5.10 Interruptible Load
The Company has 26 customers with interruptible provisions in their contracts. The
aggregate on-peak capacity available for interruptions is 81 MW. The load forecast does not
reflect any load reductions for these customers. Rather, the interruptible load is seen as a
resource when the Company’s load is peaking. As such, estimates for “demand response”
impacts are reflected by SWEPCO in determination of SPP-required resource adequacy (i.e.,
SWEPCO’s projected capacity position).
2.5.11 Blended Load Forecast
As noted above, at times the short-term models may not capture structural changes in the
economy as well as the long-term models, which may result in the long-term forecast being used
for the entire forecast horizon. Table 2-14 provides an indication of which retail models are
blended and which strictly use the long-term model results. In addition, eight of the nine
wholesale forecasts utilize the long-term forecast model results and the uses the blended model
results.
2.5.12 Large Customer Changes
The Company’s customer service engineers are in continual contact with the Company’s
large commercial and industrial customers about their needs for electric service.
These
customers will relay information about load additions and reductions. This information will be
compared with the load forecast to determine if the industrial or commercial models are
adequately reflecting these changes. If the changes are different from the model results, then add
factors may be used to reflect those large changes that are different from those from the forecast
models’ output.
2.5.13 Wholesale Customer Contracts
Company
representatives
are
in
continual
contact
with
wholesale
customer
representatives about their contractual needs. If a wholesale customer intends to seek bids for
supplying them power, they typically would need to give the Company a five year notice of such
intentions, although there may be stipulations within a contract that permits the customer to do so
22
DRAFT 2015 Integrated Resource Plan
earlier. Within the context of these two items, the Company has three wholesale customers with
“full requirements” load contracts that will expire by 2018 and one such customer whose
contract expires by 2020. The load for these wholesale customers has been removed from the
load forecast at the appropriate dates. Concurrently, any self-generation provided by those
wholesale customers that is appropriately “assumed” by SWEPCO for purposes of its long-term
resource planning has been likewise removed.
2.6 Load Forecast Scenarios
The base case load forecast is the expected path for load growth that the Company uses
for planning. There are a number of known and unknown potentials that could drive load growth
different from the base case. While potential scenarios could be quantified at varying levels of
assumptions and preciseness, the Company has chosen to frame the possible outcomes around
the base case. The company recognizes the potential desire for exact quantification of outcomes,
but the reality is if the all possible outcomes were known with a degree of certainty, then it
would become part of the base case.
Forecast sensitivity scenarios have been established which are tied to respective high and
low economic growth cases. The high and low economic growth scenarios are consistent with
scenarios laid out in the EIA’s 2014 Annual Outlook. While other factors may affect load
growth, this analysis only considered high and low economic growth. The economy is seen as a
crucial factor affecting future load growth.
“Low Load” Sensitivity Case
The Low Load forecast reflects the impact of low economic growth for the region and
consistent with the low economic growth presented by EIA.
The Low Load forecast projects firm peak load growth to average -0.8% per year on a
compound basis. Total energy growth is also projected to average about -0.8% per year. The load
factor is unchanged from the Base Case at about 57%. The low forecast for energy is 11.7%
below the base forecast in 2035.
“High Load” Sensitivity Case
23
DRAFT 2015 Integrated Resource Plan
The High Load forecast represents a scenario of more sustained growth for the
residential, commercial and industrial customer classes. As with the Low Load Case Load
Forecast the high economic growth scenario is consistent with EIA high growth in its economic
scenario.
The High Load forecast projects firm peak load growth to average 0.2% per year. Energy
growth is also projected to average 0.2 % per year with a load factor of 57%. The high forecast
for energy is 9.5% above the base forecast in 2035.
24
DRAFT 2015 Integrated Resource Plan
3.0 Resource Evaluation
3.1 Current Resources
The initial step in the IRP process is the demonstration of the capacity resource
requirements. This “needs” assessment must consider projections of:

Existing capacity resources—current levels and anticipated changes

Anticipated changes in capability due to efficiency and/or environmental retrofit
projects

Changes resulting from decisions surrounding unit disposition evaluations

Regional and sub-regional capacity and transmission constraints/limitations

Load and peak demand

Current DR/EE

SPP capacity reserve margin and reliability criteria
3.2 Existing SWEPCO Generating Resources
The underlying minimum reserve margin criterion to be utilized in the determination of
SWEPCO’s capacity needs is based on the current SPP minimum capacity margin of 12 percent.2
As a function of peak demand this converts to an equivalent “reserve margin” of 13.6 percent.3
Exhibit D in the Appendix provides the Company’s detailed Capacity, Demand and
Reserves (CDR) report for the 20-year period through the year 2035 assuming no new capacity
additions. In addition to identifying current projected peak demand requirements of its internal
customers, this “going-in” position also identifies the MW capability of resources that are
projected to be required to meet the minimum SPP reserve margin criterion. For instance, at the
beginning of the first forecasted SPP planning year (2015),4 it indicates SWEPCO is expected to
rely on 5,685 MW of owned generating capability (seasonal ratings) to achieve this threshold.
Figure 3-1 graphically displays each generating resource and its age, relative to the other
generating resources. As depicted in the figure, the gas-steam units are the oldest units on the
2
Per Section 2.1.9 of the “Southwest Power Pool Criteria” (Latest Revision: April 25, 2011).
0.12 / (1 – 0.12) = 0.136.
4
For capacity planning/reporting purposes, SPP operates on a June (Year X) -through- May (Year X+1) fiscal year
basis.
3
25
DRAFT 2015 Integrated Resource Plan
SWEPCO system. These older units are of a less efficient design than newer Natural Gas
Combined Cycle (NGCC) units and therefore are dispatched far less frequently in SPP’s Day 2
market, resulting in much lower capacity factors. As a result, while these units have relatively
low fixed costs and provide capacity value, should either a catastrophic failure occur or a very
expensive component fails that would require replacing, there is a higher degree of probability
that such gas-steam units would not be economic to repair. In such a case, the unit would likely
be retired. With the exception of Lieberman 2, which will be retired in 2015, no firm
commitment has been made to retire the balance of the gas-steam assets. However, given the age
and the potential of such expensive component failures, this IRP assumes that certain of these
relative older, less efficient gas-steam units will be retired over the planning period.
Figure 3-1
SWEPCO Current Resource Fleet & Age ‐‐‐Fossil: 5,700 MW; Wind 469 MW (nameplate)‐‐‐
Years in‐Service 0
10
20
30
40
50
60
70
Flint Creek (50%) ‐‐ Gentry, AR (264 MW)
Welsh 1 ‐‐ Pittsburg, TX (528 MW)
Welsh 2 ‐‐ Pittsburg, TX (528 MW)
Solid‐Fuel
Welsh 3 ‐‐ Pittsburg, TX (528 MW)
Pirkey (86%) ‐‐ Hallsville, TX (580 MW)
Dolet Hills (40%) ‐‐ Mansfield, LA (257 MW)
J. W. Turk (73%) ‐‐ Fulton, AR (477 MW)
Lieberman 1 ‐‐ Mooringsport, LA (25 MW)
Lieberman 2 ‐‐ Mooringsport, LA (25 MW)
Lieberman 3 ‐‐ Mooringsport, LA (109 MW)
Lieberman 4 ‐‐ Mooringsport, LA (108 MW)
Knox Lee 2 ‐‐ Longview, TX (31 MW)
Knox Lee 3 ‐‐ Longview, TX (25 MW)
Gas Steam
Knox Lee 4 ‐‐ Longview, TX (71 MW)
Knox Lee 5 ‐‐ Longview, TX (342 MW)
Lone Star ‐‐ Lone Star, TX (50 MW)
Arsenal Hill 5 ‐‐ Shreveport, LA (110 MW)
Wilkes 1 ‐‐ Avinger, TX (168 MW)
Wilkes 2 ‐‐ Avinger, TX (355 MW)
Wilkes 3 ‐‐ Avinger, TX (352 MW)
Stall ‐‐ Shreveport, LA (511 MW)
Mattison 1‐4 ‐‐ Tontitown, AR (301 MW)
Gas ‐ CC
Gas ‐ CT
Majestic‐‐Carson&Potter Cty, TX (79.5 MWnp)
Majestic II‐‐Carson&Potter Cty, TX (79.6…
Flat Ridge ‐‐ Wichita, KS (109 MWnp)
Wind (PPA)
Canadian Hills‐‐Canadian Cty, OK (201MW np)
26
DRAFT 2015 Integrated Resource Plan
Table 3-1 below identifies the generating resources, shown in Exhibit D and some of the
key characteristics. Note, again, that the retirement dates shown for, specifically the gas units,
are for planning purposes only and do not represent a firm commitment to retire those units on
those dates. Unit retirement decisions will be made based on unit condition, ongoing unit
investment requirements, and relevant market factors. SWEPCO currently utilizes several other
capacity entitlements to meet the minimum SPP reserve margin requirement. As set forth in
Exhibit D, SWEPCO continues to incorporate several represented purchases of capacity from
non-affiliates; largely wholesale customers whom the Company has contracted to meet those
customers’ “full (load) requirements”. Under Section 5 of the CDR, beginning in 2015,
SWEPCO is expected to rely on 641 MW of such “Purchases without Reserves.”
Table 3-1
UNIT DATA Current Supply Side Fossil Resources as of Jan. 1, 2015
Plant
Arsenal Hill Knox Lee Lieberman
Lonestar
Mattison
Wilkes
Unit
5
2
3
4
5
1
2
3
4
1
1
2
3
4
1
2
3
Location
Fuel Type
In Service Planning Winter Date
Retirement Date Rating MW
Summer Rating MW
Shreveport, LA
Gas Steam
1960
2025
110
110
Longview, TX
Gas Steam
1950
1952
1956
1974
2020
2020
2021
2039
31
25
71
348
31
25
71
342
Mooringsport. LA
Gas Steam
1947
1949
1957
1959
2014
2019
2022
2024
86
86
235
235
0
25
109
108
Lonestar, TX
Gas Steam
1954
2019
50
50
Tontitown, AR
Gas (CT)
2007
2007
2007
2007
2052
2052
2052
2052
78
78
79
78
75
75
76
75
Avinger, TX
Gas Steam
1964
1970
1971
2029
2035
2036
168
365
360
168
355
352
J.L Stall
6
Shreveport, LA
Gas (CC)
2010
2045
534
511
Dolet Hills
1
Mansfield, LA
Lignite
1986
2036
257
257
Flint Creek
1
Gentry, AR
Coal
1978
2038
264
264
580
Pirkey
1
Hallsville, TX
Lignite
1985
2045
580
Turk
1
Fulton, AR
Coal
2012
2052
477
477
Welsh
1
2
3
Pittsburg, TX
Coal
1977
1980
1982
2037
2016
2042
528
528
528
6,179
528
528
528
5,720
Units retiring within 20 year IRP planning period
27
DRAFT 2015 Integrated Resource Plan
3.3 Capacity Impacts of Environmental Compliance Plan
As a result of the existing and proposed environmental rules, there potentially could be
significant exposures surrounding the future operations of SWEPCO’s generating units. In order
for SWEPCO’s solid-fueled (coal and lignite) units to continue to operate in the future, they will
be required to comply with the recently finalized Mercury Air Toxics Standards (MATS).
SWEPCO’s Flint Creek unit will also be required to install best available retrofit technology to
comply with federal regional haze regulations
The specific environmental controls necessary to continue to operate the units in accordance
with the regulations are outlined in Section 3.4. In addition, SWEPCO must consider the
implications of the EPA’s proposed CPP. The proposed CPP does not impose specific emission
limits on existing units, but rather proposes state specific emission rates that are based on the
“Best System of Emission Reduction” as defined by the EPA. The implications of the proposed
CPP are addressed later in this report.
3.4 Existing Unit Disposition
For purposes of establishing a modeling “baseline,” it is necessary to establish assumptions
pertaining to all of the capacity and energy resources available to SWEPCO. Therefore, the
following Table 3-2 provides the SWEPCO unit-by-unit disposition profile for all solid-fuel and
gas-steam units that were assumed for purposes of portfolio modeling:
28
DRAFT 2015 Integrated Resource Plan
TABLE 3-2
UNIT DISPOSITION SUMMARY
Unit
Fuel Type
C.O.D. 1
Rating MW2
Retire (year) or Operate
5
2
3
4
5
1
2
3
4
1
1
2
3
4
Gas Steam
1960
110
2025
Gas Steam
1950
1952
1956
1974
31
25
71
342
2020
2020
2021
Operate
See Note (4)
See Note (4)
See Note (4)
See Note (4)
Gas Steam
1947
1949
1957
1959
0
25
109
108
2014
2019
2022
2024
See Note (4)
See Note (4)
See Note (4)
See Note (4)
Gas Steam
1954
50
2019
See Note (4)
Gas (CT)
2007
2007
2007
2007
75
75
76
75
Operate
Operate
Operate
Operate
Wilkes
1
2
3
Gas Steam
1964
1970
1971
168
355
352
2029
2035
Operate
J.L Stall
6
Gas (CC)
2010
511
Operate
Dolet Hills
1
Lignite
1986
257
2036
ACI/DSI/BH/DBAC/WWT
Flint Creek
1
Coal
1978
264
Retrofit
DFGD/ACI/BH/DBAC/LVWW/Note (4)
Pirkey
1
Lignite
1985
580
Retrofit
ACI/DBAC/LVWW/Note (4)
Turk
1
Coal
2012
477
Operate
Welsh
1
2
3
Coal
1977
1980
1982
528
528
528
Retrofit
2016
Retrofit
Plant
Arsenal Hill Knox Lee Lieberman
Lonestar
Mattison
Retrofit Technology3
See Note (4)
See Note (4)
See Note (4)
ACI/BH/Stack/DBAC/BAPR/Note (4)
ACI/BH/Stack/DBAC/BAPR/Note (4)
Notes: (1) Commercial operation date.
(2) Peak net dependable capability (Summer) as of filing.
(3) ACI ‐ Activated Carbon Injection, BH ‐ Baghouse, DFGD ‐ Dry Flue Gas Desulfurization, DSI ‐ Dry Sorbent Injection, DBAC ‐ Dry Bottom Ash Conversion, WWT ‐ Waste Water Treatment, BAPR ‐ Bottom Ash Pond Reline/DBAC, LVWW ‐ Low Volume Waste Water
(4) A yet to be defined project to meet 316(b) standards
To confirm SWEPCO’s “going-in” position regarding its solid-fueled units in light of the
proposed CPP, SWEPCO undertook a modeling exercise to evaluate its nearer-term
environmental compliance related investments at Flint Creek, Welsh Units 1&3 and Pirkey by
29
DRAFT 2015 Integrated Resource Plan
applying an assumption of significantly higher dispatch costs related to solid fuel-unit CO2
emissions that would be tied to the prospect of, say, an equivalent “proxy” carbon/CO2 “tax” on
unit dispatch. Keeping in mind that it may be two to four years until State Implementation Plans
(SIPs) for CPP are developed and approved for Arkansas, Louisiana and Texas, SWEPCO desire
to confirm that it’s nearer-term investments in these solid-fuel units continue to be rational and
remain as the lowest cost alternative for its customers; even under a potentially “carbonconstrained” regime. To simulate the potential impact of the proposed CPP, in addition to a
Business-as-Usual view, three unique commodity price scenarios were created, each with
different “proxy” assumptions regarding the ultimate effect of the CPP on the (dispatchable)
value/cost of carbon and its attendant correlative impacts on natural gas and energy. Those
pricing scenarios being:

A “Business as Usual” scenario with no carbon costs assumed.

A “Base” –or most likely—pricing scenario where a carbon pricing proxy would
apply to all fossil generation at $15/metric ton beginning in 2022.
…as well as two extreme, lower probability (“plausible worst”) CPP-based carbon pricing
outcomes:

A scenario where a carbon cost proxy would be applied to coal generation but not
gas-fired generation using $15/metric ton in 2020 escalating annually to $25/metric
ton in 2030.

A scenario where carbon pricing would apply to all fossil generation beginning at
$25/metric ton in 2020 and escalating to $40/metric ton in 2030.
The impact of applying a cost to carbon emissions penalizes higher carbon emitting
resources while increasing the value of lower carbon emitting resources. As a point of reference,
ERCOT’s analysis5 of the proposed CPP determined that a CO2 price of between $20/ton and
$25/ton would attain the proposed CPP emission intensity goals for the state of Texas which is
largely embodied by the ERCOT system. SWEPCO’s other generation domiciled states—
Louisiana and Arkansas—have proposed CPP intensity goals similar to Texas.
5
See “ERCOT Analysis of the Impacts of the Clean Power Plan”, ERCOT Public, dated November 17,2014
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DRAFT 2015 Integrated Resource Plan
For each of these solid-fuel unit disposition evaluations, SWEPCO calculated the present
value of long-term revenue requirements using the four pricing scenarios listed above under a
variety of “retrofit and operate” cases—to meet MATS as well as known and potential National
Ambient Air Quality Standards (NAAQS)—or “retire and replace” options. The “retire and
replace” disposition cases incorporated both new build NGCC options as well as a market
option. In the cases of Welsh and Flint Creek, “gas conversion” options were also considered.
In all of the scenarios that were considered, the lowest cost solution was to retrofit and continue
to operate the solid-fuel units; thereby confirming SWEPCO’s current disposition strategy for
these units.
SWEPCO assumed that a portion of its less efficient gas-fired steam units would retire over
the course of the IRP planning period. These units, while providing capacity value, contribute
very little energy value. Therefore, as equipment ages and needs to be replaced, there will come
a time where the cost of replacing equipment will exceed the future value of energy and capacity
those units provide. However, this in no way serves as a commitment to this course of action for
these SWEPCO unit dispositions—or the attendant timing of same. Rather, it simply serves as a
basis for the modeling process for SWEPCO unit analyses. SWEPCO will weigh a variety of
factors prior to making unit retirement decisions. These factors include such variables as:
1. the ongoing cost to operate and maintain the unit,
2. the cost of replacement capacity and energy,
3. the availability of replacement options, and
4. any reliability related issues or remedial actions necessary due to unit retirement.
3.5 Environmental Compliance
3.5.1 Introduction
The following information provides background on both current and future environmental
regulatory compliance plan issues within the SWEPCO system. The Company’s goal is to
develop a comprehensive plan that not only allows SWEPCO to meet the future resource needs
of the Company in a reliable manner, but also to meet increasingly stringent environmental
requirements in a cost-effective manner.
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DRAFT 2015 Integrated Resource Plan
3.5.2 Air Emissions Compliance
There are numerous air regulations that have been promulgated or that are under
development, which will apply to SWEPCO’s facilities. Currently, air emissions from plants are
regulated by Title V operating permits that incorporate the requirements of the Clean Air Act
(CAA) and applicable SIPs. Other applicable requirements include those related to the Clean Air
Interstate Rule (CAIR), the Cross-States Air Pollution Rule (CSAPR), MATS and the Regional
Haze Rule. Several air regulatory programs are under development and will apply to SWEPCO
plants, including those related to the regulation of GHG and revisions to the NAAQS for sulfur
dioxide (SO2), oxides of nitrogen (NOx), fine particulate matter (PM), and ozone.
To ensure compliance, air emissions at SWEPCO’s units are or will be reduced through the
use of some combination of the following control practices/technologies: electrostatic
precipitators (ESP), low sulfur coal, low NOx burners, baghouses, over-fire air (OFA), activated
carbon injection (ACI), wet flue gas desulfurization (FGD), dry FGD, dry sorbent injection
(DSI), selective catalytic reduction (SCR), carbon monoxide catalysts, emissions allowance
purchases (for CSAPR), and dry fly-ash handling systems.
3.5.3 Environmental Compliance Programs
3.5.3.1 Clean Air Interstate Rule (CAIR) and Cross-State Air Pollution Rule (CSAPR)
The CAIR was created in order to significantly reduce emissions of SO2 and NOx, primarily
from the power generation sector, in two phases with compliance deadlines in 2009/2010 and
2015. The emissions reductions are implemented through an interstate cap and trade program.
The cap and trade program provides emission allowances for SO2 and NOx for sources and for
states. EGUs’ compliance with the annual NOx reduction requirements began January 1, 2009
and with the ozone season (summer) NOx reduction requirements May 1, 2009.
EGUs’
compliance with the annual SO2 reduction requirements began January 1, 2010. The geographic
reach of the CAIR program included SWEPCO states. As of these dates, operators of electric
generating units were required to hold enough CAIR allowances in their respective accounts to
account for every ton of NOx or SO2 emitted.
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DRAFT 2015 Integrated Resource Plan
The CAIR was remanded back to EPA in 2008 by the D.C. Circuit Court of Appeals to
address legal deficiencies identified by the Court. While CAIR remained in effect, EPA proposed
the Clean Air Transport Rule (CATR) in August 2010 as a replacement.
EPA ultimately
finalized this rule as the Cross-State Air Pollution Rule (CSAPR) in mid-2011. The CSAPR
addresses NAAQS for ozone and PM, and is focused on the reduction of emissions of SO2 and
NOX within 28 eastern, southern and mid-western states—including Louisiana, Arkansas (NOx
only) and Texas (SO2 only). Along with other requirements, the final CSAPR established statespecific annual emission “budgets” for SO2 and NOx. The EPA’s approach for obtaining these
emission reductions requires each state to limit its emissions to a prescribed cap. Based on this
cap, each emitting unit within affected states was allocated a specified budget of NOx and SO2
allowances for the applicable compliance period, whether annual or ozone season. An annual
cap for SO2 and NOx and an ozone season cap for NOx emissions was established for each
affected state. Allowance trading within and between states was allowed on a regional basis.
The CSAPR was stayed on December 30, 2011 by the DC Circuit Court of Appeals, and
was subsequently vacated by the same Court. EPA petitioned the United States Supreme Court
to review the vacatur decision on the CSAPR, and the Supreme Court announced on June 24,
2013 that it accepted the petition. On April 29, 2014, the U.S. Supreme Court reversed the D.C.
Circuit opinion vacating CSAPR and in October 2014, the U.S. Court of Appeals for the D.C.
Circuit ordered that EPA's June 2014 motion to lift the stay of the Cross-State Air Pollution Rule
be granted. On November 21, 2014, EPA issued a rule that aligned the dates in the CSAPR rule
text with the revised court-ordered schedule, including 2015 Phase 1 implementation and 2017
Phase 2 implementation.
In a separate action, EPA issued a Notice of Data Availability
(NODA), as required by the CSAPR, that aligns the final CSAPR default allowance allocation
years with the revised court-ordered schedule. The EPA amended deadlines for CSAPR shifted
deadlines for rule compliance to 2015 and 2016 for Phase I.
Parties will resolve some
outstanding issues with regard to CSAPR in briefs due to the D.C. Circuit in December and in
oral arguments on March 11, 2015. The CSAPR is now in effect, having been published in the
Federal Register on December 3, 2014.
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DRAFT 2015 Integrated Resource Plan
3.5.3.2 Mercury and Air Toxics Standard (MATS) Rule
The final MATS Rule became effective on April 16, 2012, with compliance required within
three years of this date (with the possibility of a one-year administrative extension in certain
circumstances). This rule regulates emissions of hazardous air pollutants (HAPs) from coal and
oil-fired electric generating units. HAPs regulated by this rule are: 1) mercury; 2) several nonmercury metals such as arsenic, lead, cadmium and selenium; 3) various acid gases including
hydrochloric acid (HCl); and 4) many organic HAPs. The MATS Rule includes stringent
emission rate limits for several individual HAPs, including mercury. In addition, this rule
contains alternative stringent emission rate limits for surrogates representing two classes of
HAPs, acid gases and non-mercury particulate metal HAPs. The surrogates for the non-mercury
particulate metal and acid gas HAPs are filterable PM and HCl, respectively. The rule regulates
organic HAPs through work practice standards.
Table 3-2 identifies retrofit technologies that are being added to the SWEPCO fleet,
including technologies to meet the requirements of the MATS Rule.

Dolet Hills Unit 1 will be installing an activated carbon injection (ACI)
system, dry sorbent injection (DSI) technology, and a baghouse to mitigate
mercury and PM emissions.

Pirkey Unit 1 will be installing an ACI system.

Welsh (Units 1 &3) will be installing an ACI system with a baghouse.

Welsh Unit 2 will be retired per an unrelated settlement agreement and
received an extension of the MATS requirements until the unit retires.

Flint Creek will also have installed a dry FGD (NIDTM technology), ACI
system and a baghouse to meet MATS and regional haze requirements.
All other SWEPCO generating units are expected to meet the MATS requirements without
modification.
On November 25, 2014, the U.S. Supreme Court granted petitions to hear state and
industry challenges against the EPA’s MATS Rule to decide whether EPA unreasonably refused
to consider costs in determining that it is appropriate to regulate hazardous air pollutants emitted
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DRAFT 2015 Integrated Resource Plan
by coal- and oil-fired electric generating units (EGUs). The Court will hear oral arguments in
the spring of 2015 and render its decision by the end of June 2015.
3.5.3.3 Coal Combustion Residuals (CCR) Rule
The EPA issued a proposed rule in June 2010 to address the management of residual
byproducts from the combustion of coal in power plants (coal ash) and captured by emission
control technologies, such as FGD. The proposed rule included specific design and monitoring
standards for new and existing landfills and surface impoundments, as well as measures to
ensure and maintain the structural integrity of surface impoundment/ponds. The final CCR rule,
signed by EPA’s Administrator on December 19, 2014, regulates CCR as a non-hazardous waste
under Subtitle D of RCRA and becomes effective six (6) months from the date of its publication
in the Federal Register (date unknown). While the final rule is still under review, initial estimates
of anticipated plant modifications and capital expenditures are factored into this IRP.
3.5.3.4 Clean Water Act “316(b)” Rule
A proposed rule for the Clean Water Act 316(b) was issued by the EPA on March 28, 2011,
and final rulemaking was promulgated on May 19, 2014. The impact of the final rule on
SWEPCO facilities will be relatively minor and estimated costs are factored into this IRP.
3.5.4 Future Environmental Rules
Several environmental regulations have been proposed that will apply to the electricity
generating sector once finalized. The following is not meant to be comprehensive, but lists some
of the major issues that will need to be addressed over the forecast period.
3.5.4.1 Effluent Limitation Guidelines and Standards (ELG)
The EPA proposed an update to the ELG (40 CFR 423) for the steam electric power
generating category in the Federal Register on June 7, 2013. The ELG would require more
stringent controls on certain discharges from certain EGUs and will set technology-based limits
for waste water discharges from power plants with a main focus on process and wastewater from
FGD, fly ash sluice water, bottom ash sluice water and landfill/pond leachate. The final rule is
expected by September 30, 2015.
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DRAFT 2015 Integrated Resource Plan
3.5.4.2 National Ambient Air Quality Standards (NAAQS)
The Clean Air Act requires the EPA to establish and periodically review NAAQS designed
to protect public health and welfare. Several NAAQS have been recently revised or are under
review, which could lead to more stringent SO2 and NOx limits. This includes NAAQS for SO2
(revised in 2010), NO2 (revised in 2010), fine PM (revised in 2012), and ozone (final revision
expected in 2015). The scope and timing of potential requirements is uncertain.
3.5.4.3 Carbon and GHG Regulations
EPA proposed GHG New Source Performance Standards (NSPS) for new fossil fuel-fired
EGUs in April, 2012 (Section 111b of the Act). This initial “111(b)” proposal was subsequently
withdrawn and a new proposal “Carbon Pollution Standard for New Power Plants” was issued by
EPA on September 20, 2013, but not published in the Federal Register until January, 2014.
Under the September 2013 proposal, new large natural gas-fired turbines would need to meet a
limit of 1,000 pounds of CO2 per megawatt-hour, while new small natural gas-fired turbines
would need to meet a limit of 1,100 pounds of CO2 per megawatt-hour. New coal-fired units
would need to meet a limit of 1,100 pounds of CO2 per megawatt-hour, and would have the
option to meet a somewhat tighter limit if they choose to average emissions over multiple years,
giving those units additional operational flexibility. Of significant note, the emission rate limit
prescribed for new coal-fired facilities was based solely on the use of carbon capture and
sequestration technology, which in SWEPCO’s view has not been adequately demonstrated.
Thus, this standard would prevent the development of new coal-fuel generation resource for the
foreseeable future.
EPA has been working on a regulatory program for greenhouse gas emissions from existing
power plants since December 2010 (Section 111d). EPA proposed NSPS guidelines on June 2,
2014 that are designed to reduce CO2 emissions from existing fossil fuel generation. The
program is referred to as the Clean Power Plan or “CPP”. Separately, on June 2, 2014, EPA
proposed NSPS related to the modification or reconstruction of existing units. A comment
period ran to December 2014 and all three of these proposals are expected to be finalized by
EPA during the summer of 2015.
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DRAFT 2015 Integrated Resource Plan
The proposed timeline for implementing the existing source goals is aggressive. The
anticipated timing of the proposed CPP (111d) is as follows:
1)
The EPA plans to issue final guidelines during the summer of 2015.
2)
SIPs are to be developed by individual states, for submittal to the EPA by summer,
2016. If a state is developing its SIP but needs additional time, a second year is
available, until summer, 2017. There is also a provision to allow one more year for
multiple states that decide to develop a regional plan. This would extend the SIP
development process until summer, 2018.
3)
The EPA will then have up to one year to review and approve SIPs, or to
disapprove the SIPs and implement a Federal Implementation Plan (FIP). Only
when a SIP or FIP has become final will regulated entities know the
requirements for their facilities and/or activities. This could occur as early as the
summer of 2017, but may extend beyond 2019.
4)
Under the proposed CPP, compliance requirements would become effective
beginning in 2020.
The proposed CPP is built upon four “building blocks,” which the EPA uses to calculate
proposed CO2 emission rate target/goals for each state.6 These four building blocks, and their
basic assumptions in the proposed CPP, are as follows:
1.
2.
3.
4.
Coal plant heat rate improvement - The EPA assumed that all coal generators can
improve operating efficiency by 6%, resulting in lower CO2 emission rates for those
generating units;
Re-dispatch of natural gas generation – The EPA assumed that existing and new
planned NGCC generating units could increase their capacity factor to 70%, with
the resulting increase in NGCC generation displacing more CO2-intensive, coal and
oil/gas steam generation;
Renewable Energy and Nuclear Energy – The EPA assumes that states will
implement what in effect is a 13% national renewable portfolio standard by 2030,
that no unplanned nuclear plant retirements occur, and that nuclear units currently
under construction are completed;
End-use Energy Efficiency programs – The EPA assumes that states can
eventually achieve annual incremental end-use energy efficiency levels equivalent
to 1.5% of sales, up to approximately 10% cumulative energy savings by 2030.
6
Where such emission rate, or “intensity” target equals: pounds of CO2 emitted, divided by applicable MWh generation from
both emitting sources as well as ‘carbon-free’ resources as provided in the rulemaking.
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DRAFT 2015 Integrated Resource Plan
Relying on various technical and economic assumptions for each of these building blocks,
some generic and some state-specific, the EPA calculated what it believes to be an achievable
CO2 emission rate for each state, starting with 2012 fossil unit operations and emissions as a
baseline. As summarized on Table 3-3, on an averaged basis from 2020 to 2029, the proposed
“Interim”, as well as “Final” (2030) CPP targets for the states in which SWEPCO owns fossilfired generation units are:
Table 3-3
State
Interim Goal
(proposed)
(lbs. CO2/ net MWh)
Final Goal
(proposed)
(lbs. CO2/ net MWh)
Arkansas
968
910
Louisiana
948
883
Texas
853
791
The Interim Goal will apply for the time period 2020 – 2029 and can be met as an average
over that 10 year period. The Final Goal represents what is expected to be achievable by 2030
and beyond. As depicted in Figure 3-2 and summarized in Table 3-4, these targets result in the
following proposed reduction of CO2 emission rates for SWEPCO states based on 2012
operation and emissions:
Figure 3-2
EPA CPP CO2 Intensity Reductions
#/MWh
2,500
2,000
1,500
1,000
500
0
Arkansas
EPA 2012 Adj Rate
Louisiana
2020‐2029 Avg. Goal
38
Texas
2030 Goal
DRAFT 2015 Integrated Resource Plan
Table 3-4
Required Reduction in CO2 Emission Rate from 2012
State
Interim Emissions
Reduction % (proposed)
Final Emissions Reduction
% (proposed)
Arkansas
37
45
Louisiana
31
40
Texas
28
39
As evidenced by the proposed emission reductions shown above, the emission rate targets
proposed by the EPA vary widely by state. In all three SWEPCO states, the Interim Goal
expectation is that the largest portion of carbon emissions reductions will come from “building
block #2”, re-dispatch of natural gas units. The three SWEPCO states are also expected to
achieve reductions via renewable energy (“building block #3”) and energy efficiency (“building
block #4”), with the expectation being greatest for Texas, where almost 50% of the reductions
are anticipated to come from these two proposed building blocks, followed by Louisiana, then
Arkansas.
The proposal provides little credit for the significant CO2 emission reductions that have
already been made by the electricity sector and that will continue to be made through the
remainder of this decade with the retirement of coal-fired generation in response to
environmental regulations and other factors.
SWEPCO’s CO2 emission intensity has been
reduced by more than 10 percent since 2005, and will be approximately an additional 4% lower
after it retires Welsh Unit 2 in 2016.
SWEPCO and AEP submitted extensive technical and legal comments on the proposed
CPP. The ultimate impacts of the rule on our resource planning will ultimately be dictated by the
guidelines of the final rule which could vary significantly from the initial proposal and, further,
the specific requirements of any ultimate SIP could be removed from the original EPA rule.
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DRAFT 2015 Integrated Resource Plan
3.5.4.4 Regional Haze Rule
The Regional Haze Rule (RHR) requires affected states to develop regional haze state
implementation plans (Regional Haze SIP) that contain enforceable measures and strategies for
reducing emission of pollutants associated with visibility impairment. Each SIP must require
certain eligible facilities to conduct an emission control analysis, known as Best Available
Retrofit Technology (BART), including nitrogen oxides (NOX), sulfur dioxide (SO2) and
particulate matter (PM) – to evaluate emission limitations necessary to improve visibility.
BART is applicable to EGUs greater than 250 megawatt (MW) and that are of a certain age.
On July 6, 2005, the EPA published the final “Regional Haze Regulations and Guidelines
for Best Available Retrofit Technology Determinations.” The Federal Clean Air Act (CAA) and
the RHR require certain states, including Arkansas and Texas, to make reasonable progress
toward the “prevention of any future, and the remedying of any existing, impairment of
visibility” in mandatory Class I Federal areas, both within the state and in each mandatory Class
I Federal area located outside the state which may be affected by emissions from within the state.
Air pollutants emitted by BART-eligible sources, which may reasonably be anticipated to cause
or contribute to visibility impairment in any mandatory Class I Federal area are: NOX, SO2, PM10, and PM-2.5. EPA also provided guidance on what level of control is reasonable for certain
BART-eligible sources, including EGUs, and published “presumptive BART” emission rates for
SO2 and NOX based on the types of cost-effective controls available.
3.5.4.4.1 Arkansas Regional Haze
The State of Arkansas and the Arkansas Department of Environmental Quality (ADEQ)
submitted a regional haze SIP to the EPA on April 2, 2008, to establish the emission limits
necessary to meet its BART obligations. The SIP also included in its supporting documentation
analysis by and correspondence from the subject-to-BART sources, outlining the pollution
controls reviewed for compliance with the RHR. Pursuant to the RHR, ADEQ identified 18
potential BART-eligible sources in Arkansas in its SIP.
Subsequently, ADEQ performed
modeling and determined that approximately 9 units at 6 Arkansas facilities are subject-toBART, one of which is Flint Creek.
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DRAFT 2015 Integrated Resource Plan
The ADEQ utilized the presumptive NOX and SO2 limits provided by the EPA in the
guidance document, Regional Haze Regulations and Guidelines for BART Determinations (70
Fed. Reg. 39,131). During the Regional Haze Rule development, these presumptive limits were
determined by the EPA to sufficiently result in significant improvements in visibility and to
ensure reasonable progress toward the national visibility goal.
The Regional Haze SIP developed by ADEQ was incorporated into Chapter 15 of
Regulation 19 of the Arkansas Pollution Control and Ecology Commission (APC&EC), with an
effective date of October 15, 2007, and a compliance deadline “as soon as practicable,” but no
later than October 15, 2013. However, on March 26, 2010, the APC&EC granted a variance
from the October 15, 2013 deadline, instead requiring compliance with BART as expeditiously
as practicable, but in no event later than five years after the EPA approval of the Arkansas
Regional Haze SIP. This was done because the EPA had not yet issued its determination on
whether or not it would approve the state’s Regional Haze SIP and the time needed to engineer,
permit and construct the necessary retrofits to comply with the presumptive limits in the SIP was
not sufficient given the delay in the EPA’s determination. On November 16, 2011, the EPA
issued their proposed decision on Arkansas’ Regional Haze SIP. The EPA proposed to deny
approval of the Regional Haze SIP, in part, and prescribed that the ADEQ perform additional
analysis then propose a revision to its SIP.
The EPA’s proposed decision to deny Arkansas’s Regional Haze SIP included a
requirement to perform a more detailed BART analysis in which potentially more restrictive
limits must be evaluated. SWEPCO coordinated with ADEQ and EPA to conduct that analysis
for Flint Creek and EPA indicated they had no further comments on November 8, 2013.
ADEQ has chosen to not prepare a revised SIP and is currently awaiting EPA to issue a
FIP which is expected in the first quarter of 2015. Details of the requirements and schedule for
Flint Creek will not be known until EPA issues the FIP.
Flint Creek has proposed to meet the NOX requirements through participation in the
CSAPR program. EPA has determined that, on a parameter-by-parameter basis, compliance
with CSAPR is sufficient to meet the Regional Haze obligations for facilities covered by that
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DRAFT 2015 Integrated Resource Plan
program. As an alternative to using compliance with CSAPR to meet BART obligations, Flint
Creek would install LNB/OFA and have a NOX limit of 0.23 lb NOX/mmBtu. The SO2 Regional
Haze requirements will be met with the installation of a dry scrubber (NIDS).
The existing PM emission limit (0.1 lb/mmBtu) was found to satisfy the BART PM
requirement.
3.5.4.4.2 Louisiana Regional Haze
Louisiana submitted a Regional Haze SIP to EPA in June of 2008. All SWEPCO units
were determined not to be “BART-eligible” and, therefore, no BART analysis or emission
reductions were required for BART. EPA partially approved and partially disapproved
Louisiana’s SIP in July 2012. EPA approved the BART determinations but required additional
evaluation to be done to meet the Reasonable Progress Goals and Long Term Strategy to
improve visibility in two Class I areas in Louisiana. The impact evaluation did not include any
of the SWEPCO units and no additional emission controls are expected for those facilities as a
result of the Regional Haze Rule at this time. States are required to reevaluate their Reasonable
Progress Goals and Long Term Strategy every five years.
3.5.4.4.3 Texas Regional Haze
Texas submitted their initial Regional Haze SIP to EPA February 2009 and the 5-year
update February 2014. Both submittals state that BART-eligible facilities in Texas do not
impact Class I areas such that emissions controls are required. EPA has reviewed the Texas SIP
and issued a Federal Implementation Plan in November 2014 for addressing Regional Haze in
Texas. EPA accepted portions of the Texas SIP that relate to BART-eligible facilities, however,
EPA determined that the Reasonable Progress Goals and Long Term Strategy did not adequately
address visibility improvements needed in certain Class I areas. EPA conducted impact analyses
to identify cost-effective controls to achieve those improvements. The FIP requires SO2
reductions for 15 units in Texas resulting in scrubber retrofits for 7 units and scrubber upgrades
for 7 other units. One unit is believed to be able to meet its new limit without adding additional
controls. No SWEPCO unit was included in the group EPA identified as needing to reduce
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DRAFT 2015 Integrated Resource Plan
emissions, and, therefore, SWEPCO units have no emission reductions resulting from Regional
Haze requirements at this time.
3.5.4.5 SWEPCO Environmental Compliance
This 2015 IRP considers the potential impacts of proposed EPA regulations to SWEPCO
generating facilities. Consistent with prior assessments performed by the Company over the last
several years—including prior SWEPCO IRPs—this IRP includes the assumption that there will
be future regulation of GHG/CO2 emissions which would become effective at some point in the
early 2020’s. Also, while the EPA is evaluating over 2 million comments received regarding the
proposed CPP, and will likely make modifications to the CPP7, SWEPCO has considered and
incorporated costs associated with carbon emissions in the form of a CO2 penalty or tax. Prior to
filing its final IRP in August 2015, EPA may have issued its final GHG rule. This final rule may
provide additional insight as to how or if SWEPCO’s Preferred Portfolio would be adjusted to
accommodate anticipated SIPs. Even so, until such SIPs have been reviewed and approved by
the EPA, SWEPCO’s ultimate compliance strategy will continue to be subject to change.
Environmental compliance requirements have a major influence on the consideration of new
supply-side resources for inclusion in the IRP because of the potential significant effects on both
capital and operational costs. Moreover, the cumulative cost of complying with these rules will
ultimately have an impact on existing coal-fueled units that are required to install and operate
emission control equipment.
3.6 SWEPCO Current Demand Side Programs
3.6.1 Background
Current “Demand Side Management” (DSM) refers to, for the purposes of this IRP, utility
programs, including tariffs, which encourage reduced energy consumption, either at times of
peak consumption or throughout the day/year. Programs or tariffs that reduce consumption at
the peak are “(peak) demand reduction” (DR) programs, while around-the-clock measures are
7
For example, in a letter from the Louisiana Department of Environmental Quality to EPA dated Sept. 12, 2014
identified data discrepancies which, if corrected, would increase Louisiana’s final goal from 883 lb CO2/MWh to
1078 lb CO2/MWh.
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DRAFT 2015 Integrated Resource Plan
typically categorized as “energy efficiency” (EE) programs.
The distinction between peak
demand reduction and energy efficiency is important, as the solutions for accomplishing each
objective are typically different, but not necessarily mutually exclusive.
Included in the load forecast discussed in Section 2 of this report are the demand and energy
impacts associated with SWEPCO “embedded” energy efficiency programs that have been
previously approved in Arkansas and Texas, as well as impacts from prospective programs that
started November 1, 2014 in Louisiana. As will be discussed later, within the IRP process, the
potential for additional or “incremental” demand-side resources, including EE activity—over and
above the levels embedded in the load forecast—as well as other smart-grid related projects such
as VVO, are modeled on the same economic basis as supply-side resources. However, because
customer-based EE programs are limited by factors such as customer acceptance and saturation,
an estimate as to their costs, timing and maximum impacts must be formulated. Exhibit B in the
Appendix offers a summary of the latest long-term projection of SWEPCO’s embedded demandside activity. For the year 2015, the Company anticipates 93 MW of peak demand reduction
(total Company basis); consisting of 12 MW and 81 MW of “passive” EE and “active” DR peak
demand reductions activity, respectively.8 As also observed in that summary, the rate of
projected growth in SWEPCO’s total embedded demand-side activity increasing over the 10year period (2015-24) by 23 MW, resulting in a compound annual growth rate (CAGR) in that
timeframe of 2.5%. In total, SWEPCO’s estimated demand-side contribution to reduce its peak
demand responsibility by the year 2035 is projected to be at 116 MW, or a figure then
representing 2.2% of the forecasted total SWEPCO peak demand.
3.6.2 Existing Demand Reduction/Energy Efficiency Mandates and Goals
The Energy Independence and Security Act of 2007 (EISA) requires, among other things, a
phase-in of heightened lighting efficiency standards, appliance standards, and building codes.
The increased standards will have a pronounced effect on energy consumption.
Many of the
standards already in place impact lighting. For instance, beginning in 2013 and 2014 common
8
“Passive” demand reductions are achieved via “around-the-clock” energy efficiency program activity as well as
voluntary price response programs; while “Active” DR is centered on focused summer peak reduction initiatives,
including interruptible contracts and electric load management/direct load control programs.
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DRAFT 2015 Integrated Resource Plan
residential incandescent lighting options have begun their phase out as have common
commercial lighting fixtures. Given that “lighting” options have comprised a large portion of
utility-sponsored energy efficiency programs over the past decade; this pre-established transition
already incorporated into the SAE long-term load forecast modeling previously describe in
Section 2 may greatly affect the market potential of utility energy efficiency programs in the near
and intermediate term. Table 3-5, illustrates the current schedule for the implementation of new
EISA codes and standards.
Table 3-5, Forecasted View of Relevant Energy Efficiency Code Improvements
Today's Efficiency or Standard Assumption
End Use
Technology
2012 2013 2014 2015 2016 2017 Central AC
Cooling
Room AC
Space Heating
Electric Resistance
Water Heating
Lighting
Appliances
2019 EER 9.8
2020 2021 2025 SEER 14.0/HSPF 8.0
Electric Resistance
EF 0.90
EF 0.95
EF 0.90
Heat Pump Water Heater
Screw‐in/Pin Lamps
Incandescent
Linear Fluorescent
T12 Advanced Incandescent ‐ tier 1 (20 lumens/watt)
Advanced Incandescent ‐ tier 2 (45 lumens/watt)
T8
Refrigerator/2nd Refrigerator
NAECA Standard
25% more efficient Freezer
NAECA Standard
25% more efficient Clothes Dryer
2024 Conventional
SEER 13.0/HSPF 7.7
Water Heater (>55 gallons)
Clothes Washer
2023 EER 11.0
Water Heater (<=55 gallons)
Dishwasher
2022 Conventional
Evaporative Room AC
Heat Pump
2018 SEER 13
Evaporative Central AC
Cooling/Heating
1st Standard (relative to today's standard)
2nd Standard (relative to today's standard)
Conventional (355kWh/yr)
Conventional (MEF 1.26 for top loader)
14% more efficient (307 kWh/yr)
MEF 1.72 for top loader
Conventional (EF 3.01)
MEF 2.0 for top loader
5% more efficient (EF 3.17)
Source: AEG-Kentucky Power Market Potential Study Kickoff
The impact of emerging codes and standards on SWEPCO’s load forecast can be seen in
Table 3-6. Over the planning period codes and standards are forecasted to reduce retail load by
more than 9.0%.
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DRAFT 2015 Integrated Resource Plan
Table 3-6, Codes & Standards Impact on Retail Load
Impact of Federal Codes & Standards on Forecasted Retail Load
30,000
10.0%
9.0%
25,000
8.0%
7.0%
20,000
6.0%
G
W 15,000
H
5.0%
4.0%
Codes & Standards Energy Savings
10,000
5,000
Retail Load
3.0%
Federal Codes & Standards % of Retail
2.0%
1.0%
‐
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
0.0%
Louisiana has initiated an energy efficiency program and the “Quick Start Phase” began
November 1, 2014. The Arkansas Public Service Commission (APSC) mandated the attainment
of 0.25%, 0.50%, and 0.75% annual energy efficiency savings utilizing a 2010 retail sales basis
in the years 2011, 2012 and 2013, respectively. The 0.75% attainment goal utilizing a 2013
retail sales basis was extended for 2014, and the APSC has tentatively established a 0.90%
attainment goal utilizing a 2013 retail sales basis for 2015 contingent upon the outcome of a
statewide energy efficiency potential study that was ordered in 2014. Texas’ state energy
efficiency program requires the reduction of 25% of its relative annual growth in peak demand or
the previous year’s requirement, whichever is greater, increasing to 30% in 2013.
This IRP considers attainment of these levels and the subsequent continuation of the
program at the same level as the most likely or “base case” and again, has embedded such levels
of energy efficiency savings into SWEPCO’s load forecast.
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DRAFT 2015 Integrated Resource Plan
3.6.3 Current DR/EE Programs
Table 3-7 summarized the recent level of SWEPCO DR and EE program activity in its
states. SWEPCO currently operates energy efficiency in all three service territories as well as
load management (demand reduction) programs in its Texas and Arkansas service territories. All
states have approved rate-design programs to promote energy efficiency programs. Since 2008,
SWEPCO has installed energy efficiency measures that reduced peak demand in 2013 by 24
MWs and reduced 2013 energy consumption by 47 GWh. Interruptible and load management
programs add an additional 71 MW of peak demand reduction capability
Table 3-7: Historical SWEPCO DR/EE Installed Resources
SWEPCO 2008 ‐ 2013 Installed Demand‐Side Resources
Energy Reductions Actuals
SWEPCO EE (GWh) Total
2008
20.5
2009
25.0
GWh
2010
2011
29.7 34.8
2012
44.2
2013
46.8
2008
9.2
‐
‐
9.2
2009
10.5
31.0
‐
41.5
MW
2010
2011
23.4 23.2
20.1 ‐
41.0 11.6
84.4 34.8
2012
24.0
43.9
16.8
84.7
2013
24.1
54.2
16.6
94.9
Demand Reductions Actuals (MW)
SWEPCO EE (MW) Total
SWEPCO Interruptible (MW)Total
SWEPCO DLC/ELM (MW) Total
Total Peak Demand Reduction
3.6.4 Demand Reduction
Peak demand, measured in megawatts (MW), can be thought of as the amount of power
used at the time of maximum power usage. SWEPCO’s maximum (system peak) is likely to
occur on the hottest summer weekday of the year, in the late afternoon. This happens as a result
of the near-simultaneous use of air conditioning by the majority of customers, as well as the
normal use of other appliances and (industrial) machinery. At all other times during the day, and
throughout the year, the use of power is less.
As peak demand grows with the economy and population, new capacity must ultimately be
built. To defer construction of new power plants, the amount of power consumed at the peak
must be reduced. This can be addressed several ways via both “active” and “passive” measures:
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DRAFT 2015 Integrated Resource Plan

Interruptible loads (Active DR). This refers to a contractual agreement between the
utility and a large consumer of power, typically an industrial customer. In return for
reduced rates, an industrial customer allows the utility to “interrupt” or reduce power
consumption during peak periods, freeing up that capacity for use by other consumers.

Direct load control (Active DR). Very much like an (industrial) interruptible load, but
accomplished with many more, smaller, individual loads. Commercial and residential
customers, in exchange for monthly credits or payments, allow the energy manager to
deactivate or cycle discrete appliances, typically air conditioners, hot water heaters,
lighting banks, or pool pumps during periods of peak demand. These power
interruptions can be accomplished through radio signals that activate switches or
through a digital “smart” meter that allows activation of thermostats and other control
devices.

Time-differentiated rates (Active DR). This offers customers different rates for power at
different times during the year and even the day. During periods of peak demand,
power would be relatively more expensive, encouraging conservation. Rates can be
split into as few as two rates (peak and off-peak) and to as often as 15-minute
increments in what is known as “real-time pricing.” Accomplishing real-time pricing
requires digital (smart) metering.

Energy Efficiency measures (Passive DR). If the appliances that are in use during peak
periods use less energy to accomplish the same task, peak energy requirements will
likewise be less.

Line loss mitigation (Passive DR). A line loss results during the transmission and
distribution of power from the generating plant to the end user. To the extent that these
losses can be reduced, less energy is required from the generator.
What may be apparent is that, with the exception of Energy Efficiency and Line Loss
measures, the remaining “demand response” programs do not significantly reduce the amount of
power consumed by customers. Less power may be consumed at the time of peak load, but that
power will be consumed at some point during the day. For example, if rates encourage someone
to avoid running their clothes dryer at four P.M.; they will run it at some other point in the day.
This is often referred to as load shifting.
3.6.4.1 Demand Reduction -- Base Amounts
Demand reduction is included in the base load forecast and this process is discussed in
Section 2.
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DRAFT 2015 Integrated Resource Plan
3.6.3 Energy Efficiency
EE measures save money for customers billed on a per kilowatt-hour usage basis. The
trade-off is the reduced utility bill for any up-front investment in a building/appliance/equipment
modification, upgrade, or new technology. If the consumer feels that the new technology is a
viable substitute and will pay him back in the form of reduced bills over an acceptable period, he
will adopt it.
EE measures most commonly include efficient lighting, weatherization, efficient pumps and
motors, efficient HVAC infrastructure, and efficient appliances. Often, multiple measures are
bundled into a single program that might be offered to either residential or commercial/industrial
customers.
EE measures will reduce the amount of energy consumed but may have limited
effectiveness at the time of peak demand. Energy Efficiency is viewed as a readily deployable,
relatively low cost, and clean energy resource that provides many benefits. According to a
March 2007 DOE study such benefits include:

Economics: Reduced energy intensity provides competitive advantage and frees
economic resources for investment in non-energy goods and services

Environment: Saving energy reduces air pollution, the degradation of natural
resources, risks to public health and global climate change.

Infrastructure: Lower demand lessens constraints and congestion on the electric
transmission and distribution systems

Security: Energy Efficiency can lessen our vulnerability to events that cut off
energy supplies
However, as summarized in Table 3-8, market barriers to Energy Efficiency exist for the
potential participant.
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DRAFT 2015 Integrated Resource Plan
Table 3-8 Energy Efficiency Market Barriers
Market Barriers to Energy Efficiency
High First Costs
Energy-efficient equipment and services are often considered “high-end”
products and can be more costly than standard products, even if they save
consumers money in the long run.
High Information or It can take valuable time to research and locate energy efficient products
Search Costs
or services.
Consumer Education
Consumers may not be aware of energy efficiency options or may not
consider lifetime energy savings when comparing products.
Performance
Uncertainties
Evaluating the claims and verifying the value of benefits to be paid in the
future can be difficult.
Transaction Costs
Additional effort may be needed to contract for energy efficiency services
or products.
Access to Financing
Lending industry has difficulty in factoring in future economic savings as
available capital when evaluating credit-worthiness.
Split Incentives
The person investing in the energy efficiency measure may be different
from those benefiting from the investment (e.g., rental property)
Product/Service
unavailability
Energy-efficient products may not be available or stocked at the same
levels as standard products.
Externalities
The environmental and other societal costs of operating less efficient
products are not accounted for in product pricing or in future savings
Source: Eto, Goldman, and Nadel (1998): Eto, Prahl, and Schlegel (1996); and Golove and Eto (1996)
To overcome many of the participant barriers noted above, a portfolio of programs may often
include several of the following elements:

Consumer education

Technical training

Energy audits

Rebates and discounts for efficient appliances, equipment and buildings

Industrial process improvements
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DRAFT 2015 Integrated Resource Plan
The level of incentives (rebates or discounts) offered to participants is a major determinant
in the pace of market transformation and measure adoption.
Additionally, the speed with which programs can be rolled out also varies with the
jurisdictional differences in stakeholder and regulatory review processes. The lead time can
easily exceed a year for getting programs implemented or modified. This IRP begins adding new
demand-side resources in 2017 that are incremental to approved or mandated programs.
3.6.3.1 Energy Conservation
Often used interchangeably with efficiency, conservation results from foregoing the benefit
of electricity either to save money or simply to reduce the impact of generating electricity.
Higher rates for electricity typically result in lower consumption. Inclining block rates, or rates
that increase with usage, are rates that encourage conservation.
3.6.4 Smart Grid Technologies and Opportunities
3.6.4.1 Distributed Generation
Distributed generation (DG) typically refers to small scale customer-sited generation
downstream of the customer meter. Common examples are combined heat and power (CHP),
residential and small commercial solar applications, and even wind. Currently, these sources
represent a small component of demand-side resources; even with available Federal tax credits
and the implementation of Louisiana’s residential rooftop solar rebate program.
All three SWEPCO retail jurisdictions do have “net metering” tariffs in place which allow
for the sale of power generated by customers to be purchased by the utility at the customers’
(retail) rate. Most power generated in this manner is consumed “on-site” and the net power
available to be fed back into the grid for system use is negligible.
The economics of distributed generation, particularly solar, continue to improve. Figure 3-1
charts the fairly rapid decline of expected installed solar costs, based on a combination of AEP
market intelligence and the Bloomberg New Energy Finance’s (BNEF) Installed Cost of Solar
forecast. These are costs shown without accounting for the 30% Federal investment tax credit
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DRAFT 2015 Integrated Resource Plan
(ITC) (reduced to a 10% credit in 2016) as well as Louisiana’s rooftop solar rebate program,
which would further reduce the installed cost.
Figure 3-1 Forecasted Solar Installed Costs (Excl. Fed & State Incentives)
Installed Solar Cost
6.00
5.50
5.00
Utility Cost
$/Watt(ac)
4.50
Commercial
4.00
Residential
3.50
3.00
2.50
2.00
1.50
1.00
2014
2019
2024
2029
2034
2039
Not surprisingly, the declining cost of solar and the associated Louisiana residential rooftop
solar rebate program has accelerated the installation of rooftop solar within Louisiana. As
illustrated in Figure 3-2, from the Residential Customer perspective, upon consideration of the
current net-metering arrangement, the estimated cost to install rooftop solar, as well as the
current federal and state incentives, it may provide a Louisiana customer considerable incentive
to install rooftop solar in the nearer-term. However, when the Louisiana Residential rooftop
solar incentive cap is met at 7.8 MWs of rooftop solar and the federal ITC is reduced, the relative
value proposition will likely be reduced considerably for Louisiana retail customers. That is, the
cost to install and the value (i.e. avoided costs) received are projected to become very close.
Figure 3-2, further illustrates, by SWEPCO state jurisdictional residential sector, the equivalent
value a customer would achieve on a $/Watt AC basis over the assumed 30 year life of the
installed solar panels based on the customers’ avoided retail rate.
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DRAFT 2015 Integrated Resource Plan
Figure 3-2 Distributed Solar Customer Breakeven Costs
6.00
Distributed Solar Grid Parity ‐ Residential Sector
5.50
SWEPCO‐Arkansas
ITC is reduced from 30% to 10% year‐end 2016
5.00
SWEPCO‐Louisiana
SWEPCO‐Texas
Installed Cost $/W ‐ AC
4.50
2015 Proj. Installed Cost SWEPCO ‐ Bid &
BNEF
4.00
Louisiana Rooftop incentive ends or when Cap is met ~7.8MW
3.50
3.00
2.50
2.00
1.50
Grid Parity is very sensitive to discount rate assumption
8% WACC assumption, 30‐yr panel life
1.00
2014
2015
2016
2017
2018
2019
Figure 3-3, demonstrates the historical installed rooftop solar capacity for SWEPCO by
jurisdiction and the projected rooftop solar capacity additions that are included in the Preferred
Plan.
Figure 3-3 Cumulative Rooftop Solar Additions/Projections
SWEPCO Cumulative Rooftop Solar Additions
50
40
7.8 MW LPSC Incentive Cap
30
20
10
0
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Installed Cap (MWac)
60
SWP LA
SWP AR
SWP TX
SWP Total
SWP LA
SWP AR
SWP TX
SWP Total
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DRAFT 2015 Integrated Resource Plan
The current distributed resources net metering cap for SWEPCO Louisiana is 7.8MW and
based on current projections SWEPCO Louisiana will meet this cap in 2016. The assumed
growth rate for rooftop solar is 5% per year after SWEPCO Louisiana reaches the cap. The
assumed growth rate is an estimate and is based on both the declining cost for rooftop solar as
well as the historical additions by SWEPCO state jurisdiction.
3.6.4.3 Volt VAR Optimization (VVO)
An emerging technology known as VVO represents a form of voltage control that allows
the grid to operate more efficiently. Depicted at a high-level in Figure 3-4, with VVO, sensors
and intelligent controllers monitor load flow characteristics and direct controls on capacitor and
voltage regulating equipment to optimize power factor and voltage levels.
Power factor
optimization also improves energy efficiency by reducing losses on the system. VVO enables
conservation voltage reduction (CVR) on a utility’s system. CVR is a process by which the
utility systematically reduces voltages in its distribution network, resulting in a proportional
reduction of load on the network. Voltage optimization can allow a reduction of system voltage
that still maintains minimum levels needed by customers, thereby allowing customers to use less
energy without any changes in behavior or appliance efficiencies. Early results from limited
rollouts in AEP affiliate operating companies indicate a range of 0.7% to 1.2% of energy demand
reduction for a 1% voltage reduction is possible.
Figure 3-4: Voltage/VAR Optimization
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DRAFT 2015 Integrated Resource Plan
While there is no “embedded” VVO load reduction impacts implicit in the base load
forecast case, VVO has been modeled as a unique energy efficiency resource. The results of
which are discussed in Section 4.
3.7 AEP-SPP Transmission
3.7.1 Transmission System Overview
The portion of the AEP Transmission System operating in SPP (AEP-SPP zone) consists of
approximately 1300 miles of 345 kV, approximately 3600 miles of 138 kV, and approximately
2300 miles of 69 kV. The AEP-SPP zone is also integrated with and directly connected to ten
other companies at 89 interconnection points, of which 71 are at or above 69 kV and to ERCOT
via two high voltage direct current (HVDC) ties. These interconnections provide an electric
pathway to provide access to off-system resources, as well as a delivery mechanism to
neighboring systems.
3.7.2 Current AEP-SPP Transmission System Issues
The limited capacity of interconnections between SPP and neighboring systems, as well as
the electrical topology of the SPP footprint transmission system, influences the ability to deliver
non-affiliate generation, both within and external to the SPP footprint, to AEP-SPP loads and
from sources within AEP-SPP balancing authority to serve AEP-SPP loads. Moreover, a lack of
seams agreements between SPP and its neighbors has significantly slowed down the process of
developing new interconnections.
Despite the robust nature of the AEP-SPP transmission
system as originally designed, its current use is in a different manner than originally designed, in
order to meet SPP RTO requirements, which can stress the system. In addition, factors such as
outages, extreme weather, and power transfers also stress the system. This has resulted in a
transmission system in the AEP-SPP zone that is constrained when generation is dispatched in a
manner substantially different from the original design of utilizing local generation to serve local
load. SPP uses models developed from data provided by all load serving entities to study the
reliability needs of the SPP footprint.
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DRAFT 2015 Integrated Resource Plan
3.7.2.1 The SPP Transmission Planning Process
Currently, SPP produces an annual SPP Transmission Expansion Plan (STEP). The STEP is
developed through an open stakeholder process with AEP participation.
SPP studies the
transmission system, checking for base case and contingency overload and voltage violations in
SPP base case load flow models, plus models which include power transfers.
The 2014 STEP summarizes 2013 activities, including expansion planning and long-term
SPP Open Access Transmission Tariff studies (Tariff Studies) that impact future development of
the SPP transmission grid. Seven key topics are included in the STEP:
1) Integrated Transmission Planning
2) Tariff Studies,
3) Sub-regional and local area planning,
4) Transmission Congestion and top Flowgates
5) Interregional coordination,
6) Project tracking; and
7) Public Policy Impacts
These topics are critical to meeting mandates of either the SPP strategic plan or the nine
planning principles in FERC Order 890. As a RTO under the domain of the Federal Energy
Regulatory Commission (FERC), SPP must meet FERC requirements and the SPP Open Access
Transmission Tariff (OATT or Tariff). The SPP RTO acts independently of any single market
participant or class of participants. It has sufficient scope and configuration to maintain electric
reliability, effectively perform its functions, and support efficient and non-discriminatory power
markets.
Regarding short-term reliability, the SPP RTO has the capability and exclusive
authority to receive, confirm, and implement all interchange schedules. It also has operational
authority for all transmission facilities under its control. The 10-year RTO regional reliability
assessment continues to be a primary focus.
STEP projects are categorized by the following designations:

Balanced Portfolio – Projects identified through the Balanced Portfolio Process;
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DRAFT 2015 Integrated Resource Plan

Generation Interconnect – Projects associated with a FERC-filed Interconnection
Agreement;

Interregional – Projects developed with neighboring Transmission Providers;

ITP – Projects needed to meet regional reliability, economic, or policy needs in the
ITP study process;

ITP – Non-OATT – Projects to maintain reliability for SPP members not
participating under the SPP OATT

Transmission service – Projects associated with a FERC-filed Service Agreement;

Zonal Reliability – Projects identified to meet more stringent local Transmission
Owner criteria; and

Zonal-Sponsored – Projects sponsored by facility owner with no Project Sponsor
Agreement
The 2014 STEP identified 386 transmission network upgrades with a total cost of
approximately $6.2 billion. At the heart of SPP’s STEP process is its Integrated Transmission
Planning (ITP) process, which represented approximately two thirds of the total cost in the 2014
STEP. The ITP process was designed to maintain reliability and provide economic benefits to
the SPP region in both the near and long-term. The ITP process was conducted in three phases.
The first phase recommended a long-term transmission plan for a 20-year horizon, incorporating
a proposed extra-high voltage supply system. The second phase of the ITP process resulted in a
recommended portfolio of transmission projects for comprehensive regional solutions, local
reliability upgrades, and the expected reliability and economic needs of a 10-year horizon.
Finally, the third phase of the ITP process studied the reliability of the SPP transmission system
in the near-term, identifying upgrades requiring expenditures within the next four years.
3.7.2.2 PSO-SWEPCO Interchange Capability
Operational experience and internal assessments of company transmission capabilities
indicate that, when considering a single contingency outage event, the present firm capability
transfer limit from Public Service Oklahoma (PSO) to SWEPCO and from SWEPCO to PSO is
about 200 MW. As much as 900 MW from PSO to SWEPCO and 700 MW from SWEPCO to
PSO may be available for economical energy transfers when no transmission facilities are out of
service. However, the intra-company available transmission capability between the two
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DRAFT 2015 Integrated Resource Plan
companies is available to all transmission users under the provisions established by FERC Order
888 and subsequent orders. Thus, there is some question as to whether, in the future, as SPP
grants further transmission rights, any transfer capability will in fact be available without further
upgrades to the transmission system.
As previously indicated, each company’s generation capacity additions are planned so that
each meets its own reserve requirement over the long-term. Any capacity transfers (i.e. “reserve
sharing”) should be considered for short time frames only. Specifically, the practice has been
that, as the last step of the planning process, the respective PSO and SWEPCO expansion plans
are adjusted to take advantage of any surplus of one company that might match a potential deficit
of the other, and thereby delay some of the identified new capacity. Because of the sizes,
demand growth rates, and peak coincidence of the two companies, it rarely appears that either
company would ever have more than 200 MW of surplus capacity in any year that could be
transferred to the other company.
3.7.2.3 AEP-SPP Import Capability
Currently the capability of the transmission system to accommodate large incremental firm
imports to the AEP-SPP area is limited. Generally, the transfers are limited by the facilities of
neighboring systems rather than by transmission lines or equipment owned by AEP.
Increasing the import capabilities with AEP-SPP’s neighboring companies could require a
large capital investment for new transmission facilities by the neighboring systems or through
sponsored upgrades by SPP transmission owners. An analysis of the cost of the upgrades cannot
be performed until the capacity resources are determined. For identified resources, the cost of
any transmission upgrades necessary on AEP’s transmission system can be estimated by AEP
once SPP has identified the upgrade. AEP’s West Transmission Planning group can identify
constraints on third-party systems through ad hoc power flow modeling studies, but West
Transmission Planning does not have information to provide estimates of the costs to alleviate
those third-party constraints.
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DRAFT 2015 Integrated Resource Plan
3.7.2.4 SPP Studies that may Provide Import Capability
Within the STEP, some projects that may lead to improved transfer capability between
AEP-SPP and neighboring companies and regions include:

A Chisholm-Gracemont 345 kV line across western Oklahoma from a new
Chisholm 345-230 kV station west of Elk City to Gracemont station near
Anadarko

A new Layfield 500-230 kV station in northwestern Louisiana (previously
referred to as Messick)

A Sooner-Cleveland 345 kV line in northern Oklahoma, west of the Tulsa
area (completed)

A Seminole-Muskogee 345 kV line in eastern Oklahoma (completed)

A Sunnyside-Hugo-Valliant 345 kV line across southeastern Oklahoma
(completed)

A Tuco-Woodward 345 kV line from the Texas Panhandle to northwestern
Oklahoma (nearing completion)
Besides the annual STEP process, SPP also performs other special studies or area studies on
an as needed basis. One SPP study that resulted in approved projects that may lead to improved
transfer capability between AEP-SPP and neighboring companies and regions is the Priority
Projects study. Among the projects approved as a result of this study are:

Double circuit 345 kV line in southwestern Kansas from Spearville to Clark
County to Thistle to Wichita (The Thistle to Wichita portion has been
completed.)

Double circuit 345 kV line from southwestern Kansas (Medicine Lodge) to
northwestern Oklahoma (Woodward)

Double circuit 345 kV line in northwestern Oklahoma from Woodward to
Hitchland (completed)

A 345 kV line from Valliant, in southeastern Oklahoma to Northwest
Texarkana in northeastern Texas
3.7.3 Recent AEP-SPP Bulk Transmission Improvements
Over the past several years, there have been several major transmission enhancements
initiated to reinforce the AEP-SPP transmission system. These enhancements include:
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DRAFT 2015 Integrated Resource Plan

Northwest Arkansas—The AEP Transmission System serves approximately
1,300 MW of load in the Northwest Arkansas area, about 49% of which is
AECC load. This load is supplied primarily by the SWEPCO and AECC jointlyowned Flint Creek generating plant, the SWEPCO Mattison generating plant, the
GRDA-Flint Creek 345 kV line, and the Clarksville-Chamber Springs 345 kV
line. Wal-Mart’s international headquarters and its supplying businesses’ offices
and Tyson’s headquarters are all located in this area. A new 345 kV line has
been completed from Flint Creek to the new Shipe Road 345/161 kV substation
along with a 161 kV line connecting Shipe Road substation to East Centerton
substation.

Port of Shreveport (Port), Louisiana— A 138 kV loop was completed in 2012
around the Port to increase system reliability and to serve the increasing area
load. This loop extends approximately 33 miles from Wallace Lake Station to
the Port to Bean Station to Caplis Station to McDade Station to Haughton
Station to Red Point Station. In order to serve a new industrial customer,
Benteler Steel/Tube, two 138 kV lines of approximately three to four miles each,
are being built from the Port to the Benteler Steel/Tube plant.

Turk Generation Interconnection – In order to connect the 600 MW coal-fired
Turk Power Plant in southwestern Arkansas, near McNab, to the transmission
system, the Turk 345/138/115 kV substation was built and several new
transmission lines were built or upgraded. A 345 kV line approximately 33
miles from Turk to Northwest Texarkana substation, a 138 kV line
approximately 22 miles from Turk to Sugar Hill substation, and a 138 kV line
approximately 27 miles from Turk to Southeast Texarkana substation have been
completed as well as a 138 kV line section and a 115 kV line section
approximately 2 miles each from Turk to the Okay-Hope 115 kV line, which
was opened and routed into Turk. The Patterson to Okay and Okay to Hope 115
kV lines were rebuilt to 138 kV standards, though the portion between Hope and
Turk will continue to be operated at 115 kV. This expansion provides the
interconnection of the Turk Power Plant, transmission service, improved
reliability for the City of Hope and southwestern Arkansas, and improved
reliability to Texarkana by completing a 138 kV loop around the city.

McAlester, Oklahoma area – The Canadian River 345-138 kV substation has
been completed northwest of McAlester, along OG&E’s Pittsburg-Muskogee
345 kV line. A 69 kV line was converted to 138 kV line for approximately 17
miles from the Canadian River substation to the McAlester City substation. This
will relieve 138 kV line loadings in the area and provide voltage support.
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DRAFT 2015 Integrated Resource Plan
These major enhancements are in addition to several completed or initiated upgrades to 138
kV and 69 kV transmission lines to reinforce the AEP-SPP transmission system.
3.7.4 Impacts of New Generation
Integration of additional generation capacity within the AEP-SPP zone will likely require
significant transmission upgrades. At most locations, any additional generation resources will
aggravate existing transmission constraints. Specifically:

Western Oklahoma/Texas Panhandle— Until recently there were very few
Extra High Voltage (EHV) transmission lines in this area, though that is
changing due to the 345 kV projects discussed above. The area is one of the
highest wind density areas within the SPP RTO footprint. The potential wind
farm capacity for this area has been estimated to exceed 4,000 MW. Several
wind farms have already been built, and several more are in the development
stages. Wind generation additions in the SPP footprint in this region will likely
require significant transmission enhancements, including EHV line and station
construction, to address thermal, voltage, and stability constraints.

PSO/SWEPCO Interface - There is one 345 kV EHV line linking PSO’s
service area with the majority of SWEPCO’s generation resources in its service
area. An SPP approved project, mentioned above, to build a 345 kV line
approximately 76 miles from Valliant substation to Northwest Texarkana
substation will improve transfer capability by forming a second 345 kV path
between PSO and SWEPCO’s transmission system in northeastern Texas.
Significant generation additions to the AEP-SPP transmission facilities (or
connection to neighbor’s facilities) may require significant transmission
enhancements, possibly including EHV line and station construction, to address
thermal, voltage, and stability constraints.

Tulsa Metro Area—the Tulsa metro area load is supplied primarily by the PSO
Northeastern, Riverside, and Tulsa Power Station generating plants.
Additionally, Oklahoma Gas & Electric Company has large generation plants
located to the southeast and southwest of Tulsa, and there are large merchant
plants just east and south of Tulsa. The Grand River Dam Authority has a large
plant located to the east of Tulsa. Generation additions in the Tulsa area would
likely require significant enhancements in the EHV and sub-transmission system
to address thermal, voltage and stability constraints.

SPP Eastern Interface—there are only five east-west EHV lines into the SPP
region, which stretches from the Gulf of Mexico (east of Houston) north to Des
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DRAFT 2015 Integrated Resource Plan
Moines, Iowa. This limitation constrains the amount of imports and exports
along the eastern interface of SPP with neighboring regions. It also constrains
the amount of transfers from the capacity rich western SPP region to the market
hubs east and north of the SPP RTO region. Significant generation additions
near or along the SPP eastern interface would likely require significant
transmission enhancements, including EHV line and station construction, to
address thermal and stability constraints should such generation additions
adversely impact existing transactions along the interface.
Integration of generation resources at any location within the AEP-SPP zone will require
significant analysis by SPP to identify potential thermal, short circuit, and stability constraints
resulting from the addition of generation. Depending on the specific location, EHV line and
station construction, in addition to connection facilities, could be necessary. Other station
enhancements, including transformer additions and breaker replacements may be necessary.
Some of the required transmission upgrades could be reduced or increased in scope if existing
generating capacity is retired concurrent with the addition of new capacity. For example, if
SWEPCO’s Flint Creek Generating Plant were to have been retired, rather than retrofitted with
environmental controls (for which SWEPCO received approval from the APSC in Docket No.
12-008-U), SWEPCO’s transmission system would have required significant upgrades to support
the delivery of power from remote generating plants, provide transfer capability, and supply
reactive power for voltage support into that northwest Arkansas load pocket.
3.7.5 Summary of Transmission Overview
In the SPP region, the process of truly integrating Generation and Transmission planning is
still developing. AEP continues to stand ready to engage in that process. AEP also continues
supporting the SPP STEP and ITP transmission expansion processes, which include some
projects which may improve import capability. Such capability improvements are more likely to
be within SPP, but less so between SPP and neighboring regions, partly due to lack of seams
agreements which slows the development of new interconnections as discussed above. PSO and
SWEPCO have been open to imports from other control areas as evidenced by the issuing of
recent Request for Proposals (RFPs) for non-site specific generation types. Such RFP
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DRAFT 2015 Integrated Resource Plan
solicitations allow bidding entities to offer generation coupled with transmission solutions, which
would be subject to SPP approvals.
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4.0 Modeling Parameters
4.1 Modeling and Planning Process – An Overview
The objective of a resource planning effort is to recommend a system resource expansion
plan that balances “least-cost” objectives with planning flexibility, asset mix considerations,
adaptability to risk, conformance with applicable North American Electric Reliability
Corporation (NERC) and RTO criteria. In addition, given the unique impact of fossil-fired
generation on the environment, the planning effort must ultimately be in concert with anticipated
long-term requirements as established by the EPA-driven environmental compliance planning
process.
The information presented with this IRP includes descriptions of assumptions, study
parameters, methodologies, and results including the integration of supply-side resources and
DSM programs.
In general, assumptions and plans are continually reviewed and modified as new
information becomes available. Such continuous analysis is required by multiple disciplines
across SWEPCO and AEP to ensure that market structures and governances, technical
parameters, regulatory constructs, capacity supply, energy adequacy and operational reliability,
and environmental mandate requirements are constantly reassessed to ensure optimal capacity
resource planning.
Further impacting this process are a growing number of federal and state initiatives that
address many issues relating to industry restructuring, customer choice, and reliability planning.
Currently, fulfilling a regulatory obligation to serve native load customers represents one of the
cornerstones of the SWEPCO IRP process. Therefore, as a result, the “objective function” of the
modeling applications utilized in this process is the establishment of the least-cost plan, with cost
being more accurately described as revenue requirement under a traditional ratemaking
construct.
That does not mean, however, that the best or optimal plan is the one with the absolute least
cost over the planning horizon evaluated. Other factors–some more difficult to monetize than
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DRAFT 2015 Integrated Resource Plan
others–were considered in the determination of the plan. To challenge the robustness of the
ultimate Preferred Portfolio, sensitivity analyses were performed to address these factors.
4.2 Methodology
The IRP process aims to address the long-term “gap” between resource needs and current
resources. Given the various assets and resources that can satisfy this expected long-term gap, a
tool is needed to sort through the myriad of potential combinations and return an optimum
solution–or portfolio–subject to constraints. Plexos® is the primary modeling application, used
by SWEPCO and AEP for identifying and ranking portfolios that address the gap between needs
and current available resources.9 Given the cost and performance parameters around sets of
potentially-available proxy resources–both supply and demand side–and a scenario of economic
conditions that include long-term fuel prices, capacity costs, energy costs, emission-based
pricing proxies including CO2, as well as projections of energy usage and peak demand, Plexos®
will return the optimal suite of proxy resources (portfolio) that meet the resource need.
Portfolios created under similar pricing scenarios may be ranked on the basis of cost, or the
“cumulative present worth” (CPW), of the resulting stream of revenue requirements. The least
cost option is considered the “optimum” portfolio for that unique input parameter scenario.
4.3 Fundamental Modeling Input Parameters
The AEP Fundamental Analysis group derives long-term power (energy) price forecasts
from a proprietary model known as AURORAxmp. Having similarities to Plexos®, AURORAxmp
is a long-term fundamental production cost-based energy and capacity price forecasting tool
developed by EPIS, Inc., that is driven by comprehensive, user-defined commodity input
parameters. For example, nearer-term unit-specific fuel delivery and emission allowance price
forecasts which are established by AEP Fundamental Analysis and AEP Fuel, Emissions and
Logistics (FEL), are fed into AURORAxmp. Estimates of longer-term natural gas and coal pricing
are provided by AEP Fundamental Analysis in conjunction with input received from outside
consulting services. Similarly, capital costs and performance parameters for various new-build
9
Plexos® is a production cost-based resource optimization model, which was developed and supported by Energy
Exemplar, LLC. The Plexos® model is currently licensed for use in 37 countries throughout the world.
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DRAFT 2015 Integrated Resource Plan
generating options, by duty-type are vetted through AEP Engineering Services and incorporated
into the tool. Other information specific to the thousands of generating units being modeled is
researched from “Velocity Suite,” an on-line information database maintained by Ventyx, an
ABB Company. This includes data such as unit capacity, heat rates, retirement dates and
emission controls status. Finally, the model maintains and determines region-specific resource
adequacy based on regional load estimates provided by AEP Economic Forecasting, as well as
current regional reserve margin criterion. AEP uses AURORAxmp to model long-term (market)
energy and capacity prices for the entire U.S. eastern synchronous interconnect as well as
ERCOT. The projection of a CO2 pricing proxy is based on assumptions developed in
conjunction with the AEP Strategic Policy Analysis organization.
Figure 4-1 shows the
Fundamentals process flow for solution of the long-term (power) commodity forecast. The input
assumptions are initially used to generate the output report. The output is used as “feedback” to
change the base input assumptions. This iterative process is repeated until the output is congruent
with the input assumptions (e.g., level of natural gas consumption is suitable for the established
price and all emission constraints are met).
Figure 4-1: Long-term Power Price Forecast Process Flow
Input
Fuel Forecast
Load Forecast
Output
Longterm Capacity
Expansion
Annual Dispatch
Emissions Forecast
Capital Cost Forecast
Emission Retrofits
Recycle
67
Generate Report
Emission Totals
Fuel Burn Totals
Market Prices
DRAFT 2015 Integrated Resource Plan
4.3.1 Commodity Pricing Scenarios
Five commodity pricing “scenarios” were developed by AEP Fundamental Analysis for
SWEPCO to enable Plexos® to construct resource plans under various long-term pricing
conditions. In this report, the five distinct long-term commodity pricing scenarios that were
developed for Plexos® are: a “Base” scenario view, a plausible “Lower Band” view, a plausible
“Higher Band” View; a “High CO2” view; and a “No Carbon” view. The scenarios are described
below with the results shown in Figure 4-2.
4.3.1.1“Base” Scenario
This scenario recognizes the following major assumptions:



MATS Rule effective beginning in 2015;
Initially lower natural gas price due to the emergence of shale gas plays; and
CO2 emission pricing proxy begins in 2022 and was assumed to be at $15 per
metric ton, growing with inflation.
The specific effect of the MATS Rule are modeled in the development of the long-term
commodity forecast by retiring the smaller, older solid-fuel (i.e., coal and lignite) units which
would not be economic to retrofit with emission control equipment. The retirement time frame
modeled is 2015 through 2017. Those remaining solid-fuel generating units will have some
combination of controls necessary to comply with the EPA’s rules. Incremental regional capacity
and reserve requirements will largely be addressed with new natural gas plants. One effect of the
expected retirements or the emission control retrofit scenario is an over-compliance of the
previous CSAPR emission limits. This will drive the emission allowance prices for SO2 and NOX
to zero by 2018 or 2019.
4.3.1.2 “Lower Band” Scenario
This scenario is best viewed as a plausible lower natural gas/solid-fuel/energy price profile
compared to the Base view. In the near term, Lower Band natural gas prices largely track the
Base but, in the longer term, natural gas prices represent an even more significant infusion of
shale gas. From a statistical perspective, this long-term pricing scenario is approximately one
(negative) standard deviation (-1.0 SD) from the Base Case and illustrates the effects of coal-to-
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DRAFT 2015 Integrated Resource Plan
gas substitution at plausibly lower gas prices. Like the Base Case scenario, proxied CO2
mitigation/pricing is assumed to start in 2022 at a $15 per metric ton (real dollars).
4.3.1.3 “Higher Band” Scenario
Alternatively, this Higher Band scenario offers a plausible, higher natural gas/solidfuel/energy price profile compared to the Base scenario. Higher Band natural gas prices reflect
certain impediments to shale gas developments including stalled technological advances (drilling
and completion techniques) and as yet unseen environmental costs. The pace of environmental
regulation implementation is in line with Fleet Transition and Lower Band. Analogous to the
Lower Band scenario, this Higher Band view, from a statistical perspective, is approximately,
one (positive) standard deviation (+1.0 SD) from the Base. Also, like the Base and Lower Band
scenarios, CO2 pricing is assumed to begin in 2022 at the same $15 per metric ton pricing proxy.
4.3.1.4 “High CO2” Scenario
Built upon the assumption of a 66% higher, or $25 per metric ton CO2 mitigation pricing
proxy beginning in 2022, the High CO2 scenario includes correlative price adjustments to natural
gas and solid-fuel due to changes in consumption that such heightened CO2 pricing levels would
create. This results in some additional retirements of coal-fired generating units around the
implementation period. Natural gas and, to a lesser degree, renewable generation is built as
replacement capacity.
4.3.1.5 “No CO2” Scenario
This “business as usual” scenario does not consider the prospects of a carbon tax. While also
including the necessary correlative fuel price adjustments, it serves as a baseline to understand
the impact on unit dispatch and, with that, the attendant impact on energy prices associated with
the Base (CO2) and High CO2 scenarios.
The following sets of Figure 4-2 charts illustrate the range of such long-term pricing
projections---on a nominal dollar basis---, by major commodity through the year 2030.
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DRAFT 2015 Integrated Resource Plan
Figure 4-2: Commodity Prices
Power On‐Peak SPP Price ($/MWh)
120.0
100.0
80.0
60.0
40.0
20.0
0.0
2013
2015
2017
2019
Base
High Carbon
2021
2023
2025
Lower band
No Carbon
2027
2029
Higher Band
Power Off‐Peak SPP Price ($/MWh)
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
2013
2015
2017
2019
Base
High Carbon
2021
2023
2025
2027
Lower band
No Carbon
2029
Higher Band
CO2 Price ($/tonne)
30.0
25.0
20.0
15.0
10.0
5.0
0.0
2013
Base
2015
2017
2019
Lower band
2021
2023
Higher Band
70
2025
2027
High Carbon
2029
No Carbon
DRAFT 2015 Integrated Resource Plan
Henry Hub Gas Price ‐ Nominal ($/mmBtu)
12.0
10.0
8.0
6.0
4.0
2.0
0.0
2013
2015
2017
Base
High Carbon
2019
2021
2023
Lower band
No Carbon
2025
2027
2029
Higher Band
Henry Hub Gas Price ‐ Real ($/mmBtu)
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
2014
2016
2018
Base
2020
High Carbon
2022
2024
Lower band
2026
2028
2030
Higher Band
No Carbon
Coal (PRB 8800 0.8#) Price $/ton)
25.0
20.0
15.0
10.0
5.0
0.0
2013
Base
2015
2017
Lower band
2019
2021
Higher Band
71
2023
2025
2027
High Carbon
2029
No Carbon
DRAFT 2015 Integrated Resource Plan
4.3.2 Long-Term CO2 Forecast “Proxies”
Each of the pricing forecasts includes a CO2/carbon impact as a result of the
implementation of any prospective carbon-reduction rules or legislation. The “Base”, “Higher
Band” and “Lower Band” long-term pricing scenarios each reflect the fundamental view that a
CO2 penalty could be “proxied” with a $15 per metric ton dispatch cost “burden” applicable to
fossil-fired generating units’ dispatch beginning in 2022. Contrastingly, the “High Carbon”
scenario includes a higher, $25 per metric ton dispatch cost burden which also commences in
2022. Recognizing the relative higher carbon emission from a solid-fuel unit versus a natural
gas-fired unit, the relative Plexos®-modeled impact on variable/dispatch costs was reflected.
Given also that any plan to reduce CO2 emissions must be accompanied by a thorough
assessment of the impact on the electric grid, allow adequate time for implementation, respect
the authority of states and other federal agencies, and preserve a balanced, diverse mix of fuels
for electricity generation, and due, particularly, to the timing of the EPA-proposed CPP
previously discussed – it is simply too early to justify substantive proxied changes to this ($0 –
to- $25 per metric ton) range of CO2/carbon impact on SWEPCO’s relative resource modelling
that would pertain to that rulemaking.
4.3.3 DSM Program Screening & Evaluation Process
4.3.3.1 Overview
The process for evaluating DSM impacts for SWEPCO is practically divided into two
spheres; “existing programs” and “future activity.” Existing programs are those that are known
or are reasonably well-defined, follow a pre-existing process for screening and determining
ultimate regulatory approval.
The impacts of such existing SWEPCO DSM programs are
propagated throughout the long-term SWEPCO load forecast and were discussed in Section 3.
Future program impacts which are, naturally, less-defined, are developed with a dynamic
modeling process using more generic cost and performance parameter data.
For SWEPCO, the potential future DSM activity was developed and ultimately modeled
based on the Electric Power Research Institute’s (EPRI) “2014 U.S. Energy Efficiency Potential
Through 2035” report. This comprehensive report served as the basic underpinning for the
establishment of potential EE “bundles”, developed for residential and commercial customers
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DRAFT 2015 Integrated Resource Plan
that were then introduced as a resource option in the Plexos® optimization model. Industrial
programs were not developed or modeled based on the thought that industrial customers, by and
large, will “self-invest” in energy efficiency measures based upon unique economic merit
irrespective of the existence of utility-sponsored program activity.
4.3.3.2 Technologies Considered But Not Evaluated
Some DG alternatives which include micro-turbines, fuel cells, CHP, and residential and
small commercial wind were not specifically evaluated. However, distributed rooftop solar
generation was modeled as a resource that would be costed by SWEPCO at the (full retail) net
metering rate. This DG alternative is discussed in more detail later in this section.
Currently, the DG technologies listed that were not modeled tend to cost far more than other
utility-scale options and were not considered for wide-scale utility implementation. Their costs
will continue to be monitored. Figure 4-3 shows the significant variation in capital costs for DG
and where the costs are relative to other generating technologies. This charting method shows a
data distribution without making any assumption about the underlying relationship between the
data (unlike, for example, a mean and standard deviation which assumes an underlying
Gaussian). It shows the data distribution using five numbers: The minimum, lowest 25%,
median, highest 25%, and maximum point. Where fewer than three points are available for a
column, individual data points are shown and not a box and whisker chart. The yellow shaded
diamond label is for DOE program estimates and the green shaded diamond label is for other
estimates, where there is insufficient data to show the box and whisker distribution as described
above10.
10
http://www.nrel.gov/analysis/tech_cost_dg.html
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DRAFT 2015 Integrated Resource Plan
Figure 4-3: Alternative Generation Capital Cost Comparison (Including DG)
4.3.3.3 Achievable Potential
The amount of “available” EE is typically described in three buckets: technical potential,
economic potential, and achievable potential. The previously-cited EPRI report breaks down the
achievable potential into “High Achievable” (Higher Cost) and “Achievable” potential. Briefly,
the technical potential encompasses all known efficiency improvements that are possible,
regardless of cost, and thus, whether it’s even cost-effective (i.e., all EE measures would be
adopted if technically feasible). The logical subset of this pool is the economic potential. Most
commonly, the total resource cost test is used to define economic potential. This compares the
avoided cost savings achieved over the life of a measure/program with its cost to implement it,
regardless of who paid for it and regardless of the age and remaining economic life of any
system/equipment that would be replaced (i.e., all EE measures would be adopted if ‘economic’).
The third set of efficiency assets is that which is achievable. As highlighted above, the “High
Achievable” potential is the economic potential discounted for market barriers such as customer
preferences and supply chain maturity; while “Achievable” potential is additionally discounted
for programmatic barriers such as program budgets and execution proficiency.
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DRAFT 2015 Integrated Resource Plan
Of the total ‘Technical’ potential, typically only a fraction is ultimately achievable and only
then over time due to the existence of market barriers. The question of how much effort and
money is to be deployed towards removing or lowering the barriers is a decision made by state
governing bodies (legislatures, regulators or both).
The “Achievable” potential range is typically a fraction of the economic potential range.
This Achievable amount must be further split between what can or should be accomplished with
utility-sponsored programs and what should fall under codes and standards. Both amounts are
represented in this IRP as reductions to what would otherwise be the load forecast. The following
Table 4-1, (also included in Section 3) illustrates the existing and anticipated future end-use
code changes along with the expected implementation date.
Table 4-1, Forecasted View of Relevant Energy Efficiency Code Improvements
Today's Efficiency or Standard Assumption
End Use
Technology
2012 2013 2014 2015 2016 2017 Room AC
Space Heating
Electric Resistance
Water Heating
Lighting
Appliances
2020 2021 2025 SEER 14.0/HSPF 8.0
Electric Resistance
EF 0.90
EF 0.95
EF 0.90
Heat Pump Water Heater
Screw‐in/Pin Lamps
Incandescent
Linear Fluorescent
T12 Advanced Incandescent ‐ tier 1 (20 lumens/watt)
Advanced Incandescent ‐ tier 2 (45 lumens/watt)
T8
Refrigerator/2nd Refrigerator
NAECA Standard
25% more efficient Freezer
NAECA Standard
25% more efficient Clothes Dryer
2024 Conventional
SEER 13.0/HSPF 7.7
Water Heater (>55 gallons)
Clothes Washer
2023 EER 11.0
Water Heater (<=55 gallons)
Dishwasher
2022 Conventional
Evaporative Room AC
Heat Pump
2019 EER 9.8
Evaporative Central AC
Cooling/Heating
2018 SEER 13
Central AC
Cooling
1st Standard (relative to today's standard)
2nd Standard (relative to today's standard)
Conventional (355kWh/yr)
Conventional (MEF 1.26 for top loader)
14% more efficient (307 kWh/yr)
MEF 1.72 for top loader
Conventional (EF 3.01)
MEF 2.0 for top loader
5% more efficient (EF 3.17)
Source: AEG-Kentucky Power Market Potential Study Kickoff
The following Figure 4-4 shows the impact of both the increasing efficiency standards and
the utility demand-side programs are projected to have on the load forecast. The combined
impact of known or anticipated (i.e., existing) utility-sponsored efficiency programs and
heightened codes and standards is slightly less than 14% of expected consumption by 2035. In
effect, the increased codes and standards already captured in SWEPCO’s long-term load forecast
have further reduced savings from what has traditionally been achieved through utility-sponsored
energy efficiency programs.
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DRAFT 2015 Integrated Resource Plan
Figure 4-4, Impact of Energy Efficiency Programs on Forecasted Retail Load
Impact of Energy Efficiency Programs on Forecasted Retail Load
16.0%
30,000
14.0%
25,000
12.0%
20,000
10.0%
G
W 15,000
H
10,000
Existing EE Programs
8.0%
Codes & Standards Energy Savings
6.0%
Incremental EE & VVO
4.0%
Retail Load, Net of Incremental EE
5,000
% of Total EE to Retail Load
2.0%
‐
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
0.0%
4.3.4 Determining Future Demand Side Programs for the IRP
4.3.4.1 “Incremental” Energy Efficiency
To determine the economic demand-side EE activity to be modeled that would be over-andabove projected EE program offerings in the load forecast, a determination was made as to the
potential level and cost of such incremental EE activity as well as the ability to expand current
programs. Figure 4-5 shows the “going-in” make-up of projected consumption in SWEPCO’s
Residential and Commercial sectors in the year 2017. It was assumed that the incremental
programs modeled would be effective in 2017, due to the time needed to develop specific
program cost and measures and receive regulatory approval to implement such programs.
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DRAFT 2015 Integrated Resource Plan
Figure 4-5: Residential and Commercial 2017 End-use in GWh
SWEPCO Residential Energy Consumption (GWh) ‐ 2017
552
Heating
1,900
Cooling
Water Heating
1,178
Appliances
Television
707
677
Lighting
Miscellaneous
371
1,096
Total = 6,481 GWh SWEPCO Commercial Energy Consumption (GWh) ‐ 2017
90
Heating
159
Water Heating
606
1,931
Cooling
Refrigeration
775
Lighting Indoor
Lighting Outdoor
1,287
Office Equipment
Ventilation
847
Miscellaneous
Total = 6,101 GWh 264
143
The current programs target certain end-uses in both sectors. Future incremental EE activity
can further target those areas or address other end-uses. To determine which end-uses are
targeted, and in what amounts, SWEPCO looked at the previously-cited 2014 EPRI Report. This
report provides comprehensive and fairly detailed information on a multitude of current and
anticipated end-use measures including measure costs, energy savings, market acceptance ratios
and program implementation factors. SWEPCO utilized this data to develop “bundles” of future
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DRAFT 2015 Integrated Resource Plan
EE activity for the demographics and weather-related impacts of its service territory. The
following Table 4-2, from the EPRI Report, lists the individual measure categories considered
for both the residential and commercial sectors.
Table 4-2: Residential & Commercial Sector Energy Efficiency Measure Categories
Residential
Central AC
Storm Doors
Air-Source Heat Pumps
External Shades
Ground-Source Heat Pumps
Ceiling Insulation
Room AC
Foundation Insulation
AC Maintenance
Foundation Insulation
HP Maintenance
Wall Insulation
Attic Fan
Windows
Furnace Fans
Reflective Roof
Ceiling Fan
Reflective Roof
Whole-House Fan
Duct Repair
Duct Insulation
Infiltration Control
Programmable Thermostat
Dehumidifier
Water Heating
Dishwashers
Faucet Aerators
Clothes Washers
Pipe Insulation
Clothes Dryers
Low-Flow Showerheads
Refrigerators
Dishwashers (Domestic Hot Water)
Freezers
Furnace Fans
Cooking
Lighting – Linear Fluorescent
Televisions
Lighting – Screw-in
Personal Computers
Enhanced Customer Bill Presentment
Smart Plugstrips, Reduce
Standby Wattage
Notes: AC = air conditioning; HP = heat pump.
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DRAFT 2015 Integrated Resource Plan
Commercial
Heat Pumps
Fans, Energy-Efficient Motors
Central AC
Fans, Variable Speed Control
Chiller
Programmable Thermostat
Cool Roof
Variable Air Volume System
VSD on Pump
Duct Testing and Sealing
Economizer
HVAC Retro-commissioning
EMS
Efficient Windows
Roof Insulation
Lighting – Linear Fluorescent
Duct Insulation
Lighting – Screw-in
Water Heater
High-Efficiency Compressor
Water Temperature Reset
Anti-Sweat Heater Controls
Computers
Floating Head Pressure Controls
Servers
Installation of Glass Doors
Displays
High-Efficiency Vending Machine
Copiers Printers
Icemakers
Other Electronics
Reach-in Coolers and Freezers
Notes: AC = air conditioning; VSD = variable speed drive; EMS = energy management systems; HVAC =
heating, ventilation, and air conditioning.
What can be derived from the tables is that the 2014 EPRI report has taken a comprehensive
approach to identifying available EE measures. From this information, SWEPCO has developed
proxy EE bundles for both Residential and Commercial customer classes to be modeled within
Plexos®. These bundles are based on measure characteristics identified within the EPRI report
and SWEPCO customer usage, and are shown in Section 4.3.5.
4.3.4.2 VVO
As discussed in Section 3, VVO equipment is an additional resource that reduces end-use
consumption. This resource is available in amounts that can be reasonably installed and tested in
a given year. VVO opportunities estimates were developed and then grouped into viably-sized
“tranches” to be modeled within Plexos®. The specific resource modeled is shown in the section
4.3.5.
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DRAFT 2015 Integrated Resource Plan
4.3.4.3 Demand Response
While introduction of a tariff that allows for the aggregation of smaller commercial and
industrial loads would likely result in meaningful resources becoming available, this IRP does
not add these resources due to SWEPCO’s currently sufficient reserve margin. The current level
of DR is maintained throughout the plan and was discussed in Section 2. While other options,
including expanded residential DR, may also be considered in the future, the lack of a fungible
capacity market in SPP could limit the likelihood of such programs.
4.3.4.4 Distributed Generation
DG resources were evaluated using a solar PV resource, as this is likely the primary
distributed resource. Solar also has favorable characteristics in that it produces the majority of its
energy at near-peak usage times. Costs were considered to be the “full” net metering (i.e. retail)
rate, which is the credit required by regulation in SWEPCO’s states. As previously described in
Section 3, DG resources (i.e., rooftop Solar) are included in the model at an assumed growth rate
based on current federal and state level incentives, future estimated costs of rooftop solar and
historical rooftop solar additions.
4.3.5 Evaluating Incremental Demand-Side Resources
The Plexos® model allows the user to input incremental EE, DG and VVO as “resources”,
thereby considering such alternatives in the model on equal-footing with more traditional
“supply-side” generation resource options.
4.3.5.1 Incremental Energy Efficiency Modeled
Table 4-3 lists the energy and cost profiles of resource “bundles” for both residential and
commercial EE that were constructed for modeling purposes.
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DRAFT 2015 Integrated Resource Plan
Table 4-3: Incremental Demand-side Resources Cost Profiles
As can be seen from the tables, each program has both “Achievable” potential and “High
Achievable” potential characteristics. The development of these characteristics is based on the
2014 EPRI EE Potential report that has been previously referenced. This report further identifies
Market Acceptance Ratios (MAR) and Program Implementation Factors (PIF) to apply to
primary measure savings, as well as, Application Factors for secondary measures. Secondary
measures are not consumers of energy, but do influence the system that is consuming energy.
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DRAFT 2015 Integrated Resource Plan
The Residential Shell, Water Heating and Commercial Cooling—in both Achievable and High
Achievable programs—include secondary measures. The MAR and PIF are utilized to develop
the incremental Achievable program characteristics and the MAR only is used to develop the
incremental High Achievable program characteristics. Screening tests were completed for all of
the EE bundles identified in Table 4-3.
The screening metrics calculated are: the Total
Resources Cost (TRC), Utility Cost Test (UCT), Ratepayer Impact Measure (RIM) and
Participant Cost Test (PCT). The screening was performed based on the industry standard
California Public Utility Guidelines titled: “Standard Practices for Cost-Benefit Analysis of
Conservation and Load Management Programs”. Table 4-4, shows the resulting metric values
for the EE bundles modeled.
Table 4-4: Incremental EE Screening Metrics
SWEPCO ‐ 2015 IRP Incremental EE Program Metrics
Benefit/Cost Ratios
Program
TRC
UCT
RIM
Residential Shell‐Thermal AP
1.5
2.4
1.0
Residential Shell‐Thermal HAP
0.9
1.1
0.6
Residential Water Heating AP
0.8
1.4
0.5
Residential Water Heating HAP
0.7
0.9
0.4
Residential Appliances AP
0.4
0.7
0.4
Residential Appliances HAP
0.3
0.4
0.3
Residential Cooling AP
1.0
1.6
0.7
Residential Cooling HAP
0.5
0.6
0.4
Residential Lighting AP
3.1
5.0
0.5
Residential Lighting HAP
2.0
2.5
0.4
Commercial Cooling AP
0.8
1.3
0.7
Commercial Cooling HAP
0.8
1.0
0.6
Commercial Office Equipment AP
0.9
1.4
0.5
Commercial Office Equipment HAP
0.7
0.9
0.4
Commercial Indoor Lighting AP
1.7
2.7
0.7
Commercial Indoor Lighting HAP
0.9
1.1
0.5
Commercial Outdoor Lighting AP
0.5
0.9
0.4
Commercial Outdoor Lighting HAP
0.2
0.3
0.2
Total
0.7
1.0
0.5
Sum of:
Achievable Potential (AP) Programs
0.9
1.4
0.6
Highly Achievable Potential (HAP) Programs
0.6
0.8
0.4
PCT
1.0
1.1
1.1
1.2
0.8
0.9
0.9
0.9
3.4
2.7
0.7
1.0
1.2
1.3
1.6
1.3
0.8
0.8
1.0
Net Benefits
UTC
1,064,983
720,832
895,316
‐
‐
‐
1,649,348
‐
3,185,507
2,383,172
3,686,041
353,396
666,978
‐
5,810,770
481,492
‐
‐
20,897,834
1.0
1.1
14,370,080
‐
While many of the bundles did not provide values greater than 1.0—which indicates that the
benefits of the bundle is greater than the cost—all of the bundles were offered into the Plexos®
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DRAFT 2015 Integrated Resource Plan
model. The amounts of incremental EE selected in total, and for each represented bundle, are
shown in Figures 5-1, 5-2 and 5-3.
4.3.5.2 VVO Modeled
Potential future SWEPCO VVO circuits considered for modeling varied in relative cost and
energy-reduction effectiveness. The circuits were grouped into 13 “tranches” based on the
relative potential demand reduction of each tranche of circuits. The Plexos® model was able to
pick the most cost-effective tranches first and add subsequent tranches as merited. Typically, a
VVO tranche includes approximately 45 circuits. Table 4-5, illustrates all of the tranches
offered into the model and the respective cost and performance of each. The costs shown are in
2014 dollars. The amount of incremental VVO selected in the model is shown in Figure 5-4.
Table 4-5: VVO Cost Profile
VVO
Tranche 1
Tranche 2
Tranche 3
Tranche 4
Tranche 5
Tranche 6
Tranche 7
Tranche 8
Tranche 9
Tranche 10
Tranche 11
Tranche 12
Tranche 13
Number of
Circuits
48
38
48
46
46
48
46
47
46
47
57
47
43
Capital
Investment Annual O&M
$ 14,400,000 $ 432,000
$ 11,400,000 $ 342,000
$ 14,400,000 $ 432,000
$ 13,800,000 $ 414,000
$ 13,800,000 $ 414,000
$ 14,400,000 $ 432,000
$ 13,800,000 $ 414,000
$ 14,100,000 $ 423,000
$ 13,800,000 $ 414,000
$ 14,100,000 $ 423,000
$ 17,100,000 $ 513,000
$ 14,100,000 $ 423,000
$ 12,900,000 $ 387,000
KW
Reduction
22,534
10,958
11,768
10,092
9,074
8,797
7,737
6,962
5,756
4,924
4,951
2,654
1,847
MWH
Reduction
92,778
45,118
48,452
41,550
37,360
36,220
31,856
28,662
23,700
20,274
20,385
10,929
7,604
4.3.5.3 Demand Response Modeled
As indicated in Section 4.3, additional levels of DR were not modeled as an incremental
resource within this plan. However, DR associated with known and anticipated interruptible and
real-time pricing initiatives have already been incorporated into SWEPCO’s future “going-in”
capacity position, as described in Section 2.
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DRAFT 2015 Integrated Resource Plan
4.3.5.4 Customer-Owned (Distributed) Solar Modeled
Customer-owned resources, generally solar resources, specifically were modeled as a stream
of payments valued at the full-retail rate which is consistent with current net metering rules. This
treatment is independent of assumptions of installation and operating costs of the solar resources,
as they are borne by the customer, and are not part of revenue requirements. This is consistent
with how other demand-side resources are modeled.
4.3.5.5 Optimizing Incremental Demand-side Resources
The Plexos® software views demand-side resources as non-dispatchable “generators” that
produce energy similar to non-dispatchable supply-side generators such as wind or solar. Thus,
the value of each resource is impacted by the hours of the day and time of the year that it
“generates” energy. As discussed previously, Plexos® optimized five different economic pricing
scenarios.
4.3.5.6 Discussion and Conclusion
As the mechanism for regulatory cost recovery and the appetite for utility-sponsored DR/EE
is formalized through state-specific legislative and ratemaking processes in the various
jurisdictions in which SWEPCO operates, the amount and type of DR/EE programs will likely
change by jurisdiction to reflect that specific environment.
4.4 Identify and Screen Supply-side Resource Options
4.4.1 Capacity Resource Options
In addition to market capacity purchase options, “new-build” alternative were modeled to
represent peaking and baseload/intermediate capacity resource options. To reduce the number of
modeling permutations in Plexos®, the available technology options were limited to certain
representative unit types. However, it is important to note that alternative technologies with
comparable cost and performance characteristics may ultimately be substituted should
technological or market-based profile changes warrant. The options assumed to be available for
modeling analyses for SWEPCO are presented in Exhibit F of the Appendix. When applicable,
SWEPCO may take advantage of economical market opportunities in the form of limited-term
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DRAFT 2015 Integrated Resource Plan
bilateral capacity purchases and discounted generation asset purchases. Such market
opportunities could be utilized to hedge capacity planning exposures should they emerge and
create (energy) option value to the Company. Prospectively, these opportunities could take the
place of currently planned resources and will be evaluated on a case-by-case basis.
4.4.2 New Supply-side Capacity Alternatives
As identified in Exhibit F, natural gas “base/intermediate” and “peaking” generating
technologies were considered in this IRP as well as utility-scale solar and wind. However, in an
attempt to reduce the problem size within the Plexos® modeling application, an economic
screening process was used to analyze various supply options and develop a quantitative
comparison for each “duty-cycle” type of capacity (i.e., baseload, intermediate, and peaking) on
a forty-year, levelized basis. The options were screened by comparing levelized annual busbar
costs over a range of capacity factors.
In this evaluation, each type of technology is represented by a line showing the relationship
between its total levelized annual cost per kW and an assumed annual capacity factor. The value
at a capacity factor of zero represents the fixed costs, including carrying charges and fixed
O&M, which would be incurred even if the unit produced no energy. The slope of the line
reflects variable costs, including fuel, emissions, and variable O&M, which increase in
proportion to the energy produced.
The best of class technology determined by this screening process was taken forward to the
Plexos® model. These generation technologies were intended to represent reasonable proxies for
each capacity type (baseload, intermediate, peaking). Subsequent substitution of specific
technologies could occur in any ultimate plan, based on emerging economic or non-economic
factors not yet identified.
AEP’s Generation organization is responsible for the tracking and monitoring of estimated
cost and performance parameters for a wide array of generation technologies. Utilizing access to
industry collaborative organizations such as EPRI and the Edison Electric Institute, AEP’s
association with architect and engineering firms and original equipment manufacturers as well as
its own experience and market intelligence, this group continually monitors supply-side trends.
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DRAFT 2015 Integrated Resource Plan
Table 4-6 offers a summary of the most recent technology performance parameter data
developed.
Table 4-6: New Generation Technology Options
Key Supply-Side Resource Option Assumptions
Capability
Type
(MW)(a)
SO2
Emission Rates
NOx
CO2
(Lb/mmBtu)
(Lb/mmBtu) (Lb/mmBtu)
Capacity
Overall
Factor Availability
(%)
(%)
Base Load
Nuclear
1610
0.00
0.00
0.00
90
95
Base Load (90% CO2 Capture New Unit)
Pulv. Coal (Ultra-Supercritical) (PRB)
IGCC "F" Class (PRB)
540
490
0.10
0.06
0.07
0.06
21.3
21.3
85
85
88
88
Base / Intermediate
Combined Cycle (2X1 "F" Class)
Combined Cycle (2X1 "G" Class, w/duct firing & inlet cooling)
620
780
0.0007
0.0007
0.009
0.007
116.0
116.0
60
60
89
89
Peaking
Combustion Turbine (2 - "E" Class)
Combustion Turbine (2 - "F" Class, w/inlet cooling)
Aero-Derivative (2 - Small Machines)
Aero-Derivative (2 - Large Machines, w/inlet cooling)
Recip Engine Farm (22 Engines)
164
420
90
200
200
0.0007
0.0007
0.0007
0.0007
0.0007
0.033
0.007
0.093
0.007
0.018
116.0
116.0
116.0
116.0
116.0
3
3
3
25
3
93
93
96
95
94
Notes: (a) Capability at Standard ISO Conditions at 1,000 feet above sea level.
4.4.3 Baseload/Intermediate Alternatives
Coal and Nuclear baseload options were evaluated by SWEPCO but were not included in
the ultimate Plexos® resource optimization modeling analyses.
The forecasted difference
between SWEPCO’s load forecast and existing resources are such that a large, central generating
station would not be required. In addition, for coal generation resources, the proposed EPA New
Source Performance Standards (NSPS) rulemaking described in Section 3.5.4.5.1 effectively
makes the construction of new coal plants environmentally/economically impractical due to the
implicit requirement of carbon capture and sequestration (CCS) technology. For new nuclear
construction, it is financially impractical since it would potentially require an investment of,
minimally, $7,000/kW.
Intermediate generating sources are typically expected to serve a load-following and cycling
duty and effectively shield baseload units from that obligation. Historically, many generators
have relied on older, smaller, less-efficient/higher dispatch cost, subcritical coal-fired or gas86
DRAFT 2015 Integrated Resource Plan
steam units to serve such load-following roles. Over the last several years, these units’ staffs
have made strides to improve ramp rates, regulation capability, and reduce downturn (minimum
load capabilities). As the fleet continues to age and subcritical units are retired, other generation
dispatch alternatives and new generation will need to be considered to cost effectively meet this
duty cycle’s operating characteristics.
4.4.3.1 Natural Gas Combined Cycle (NGCC)
An NGCC plant combines a steam cycle and a combustion gas turbine cycle to produce
power. Waste heat (~1,100°F) from one or more combustion turbines passes through a heat
recovery steam generator (HRSG) producing steam. The steam drives a steam turbine generator
which produces about one-third of the NGCC plant power, depending upon the gas-to-steam
turbine design “platform,” while the combustion turbines produce the other two-thirds.
The main features of the NGCC plant are high reliability, reasonable capital costs, operating
efficiency (at 45-60% Low Heating Value), low emission levels, small footprint and shorter
construction periods than coal-based plants. In the past 8 to 10 years, NGCC plants were often
selected to meet new intermediate and certain baseload needs. NGCC plants may be designed
with the capability of being “islanded” which would allow them, in concert with an associated
diesel generator, to perform system restoration (“black start”) services. Although cycling duty is
typically not a concern, an issue faced by NGCC when load-following is the erosion of
efficiency due to an inability to maintain optimum air-to-fuel pressure and turbine exhaust and
steam temperatures. Methods to address these include:

Installation of advanced automated controls.

Supplemental firing while at full load with a reduction in firing when load decreases.
When supplemental firing reaches zero, fuel to the gas turbine is cutback. This
approach would reduce efficiency at full load, but would likewise greatly reduce
efficiency degradation in lower-load ranges.

Use of multiple gas turbines coupled with a waste heat boiler that will give the widest
load range with minimum efficiency penalty.
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DRAFT 2015 Integrated Resource Plan
4.4.4 Peaking Alternatives
Peaking generating sources provide needed capacity during extreme high-use peaking
periods and/or periods in which significant shifts in the load (or supply) curve dictate the need
for “quick-response” capability. The peaks occur for only a few hours each year and the installed
reserve requirement is predicated on a one day in ten year loss of load expectation, so the
capacity dedicated to serving this reliability function can be expected to provide relatively little
energy over an annual load cycle. As a result, fuel efficiency and other variable costs applicable
to these resources are of lesser concern. Rather, this capacity should be obtained at the lowest
practical installed/fixed cost, despite the fact that such capacity often has very high energy costs.
Ultimately, such “peaking” resources requirements are manifested in the system load duration
curve.
In addition, in certain situations, peaking capacity such as combustion turbines can provide
backup and some have the ability to provide emergency (Black Start) capability to the grid.
4.4.4.1 Simple Cycle Natural Gas Combustion Turbines (NGCT)
In “industrial” or “frame-type” combustion turbine systems, air compressed by an axial
compressor is mixed with fuel and burned in a combustion chamber. The resulting hot gas then
expands and cools while passing through a turbine. The rotating rear turbine not only runs the
axial compressor in the front section but also provides rotating shaft power to drive an electric
generator. The exhaust from a combustion turbine can range in temperature between 800 and
1,150 degrees Fahrenheit and contains substantial thermal energy. A simple cycle combustion
turbine system is one in which the exhaust from the gas turbine is vented to the atmosphere and
its energy lost, i.e., not recovered as in a combined cycle design. While not as efficient (at 3035% LHV), they are inexpensive to purchase, compact, and simple to operate.
4.4.4.2 Aeroderivatives (AD)
Aeroderivatives are aircraft jet engines used in ground installations for power generation.
They are smaller in size, lighter weight, and can start and stop quicker than their larger industrial
or "frame" counterparts. For example, the GE 7EA frame machine requires 20 minutes to ramp
up to full load while the smaller LM6000 aeroderivative only needs 10 minutes from start to full
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DRAFT 2015 Integrated Resource Plan
load. However, the cost per kW of an aeroderivative is on the order of 20% higher than a frame
machine.
The AD performance operating characteristics of rapid startup and shutdown make the
aeroderivatives well suited to peaking generation needs. The aeroderivatives can operate at full
load for a small percentage of the time allowing for multiple daily startups to meet peak
demands, compared to frame machines which are more commonly expected to start up once per
day and operate at continuous full load for 10 to 16 hours per day. The cycling capabilities
provide aeroderivatives the ability to backup variable renewables such as solar and wind. This
operating characteristic is expected to become more valuable over time as: a) the penetration of
variable renewables increase; b) baseload generation processes become more complex limiting
their ability to load-follow and; c) intermediate coal-fueled generating units are retired from
commercial service.
AD units weigh less than their industrial counterparts allowing for skid or modular
installations. Efficiency is also a consideration in choosing an aeroderivative over an industrial
turbine. Aeroderivatives in the less than 100 MW range are more efficient and have lower heat
rates in simple cycle operation than industrial units of equivalent size. Exhaust gas temperatures
are lower in the aeroderivative units.
Some of the better known aeroderivative vendors and their models include GE's LM series,
Pratt & Whitney's FT8 packages, and the Rolls Royce Trent and Avon series of machines.11
4.4.4.3 Reciprocating Engines (RE)
The use of reciprocating engines (RE) or internal combustion engines has increased over
the last twenty years. According to EPRI, in 1993 about 5% of the total RE units sold were
natural gas-fired spark ignition (SI) engines and post 2000 sales of natural gas-fired generators
have remained above 10% of total units sold worldwide.
Improvements in emission control systems and thermal efficiency have led to the
increased utilization of natural gas-fired RE generators incorporated into multi-unit power
generation stations for main grid applications. The RE generators have high efficiency, flat heat
11
Turbomachinery International, Jan/Feb. 2009; Gas Turbine World; EPRI TAG.
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DRAFT 2015 Integrated Resource Plan
rate curves and rapid response makes this technology very well suited for peaking and
intermediate load service and as back up to intermittent generating resources. Additionally, the
fuel supply pressure required is in the range of 40 to 70 psig, this lower gas pressure gives this
technology more flexibility when identifying locations. A further advantage of RE generators is
that power output is less affected by increasing elevation and ambient temperature as compared
to gas turbine technology. Also, a RE plant generally would consist of multiple units, which will
be more efficient at part load operation than a single gas turbine unit of equivalent size because
of the ability to shut down units and the remaining units at higher load. Common RE unit sizes
have generally ranged from 8 MW to 18 MW per machine with heat rates in the range 8,100 –to8,600 Btu/kWh (HHV).
Regarding operating cost, RE generators have a somewhat greater variable O&M than a
comparable gas turbine; however, over the long term, maintenance costs of RE are generally
lower because the operating hours between major maintenance can be twice as long as gas
turbines of similar size.
The main North American suppliers for utility scale natural gas-fired RE most recently
have been Caterpillar and Wartsila12.
4.4.5 Renewable Alternatives
Renewable generation alternatives use energy sources that are either naturally occurring
(wind, solar, hydro or geothermal), or are sourced from a by-product or waste-product of another
process (biomass or landfill gas). In the recent past, development of these resources has been
driven primarily as the result of renewable portfolio requirements. That is not universally true
now as advancements in both solar PV and wind turbine manufacturing have reduced both
installed and ongoing costs.
4.4.5.1 Utility-Scale Solar
Solar power takes a couple of viable forms to produce electricity: concentrating and
photovoltaics. Concentrating solar—which heats a working fluid to temperatures sufficient to
12
Technical Assessment Guide (TAG) Power Generation and Storage Technology Options, 2012; Electric Power
Research Institute.
90
DRAFT 2015 Integrated Resource Plan
power a turbine—produces electricity on a large scale and is similar to traditional centralized
supply assets in that way. Photovoltaics produce electricity on a smaller scale (2 kW to 20 MW
per installation) and can be distributed throughout the grid. Figure 4-6 shows the potential solar
resource locations in the U.S.
Figure 4-6: United States Solar Power Locations
The cost of solar panels has declined considerably in the past decade and is expected to
continue to decline, as shown in Figure 4-7: Solar Panel Installed Cost. This has been mostly a
result of reduced panel prices that have resulted from manufacturing efficiencies spurred by
accelerating penetration of solar energy in Europe, Japan, and California. With the trend firmly
established, forecasts generally foresee declining nominal prices in the next decade as well.
Not only are utility scale solar plants getting less expensive, the costs to install solar panels
in distributed locations, often on a rooftop, are lessening as associated hardware, such as
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DRAFT 2015 Integrated Resource Plan
inverters, racks, and wiring bundles become standardized. If the projected cost declines
materialize, both distributed and utility scale solar projects will be economically justifiable in the
future.
Figure 4-7: Solar Panel Installed Cost
Installed Solar Cost
6.00
5.50
5.00
Utility Cost
$/Watt(ac)
4.50
Commercial
4.00
Residential
3.50
3.00
2.50
2.00
1.50
1.00
2014
2019
2024
2029
2034
2039
Utility solar plants require less lead time to build than fossil plants. There is not a defined
limit to how much utility solar can be built in a given time. However, in practice, solar facilities
are not added in an unlimited fashion.
Solar resources were considered available resources with some limits on the rate with which
they could be chosen. Utility solar resources were made available up to 50 MWac13 of
incremental nameplate capacity starting in 2015. To provide some context around that, a typical
commercial installation is 50 kW and effectively covers the surface of a typical “big box”
retailer’s roof. A 50 MW utility-scale solar “farm” is assumed to consume nearly 350 acres, or
1,000 big box retailer roofs. A limit on solar capacity additions is needed in that as solar costs
continue to decrease relative to the market price of energy, there will come a point where the
13
Manufacturers usually quote system performance in DC watts, however electric service from the utility is
supplied in AC watts. An inverter converts the DC electrical current into AC electrical current. Depending on the
inverter efficiency, the AC wattage may be anywhere from 80 to 95 percent of the DC wattage.
92
DRAFT 2015 Integrated Resource Plan
optimization model will theoretically pick an unlimited amount of solar resources. This 50
MWac annual threshold recognizes that there is a practical limit as to the number of sites that can
be identified, permitted and constructed by SWEPCO in a given year. Certainly as SWEPCO
gains experience with solar installations, this limit will be modified (for example, it may be
lower earlier and greater later).
Solar resources’ useful capacity is less than its nameplate rating. In SPP, that capacity credit
will be based on actual experience and is assumed to be 42% of the nameplate rating (currently it
is 10% without 3 years of historical generation) for the single axis resource that is being
considered. Time will tell whether solar can be implemented at a pace that approaches the limits
incorporated, or perhaps, even exceed those limits.
4.4.5.2 Wind
4.4.5.2.1 Modeling Wind Resources
Utility wind energy is generated by wind turbines with a range 1.0 to 2.5 MW, with a 1.5
MW turbine being the most common size used in commercial applications today. Typically,
multiple wind turbines are grouped in rows or grids to develop a wind turbine power project
which requires only a single connection to the transmission system. Location of wind turbines at
the proper site is particularly critical as not only does the wind resource vary by geography, but
its proximity to a transmission system with available capacity will factor into the cost.
A variable source of power in most non-coastal locales, with capacity factors ranging from
30 percent (in the eastern portion of the U.S.) to 50 percent (largely in more westerly portions of
the U.S., including the Plains states), wind energy’s life-cycle cost ($/MWh), excluding
subsidies, is currently higher than the marginal (avoided) cost of energy, in spite of its negligible
operating costs.
Another consideration with wind power is that its most critical factors (i.e., wind speed and
sustainability) are typically highest in very remote locations, and this forces the electricity to be
transmitted long distances to load centers necessitating the build out of EHV transmission to
optimally integrate large additions of wind into the grid. In the SPP region, wind is credited with
5% useful capacity absent a three year generating history; or wind turbines are, on average,
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DRAFT 2015 Integrated Resource Plan
producing at 5% of nameplate capacity at the time of SPP peak. Recent wind project experience,
however, has shown that they are capable of providing capacity value up to 37% of nameplate,
using three year generating history. Figure 4-8 shows the wind resource locations in the U.S. and
their relative potential.
Figure 4-8: United States Wind Power Locations
For modeling purposes, wind was considered under various ‘blocks’ or ‘tranches’ for each
year. There will be three tranches of wind pricing. The first tranche was based on the value
provided by the Southern Wind Energy Association in feedback to the SWEPCO IRP at the first
Louisiana stakeholder meeting and then reduced by $2/MWh. The slight reduction was based on
informal market discussions with wind suppliers within the SPP footprint. The first tranche of
wind resource was modeled as a 100 MW block with a levelized cost of energy (LCOE) without
the PTC of $47/MWh in 2015$ and a 56% capacity factor load shape. The second tranche of
wind pricing was based on informal discussions with wind suppliers, and again assumed a 100
MW block; utilizing an 8.5 meter per second wind speed yielded LCOE without PTC of
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DRAFT 2015 Integrated Resource Plan
$55/MWh and a 50% capacity factor load shape. The third and last level of pricing assumes a
scenario of high demand for wind resources resulting in LCOE of $60/MWh; a 100MW block
and a 45% capacity factor load shape. Additionally, SWEPCO assumed the first two pricing
blocks to have “useful capacity” equivalent to 10%, and the third block to have 5%, of their
nameplate (nominal) capacity.
The expected magnitude of wind resources available per year was limited to 300 MW
(nameplate) with a total cap of 1,700 MW nameplate (representing 30% of SWEPCO’s overall
anticipated resource portfolio over the planning period). Also, potential wind resources added—
per pricing level defined above—were, for modeling purposes, split equally among the three
tranches in 100 MW blocks. Figure 4-9, illustrates the three tranches of wind resources modeled
and the relative “levelized cost of energy (LCOE) per MWh” utilized for each strata.
Figure 4-9: Wind Resources Modeled
Modeled Wind Resource Pricing LCOE Trends ($/MWh)
100.00
90.00
80.00
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
Tranche 1
Tranche 2
95
Tranche 3
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
Tranches are 100 MWs each and limited to one of each tranche per year
DRAFT 2015 Integrated Resource Plan
4.4.5.3 Hydro
The available sources of, particularly, larger hydroelectric potential have largely been
exploited and those that remain must compete with the other uses, including recreation and
navigation. The potentially lengthy time associated with environmental studies, Federal Army
Corp of Engineer permitting, high up-front construction costs, and environmental issues (fish and
wildlife) make hydro prohibitive at this time. As such, no incremental hydroelectric resources
were considered in this IRP.
4.4.5.4 Biomass
Biomass is a term that typically includes organic waste products (sawdust or other wood
waste), organic crops (corn, switchgrass, poplar trees, willow trees, etc.), or biogas produced
from organic materials, as well as select other materials. Biomass costs will vary significantly
depending upon the feedstock. Biomass is typically used in power generation through the
utilization of the biomass fuel in a steam generator (boiler) that subsequently drives a steam
turbine generator; similar to the same process of many traditional coal fired generation units.
Some biomass generation facilities use biomass as the primary fuel, however, there are some
existing coal-fired generating stations that will use biomass as a blend with the coal. Given these
factors, plus the typical high cost and required feedstock supply and attendant long-term pricing
issues, no incremental biomass resources were considered in this IRP.
4.4.5 Cogeneration & Combined Heat & Power (CHP)
Cogeneration is a process where electricity is generated and the waste heat by-product is
used for heating or other process, raising the net thermal efficiency of the plant. To take
advantage of the increased efficiency associated with CHP, the host must have a ready need for
the heat that is otherwise potentially wasted in the generation of electricity. SWEPCO has five
cogeneration customers, one customer in the Arkansas jurisdiction and four customers within the
Texas jurisdiction. Table 4-7, is a summary of SWEPCO’s cogeneration customers. The
majority of this CHP capacity is related to the chemical and paper industries.
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DRAFT 2015 Integrated Resource Plan
Table 4-7: SWEPCO Cogeneration Capacity
State
Arkansas
Louisiana
Texas
Total
Commercial Industrial Industrial Capacity Commercial Capacity Total (MW)
Sites
(MW)
Sites
Sites
1
130
0
0
1
0
0
0
0
0
4
529
0
0
4
5
659
0
0
5
Total Capacity (MW)
130
0
529
659
Historically, SWEPCO’s low cost of energy combined with the relatively high cost of
natural gas, a primary fuel for cogeneration facilities, has made cogeneration uneconomical in
SWEPCO’s service territory. SWEPCO is occasionally approached by customers for help in
evaluating CHP and cogeneration opportunities, but the Company’s relatively low avoided costs
have been a significant barrier to-date for any serious implementation consideration. Most
recently SWEPCO has worked with the University of Arkansas in Fayetteville to interconnect an
expected 5 MW CHP project that is anticipated to be online in the summer of 2015. While,
SWEPCO has the flexibility to include smaller CHP offerings within its EE programs, given the
unique customer/site-specific consideration of larger-scale CHP, no such incremental CHP
resources were considered in this IRP.
4.5 Integration of Supply-Side and Demand-Side Options within Plexos® Modeling
4.5.1 Optimize Expanded DSM Programs
As described in Section 3, EE and VVO options that would be incremental to the current
programs were modeled as resources within Plexos®. In this regard, they are “demand-side power
plants” that produce energy according to their end use load shape. They have an initial (program)
cost with no subsequent annual operating costs. Likewise, they are “retired” at the end of their
useful (EE measure) lives (see Table 4-3).
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DRAFT 2015 Integrated Resource Plan
4.5.2 Optimize Other Demand-Side Resources
Customer-sited distributed generation, specifically distributed solar generation, was
modeled as a purchase power agreement with the cost to the utility being the full retail rate,
consistent with current net metering tariffs.
4.5.3 Analysis and Review
To develop the “Preferred Portfolio,” SWEPCO developed resource portfolios that were
optimized under five separate (economic) pricing scenarios. In addition, two load sensitivities
plus an “early gas-steam unit retirement” scenario as well as an (lower-carbon) “Early Solid-Fuel
Unit Retirement” were created. These scenarios are described in Section 5.2.2. These optimized
portfolios form the basis for the Preferred Portfolio resource plan, which is then further evaluated
under a distribution of economic futures, often referred to as a Monte Carlo analysis, to
determine the relative economic “risk” of the plan.
SWEPCO’s Preferred Portfolio presented in Section 5 is expected to provide adequate
reliability over the forecast period.
The long-term capacity schedule reported herein is simply a snapshot of the future at this
time, based on current thinking relative to various parameters, each having its own degree of
uncertainty. The expansion reflects, to a large extent, assumptions that are subject to change. As
the future unfolds, and as parameter changes are recognized and updated, input information are
continually evaluated, and resource plans modified as appropriate.
Some key factors that can affect the timing of future capacity additions are the magnitude of
future loads and capacity reserve requirements. The magnitude of the future load in any
particular year is a function of load growth and DSM impacts. Capacity reserve requirements, as
previously discussed, could vary depending on the average system generating-unit availability of
both SWEPCO and SPP.
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DRAFT 2015 Integrated Resource Plan
5.0 Resource Portfolio Modeling
5.1 The Plexos® Model - An Overview
Plexos® LP long-term optimization model, also known as “LT Plan®,” served as the basis
from which the SWEPCO-specific capacity requirement evaluations were examined and
recommendations were made. The LT Plan® model finds the optimal portfolio of future capacity
and energy resources, including DSM additions that minimize the CPW of a planning entity’s
generation-related variable and fixed costs over a long-term planning horizon.
Plexos® accomplishes this by an objective function which seeks to minimize the aggregate
of the following capital and production-related (energy) costs of the portfolio of resources:

fixed costs of capacity additions, i.e., carrying charges on incremental capacity
additions (based on a SWEPCO-specific, weighted average cost of capital), and
fixed O&M;

fixed costs of any capacity purchases;

program costs of (incremental) DSM alternatives;

variable costs associated with SWEPCO’s generating units. This includes fuel,
start-up, consumables, market replacement cost of emission allowances, and/or
carbon ‘tax,’ and variable O&M costs;

distributed, or customer-domiciled resources were effectively value at the
equivalent of a full-retail “net metering” credit to those customers (i.e., a “utility”
perspective); and

a ‘netting’ of the production revenue made into the SPP power market from
SWEPCO’s generation resource sales and the cost of energy – based on unique
load shapes from SPP purchases necessary to meet SWEPCO’s load obligation.
Plexos® executes the objective function described above while abiding by the following
possible constraints:

Minimum and maximum reserve margins;

Resource addition and retirement candidates (i.e., maximum units built);

Age and lifetime of generators;

Retrofit dependencies (SCR and FGD combinations);

Operation constraints such as ramp rates, minimum up/down times, capacity, heat
rates, etc.;
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DRAFT 2015 Integrated Resource Plan

Fuel burn minimum and maximums;

Emission limits on effluents such as SO2 and NOX; and

Energy contract parameters such as energy and capacity.
The model inputs that compose the objective function and constraints are considered in the
development of an integrated plan that best fits the utility system being analyzed. Plexos® does
not develop a full regulatory cost-of-service (COS) profile. Rather, it typically considers only the
relative load and generation COS that changes from plan-to-plan, and not fixed “embedded”
costs associated with existing generating capacity and demand-side programs that would remain
constant under any scenario. Likewise, transmission costs are included only to the extent that
they are associated with new generating capacity, or are linked to specific supply alternatives. In
other words, generic (nondescript or non-site-specific) capacity resource modeling would
typically not incorporate significant capital spends for transmission interconnection costs.
5.1.1 Key Input Parameters
Two of the major underpinnings in this process are long-term forecasts of SWEPCO’s
energy requirements and peak demand, as well as the price of various generation-related
commodities, including energy, capacity, coal, natural gas and, potentially, CO2/carbon. Both
views were created internally within AEP. The load forecast, including the SWEPCO load and
demand summary offered in Exhibit G was created by the AEP Economic Forecasting
organization, while the long-term commodity pricing forecast was created by the AEP
Fundamental Analysis group. Exhibit C offers tables that summarize several of the key longterm fundamental commodity pricing projections utilized in these analyses. These groups have
had years of experience forecasting SWEPCO and AEP system-wide demand and energy
requirements and fundamental pricing for both internal operational and regulatory purposes.
Moreover, the Fundamental Analysis group constantly performs peer review by way of
comparing and contrasting its commodity pricing projections versus “consensus” pricing on the
part of outside forecasting entities such as IHS-CERA, PIRA and the EIA.
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DRAFT 2015 Integrated Resource Plan
Other critical input parameters include the installed cost of replacement capacity alternative
options, as well as the attendant operating costs associated with those options; data which was
sourced from the AEP Engineering Services (ES) organization.
5.2 Plexos® Optimization
5.2.1 Modeling Options and Constraints
The major system limitations that were modeled by use of constraints are elaborated on
below. The LT Plan®, LP optimization algorithm operates modeled constraints in tandem with
the objective function in order to yield the least-cost resource plan. For instance, the model
required capacity additions to meet a 15% reserve margin, slightly above the SPP-required
minimum reserve margin of roughly 13.6% as represented earlier in this report in the
development of SWEPCO’s “going-in” capacity position. This slightly higher reserve margin
allows the model to select larger blocks of resources in a given year if they provide energy value
relative to the SPP market while also providing a physical hedge against unanticipated SWEPCO
unit retirements.
There are many variants of available supply-side and demand-side resource options and
types. It is a practical limitation that not all known resource types are made available as
modeling options. A screening of available supply-side technologies was performed with the
optimum assets made subsequently available as options. Such screens for supply alternatives
were performed for duty cycle “families” (baseload, intermediate, and peaking).
The selected technology alternatives from this screening process do not necessarily
represent the optimum technology choice for that duty-cycle family. Rather, they reflect proxies
for modeling purposes. Other factors will be considered that will determine the ultimate
technology type (e.g., choices for peaking technologies). The full list of screened supply options
is included in Exhibit E of the Appendix.
Based on the established comparative economic screenings, the following specific supply
alternatives were modeled in Plexos® for each designated duty cycle:

Peaking capacity was modeled, effective in 2017 due to the anticipated period
required to approve, site, engineer and construct, from:
101
DRAFT 2015 Integrated Resource Plan

o Simple-Cycle Combustion Turbine units consisting of two “E”
class turbines at 164 MW
o Reciprocating Engine units at 50 MW and 201 MW. Note: No
more than 10 (500 MW) units could be selected by the model per
year.
Intermediate capacity was modeled, effective in 2019 due to anticipated period
required to approve, site, engineer and construct, from:
o Natural gas Combined Cycle (2x1 “F” class turbine without duct
firing platform) units, sized at either 312 MW (50%) and 624 MW
(100%).
o Natural gas Combined Cycle (2x1 “G” class turbine with duct
firing and inlet air cooling) units sized at 390 MW (50%) and 780
MW (100%), with respective summer capacity ratings with ductfiring and inlet air cooling of 453 MW and 906 MW.





Wind resources were made available up to 300 MW annually (3 tranches of 100
MW at an initial levelized cost of $47/MWh, $55/MWh, $60/MWh, respectively)
of incremental nameplate capacity, with up to 1,700 MW installed over the
planning period.
Utility-scale solar resources were made available up to 50 MW annually of
incremental nameplate capacity applied to an externally-derived declining
installed cost curve.
DG, in the form of distributed solar resources in 5 kW sizes, was made available
in amounts equal to approximately 5% of annual increases after the Louisiana
incentive cap (7.8 MW) is met.
EE resources—incremental to those already incorporated into the Company’s
long-term load and peak demand forecast in up to 16 unique “bundles” of
Residential and Commercial measures considering cost and performance
parameters for both “High Achievable” potential and “Achievable” potential
categories.
VVO was available in 13 tranches of varying installed costs and number of
circuits/sizes ranging from a low of 2 MW, up to 22 MW.
5.2.2 Optimized Portfolios
The key decision to be made by SWEPCO during the planning period is how to fill the
resource need identified. Portfolios with various options addressing SWEPCO’s capacity and
102
DRAFT 2015 Integrated Resource Plan
energy resource needs over time were optimized using the ‘Base’ load and demand forecast, but
under five different long-term commodity pricing scenarios:
1. ‘Base’ pricing
2. ‘Higher Band’ pricing
3. ‘Lower Band’ pricing
4. ‘High CO2’ (or High Carbon) pricing
5. ‘No CO2’ (or No Carbon) pricing
Two sensitivity portfolio evaluations were conducted under ‘Base’ commodity pricing,
but using two different long-term load (and peak demand) forecasts:
6. ‘High Load’ sensitivity
7. ‘Low Load’ sensitivity
Two additional “sensitivity” portfolio evaluations were created under the Base pricing
and load forecasts assessing:
8. ‘Accelerated Gas-Steam Unit Retirement’ sensitivity
9. ‘Early Solid-Fuel Unit Retirement’ sensitivity
The “Accelerated Gas-Steam Unit Retirement” sensitivity case was intended to accelerate—
by 4 to 5 years—the currently anticipated retirements of SWEPCO’s older, smaller, less
thermally-efficient gas-steam units that had already been planned for retirement over the
planning period as part of the going-in ‘base’ plan modeling. Finally, the “Early Solid-Fuel Unit
Retirement” sensitivity case sought to model the implication of as many as two existing
(approaching or exceeding 1,000 MW) solid-fuel units being retired around the 2020 timeframe;
whereas such retirements were not contemplated as part of the ‘base’ modeling.
Finally, risk or “stochastic” analyses were then performed on select portfolios. Table 5-1
below shows the matrix of the nine optimized evaluations.
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DRAFT 2015 Integrated Resource Plan
Table 5-1: Portfolios Evaluated
Fundamental Commodity Price Forecasts
Base
Load High
Forecast
Low
Base
1,8,9
6
7
Lower Band
2
Higher Band
3
High CO2 No CO2
4
5
5.2.2.1 Optimization Modeling Results Under the Base Load Forecast
Portfolios 1 through 5 were all optimized under the base load forecast. The annual capacity
additions for each portfolio are included graphically in Exhibit J. A summary of that exhibit
showing cumulative capacity additions at five year intervals is provided below in Table 5-2.
Note that all portfolios include a diversity of resource options such as natural gas fired
generation, energy efficiency, and renewable resources. The capacity values for intermittent
resources (wind, utility-scale and distributed solar) represent firm capacity for reserve margin
planning purposes, not nameplate values.
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DRAFT 2015 Integrated Resource Plan
Table 5-2
Optimized Resource Plan Cumulative Additions at 5 Year Intervals Under Various Pricing Scenarios
Pricing Scenario
Recip CT Firm Engine Firm CC Firm Capacity Capacity Capacity (MW)
(MW)
(MW)
Incremental Res Distributed Utility Solar Wind & Comm'l DSM Solar Firm Firm VVO Firm Firm Firm Capacity Capacity Capacity Capacity Capacity Total MW (MW)
(MW) (1) (MW) (2)
(MW)
(MW) (3) Additions
1. Base 2. Low Band
3. High Band
4. High Carbon
5. No Carbon
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
By 2020
36
49
45
33
49
1
1
1
1
1
21
0
30
0
0
38
26
51
51
38
0
0
10
10
0
97
76
137
95
88
1. Base 2. Low Band
3. High Band
4. High Carbon
5. No Carbon
157
157
157
0
157
50
101
0
0
101
0
0
0
453
0
By 2025
109
108
132
57
108
2
2
2
2
2
127
98
136
55
98
51
38
63
63
38
100
100
120
120
100
597
603
610
750
603
1. Base 2. Low Band
3. High Band
4. High Carbon
5. No Carbon
157
157
157
0
157
101
151
50
0
151
0
0
0
453
0
By 2030
221
218
252
134
218
3
3
4
3
3
233
204
242
161
204
100
92
100
73
92
160
150
170
170
150
975
974
974
994
974
1. Base 2. Low Band
3. High Band
4. High Carbon
5. No Carbon
157
157
157
0
157
101
151
50
0
151
0
0
0
453
0
By 2035
314
294
342
232
292
5
5
5
5
5
339
310
348
267
310
100
92
100
73
92
170
170
170
170
170
1,185
1,178
1,172
1,201
1,176
(1) Distributed Solar = 10% nameplate, (2) Utility Solar = 42.4% nameplate, (3) Wind = 10% namplate
Close examination of the optimized plan results provides SWEPCO with insight in
developing a “preferred” resource plan. For example, no new natural gas capacity is required
prior to 2020 under any pricing scenario; however, some level of peaking capacity is likely to be
needed by 2025 and, under the ‘High CO2’ pricing scenario, this peaking capacity would be
replaced with a natural gas combined cycle plant. Also by 2020, a combination of commercial
and residential incremental energy efficiency programs are added under all pricing scenarios,
providing a capacity benefit in the range of 33 MW to 49 MW. VVO and wind are selected in
relatively comparable amounts over the planning period in all pricing scenarios, while utility
scale solar is more favored in the Base, Higher Band and High CO2 pricing scenarios. Note that
distributed solar must be “forced in” to the portfolios as it will generally not be selected as an
optimal resource because, under the net-metering construct, the utility must pay the full retail
105
DRAFT 2015 Integrated Resource Plan
rate for the kWh’s created, which includes costs-of-service for generation, transmission and
distribution, while only “avoiding” the (lower) SPP market cost of energy.
5.2.2.2 Load and Unit Retirement Sensitivity Case Modeling Results
As shown in Table 5-3, resource additions are required earlier (than the Base case) in both
the Accelerated Gas Steam Unit Retirement sensitivity case, the Early Solid-Fuel Retirement
sensitivity case (which retires Welsh Unit 1 and Pirkey in 2020), and the High Load sensitivity
case, and later in the Low Load sensitivity case. The Early Solid-Fuel Retirement, High Load
and High CO2 cases all add NGCC units before 2025; whereas the Accelerated Gas Steam Unit
Retirement sensitivity case does not add an NGCC until 2035. The Low Load case also only
requires peaking capacity.
These sensitivity cases provide insight into the types of resources that should be considered
in the Preferred Portfolio, in that it must consider potential negative impacts associated with
adding (or not adding) a resource should load growth or unit retirement assumptions vary
significantly from the base case view.
The Early Solid-Fuel Retirement case serves an additional purpose in that it quantifies the
value the coal units deliver to SWEPCO’s customers. The five pricing scenario optimization runs
did not have an option to retire existing units before their planned retirement dates. While the
solid-fuel units do contribute positive value (the energy value they provide is greater than their
cost), it is appropriate to investigate if an alternative portfolio that retires solid-fuel units
provides even greater value. Evaluating the Early Solid-Fuel Retirement portfolio under ‘Base’
pricing assumptions allows for an apples-to-apples comparison to the Optimized Base Portfolio.
When the CPW of the Base Portfolio is compared to the CPW of the Early Solid-Fuel Retirement
Portfolio, the Base Portfolio is $1.7 billion less expensive than the Early Solid-Fuel Retirement
Portfolio, as shown in Table 5-4.
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DRAFT 2015 Integrated Resource Plan
Table 5-3
Optimized Resource Plan Cumulative Additions at 5 Year Intervals Under Various Load/Retirement Scenarios
Pricing Scenario
Recip CT Firm Engine Firm CC Firm Capacity Capacity Capacity (MW)
(MW)
(MW)
Incremental Res Distributed Utility Solar Wind & Comm'l DSM Solar Firm Firm VVO Firm Firm Firm Capacity Capacity Capacity Capacity Capacity Total MW (MW)
(MW) (1) (MW) (2)
(MW)
(MW) (3) Additions
6. High Load
7. Low Load
8. Accel Gas Retirement
9. Early Solid Fuel Retirement
0
0
0
0
50
0
101
0
0
0
0
906
By 2020
36
14
53
45
1
1
1
1
17
0
25
0
38
26
38
38
10
0
30
0
153
41
248
990
6. High Load
7. Low Load
8. Accel Gas Retirement
9. Early Solid Fuel Retirement
0
0
157
0
50
0
151
0
453
0
0
1,359
By 2025
113
32
158
94
2
2
2
2
123
68
132
85
51
26
83
38
110
100
120
100
903
227
803
1,678
6. High Load
7. Low Load
8. Accel Gas Retirement
9. Early Solid Fuel Retirement
157
0
157
0
101
0
151
50
453
0
0
1,359
By 2030
212
125
246
204
3
3
3
3
229
174
238
191
73
73
83
92
160
150
170
160
1,388
525
1,047
2,059
6. High Load
7. Low Load
8. Accel Gas Retirement
9. Early Solid Fuel Retirement
157
0
157
0
101
0
151
50
453
0
906
1,359
By 2035
323
256
332
294
5
5
5
5
335
280
344
297
100
73
100
92
170
170
170
170
1,644
784
2,164
2,267
(1) Distributed Solar = 10% nameplate, (2) Utility Solar = 42.4% nameplate, (3) Wind = 10% namplate
Table 5-4
Plan Cost Comparison
CPW ($000)
Preferred Plan
Early Solid Fuel Retirement (1)
2015‐2040
14,777,300
16,151,226
End‐Effects
2,658,254
2,958,822
Study
Period
17,435,555
19,110,049
Cost Over Optimal Base Plan
Study
2015‐2040 End‐Effects
Period
‐
‐
‐
1,373,926
300,568
1,674,494
(1) Assumes Welsh 1 and Pirkey are retired by 1/1/2020
5.2.2.3 Base Portfolio EE, VVO and DG Results
5.2.2.3.1 Energy Efficiency Results
In the ‘Base’ pricing plan, incremental EE resources were selected. Overall, both Residential
and Commercial programs are providing over 750 GWh of energy savings and a peak capacity
reduction of 310 MW by the end of the planning period. The programs providing the majority of
the savings are Commercial Cooling, Commercial Lighting, Residential Water Heating and
Residential Shell Thermal programs. Figure 5-1, illustrates the EE savings relative to sales and
shows that by 2035, 6.0% of the potential sales are being reduced through potential incremental
107
DRAFT 2015 Integrated Resource Plan
EE programs under the Base pricing scenario. Figure 5-2 and Figure 5-3, illustrates the detailed
annual capacity and energy savings by modeled EE program. A table is also provided in the
appendix, Exhibit I, with the specific annual energy and capacity savings values for the Base
pricing scenario. Of the sixteen EE program bundles modeled only three failed to be selected;
Commercial Outdoor Lighting (High Achievable), Residential Cooling (High Achievable) and
Residential Appliances (High Achievable).
These three programs were the highest cost
programs that were modeled.
Figure 5-1: SWEPCO Model-Selected Incremental EE – Base Pricing Scenario
Incremental EE
By 2035, EE will reduce sales by 6.0%, with an average reduction of 0.3% per year.
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
20,000
18,000
16,000
14,000
G 12,000
W 10,000
H 8,000
6,000
4,000
2,000
‐
SWEPCO Residential & Commercial Sales
with Existing & Incremental EE
108
Embedded EE
R & C Sales
DRAFT 2015 Integrated Resource Plan
Figure 5-2: Model-Selected Incremental Energy Savings – EE Programs Under Base Pricing
SWEPCO 2015 IRP EE Potential by Program ‐ DRAFT (GWH)
800.0
700.0
Residential Appliances AP
Residential Water Heating AP
Residential Appliances HAP
600.0
Residential Water Heating HAP
Residential Lighting AP
Residential Lighting HAP
500.0
Residential Cooling AP
Residential Shell‐Thermal AP
Residential Cooling HAP
Residential Shell‐Thermal HAP
400.0
Commercial Outdoor Lighting AP
Commercial Indoor Lighting AP
Commercial Office Equipment AP
300.0
Commercial Outdoor Lighting HAP
Commercial Indoor Lighting HAP
Commercial Office Equipment HAP
200.0
Commercial Cooling AP
Commercial Cooling HAP
100.0
0.0
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Figure 5-3: Model-Selected Incremental Capacity Savings – EE Programs Under Base Pricing
SWEPCO 2015 IRP EE Incremental Potential by Program ‐ DRAFT (MW)
350.0
Residential Appliances AP
300.0
Residential Water Heating AP
Residential Appliances HAP
Residential Water Heating HAP
250.0
Residential Lighting AP
Residential Lighting HAP
Residential Cooling AP
Residential Shell‐Thermal AP
200.0
Residential Cooling HAP
Residential Shell‐Thermal HAP
Commercial Outdoor Lighting AP
Commercial Indoor Lighting AP
150.0
Commercial Office Equipment AP
Commercial Outdoor Lighting HAP
Commercial Indoor Lighting HAP
100.0
Commercial Office Equipment HAP
Commercial Cooling AP
Commercial Cooling HAP
50.0
0.0
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
109
2028
2029
2030
2031
2032
2033
2034
2035
DRAFT 2015 Integrated Resource Plan
5.2.2.3.2 VVO
In the ‘Base’ pricing plan, 8 of the 13 available VVO tranches were ultimately selected by
the model, with the first tranche of circuits added in 2017, a second tranche in 2018 and tranches
3 through 7 are added one tranche per year beginning in 2026 through 2030. Circuits where less
savings are expected optimize at different times, depending on the pricing or sensitivity scenario.
The “tranches” of VVO consist of circuits that provide both summer peak demand reduction and
significant annual energy reduction, as previously illustrated on Table 4-7. Figure 5-4 and
Figure 5-5 illustrate the schedule when VVO resources were optimized and selected, along with
the potential savings amounts per tranche for both peak demand and energy.
Model-Selected VVO Potential Savings – Under Base Pricing
Figure 5-4 Peak Demand
Figure 5-5 Energy
SWEPCO 2015 IRP VVO Potential ‐ DRAFT (MW)
SWEPCO 2015 IRP VVO Potential ‐ DRAFT (GWH)
400.0
120.0
350.0
100.0
VVO TR13
VVO TR13
300.0
VVO TR12
VVO TR12
VVO TR11
80.0
VVO TR11
VVO TR10
VVO TR09
VVO TR10
250.0
VVO TR09
VVO TR08
VVO TR07
M
60.0
W
VVO TR08
VVO TR06
G
W200.0
VVO TR05
h
VVO TR07
VVO TR06
VVO TR05
VVO TR04
VVO TR03
VVO TR04
VVO TR03
150.0
VVO TR02
40.0
VVO TR02
VVO TR01
VVO TR01
100.0
20.0
50.0
0.0
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
0.0
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
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DRAFT 2015 Integrated Resource Plan
The VVO estimates are subject to future revision as more operational information is gained
from installations that are currently underway throughout the AEP system.
5.2.2.3.3 Distributed Solar
Distributed solar resources were not optimized under any economic scenario during the
planning period. Distributed rooftop solar was included as a resource based on historical
additions for SWEPCO, the continued decline in the installed cost of solar resources and the
ongoing Louisiana Residential rooftop solar incentive. Figure 5-6 below, illustrates the
embedded rooftop solar and well as the forecasted DG solar additions that were trended from the
installation history.
Figure 5-6
SWEPCO Cumulative Rooftop Solar Additions
Installed Cap (MWac)
60
50
40
30
20
10
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
0
SWP LA
SWP AR
SWP TX
SWP Total
SWP LA
SWP AR
SWP TX
SWP Total
5.2.2.4 EE/VVO/DG Discussion
Incremental EE programs as described above have been selected in the base plan. The
model selected both Residential and Commercial programs. Ultimately regulatory approval of
new incremental EE programs will be needed to achieve these identified resource levels.
While DG was not optimized within the model, new DG was included in the resource mix at
somewhat aggressive levels when compared to historical DG additions. As previously discussed,
111
DRAFT 2015 Integrated Resource Plan
DG cost and performance characteristics continue to improve and continued monitoring of this
technology is required and will be reflected in future IRPs.
5.2.3 Proposed CPP Rule – Impact on IRP
The EPA’s CPP remains a proposed rule. Over two million comments have been submitted
to the EPA regarding the proposed plan since it was made public in June, 2014. While SWEPCO
cannot predict how EPA will incorporate those comments into a final rule, it is likely that the
final rule will be different than the proposed rulemaking. In addition, SIPs may set compliance
requirements that will influence the actions available to SWEPCO, which are not knowable at
this point in time. Therefore, preparing a resource plan that meets the proposed rule has limited,
if any, value.
The CPP requires states to submit SIPs that meet carbon emission intensity goals (pounds of
CO2 emitted from effected sources per MWh) developed by the EPA for each state. Because
SWEPCO has affected resources in three states, and each state has different carbon intensity
goals, designing a compliant resource plan has significant challenges. A number of the building
blocks used by EPA to set the state goals, specifically the re-dispatch of natural gas plants and
the incremental generation associated with at-risk nuclear units, are not available to SWEPCO in
all three states. As a result, SWEPCO does not know if the ultimate SIPs will require compliance
with carbon intensity goals that will be equal to, or different from, the overall state goals set by
EPA. As illustrated in Figure 5-7, while SWEPCO’s Preferred Portfolio reduces carbon intensity
by over 38% below 2005 levels, the state goals are significantly below even this level.
To meet the CO2 intensity reduction requirements of the CPP, EPA assumes coal/solid-fuel
plant retirements and curtailments, and significant EE and renewable resource additions. To meet
reliability criteria and customer energy requirements, the retired/curtailed coal capacity would
likely be replaced by NGCC capacity as early as 2020. SWEPCO has not evaluated the
transmission infrastructure or reliability issues that may arise from unit retirements/curtailments
or the addition of renewable resources associated with meeting the EPA-proposed CPP goals.
The Company also did not evaluate if there would be adequate gas supply to serve any new
NGCC capacity.
112
DRAFT 2015 Integrated Resource Plan
Figure 5-7
SWEPCO Projected "Carbon Intensity" Under the Base Plan CO2 emissions from affected existing fossil generation (lb.) / [Affected existing fossil generation (Mwh) + Non‐hydro renewable generation (Mwh) + incremental energy efficiency (Mwh)]
2100
2000
Based on PRELIMINARY Resource Planning Profile
2046
1934
(2005A)
1900
(2012A)
1800
1700
15.3% Reduction (from 2005A); 1,838 1,843 1,795 1,793 10.4% Reduction (from 2012A)
1,753 1,764 1,732 1,681 1,613 1600
1,532 1,494 1500
1,412 1,400 38.3% Reduction (from 2005A); 1,367 34.7% Reduction (from 2012A)
1,324 1,313 1,263 1,259 1,250 1,225 1,207 1,187 1,201 1,191 1,170 1,182 1,131 CO2 lb. per Mwh
1400
1300
1200
1100
1,028 1,017 1,003 989 974 959 946 933 921 910 910 910 910 910 910 910 910 910 910 910 910 1,015 1,002 988 972 956 939 930 914 924 909 896 878 896 883 883 883 883 883 883 883 883 883 883 883 883 859 841 824 807 795 791 791 791 791 791 791 791 791 791 791 791 791 1000
900
800
700
600
500
400
SWEPCO Projected (Avg. Total Co.)
300
Arkansas ‐ Proposed 111(d) Targets
200
Louisiana ‐ Proposed 111(d) Targets
100
Texas ‐ Proposed 111(d) Targets
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2012(A)
2005(A)
0
Recognizing that states have between one to three years from the issuance of the final CPP
rule to submit a SIP, and EPA has an additional year to approve the SIPs, it is not feasible to
assume new NGCC capacity could be brought on line by 2020. To do so, a utility would have to
receive regulatory approval to begin construction up to three years before SIP approval is
received. Therefore, while SWEPCO could hypothesize as to the components of a portfolio that
meets the CPP emission intensity limit, such a portfolio could not be planned for, authorized and
implemented in the proposed timeframe.
5. 3 Risk Analysis
In addition to developing the Preferred Portfolio based on the discrete, optimized portfolio
created under Base pricing assumptions, the Preferred and “Early Solid-Fuel Retirement”
portfolios were evaluated using a stochastic, or Monte Carlo modeling technique where input
113
DRAFT 2015 Integrated Resource Plan
variables are randomly selected from a universe of possible values, given certain standard
deviation constraints and correlative relationships. This offers an additional approach by which
to “test” the Preferred Plan over a distributed range of certain key variables. The output is, in
turn, a distribution of possible outcomes, providing insight as to the risk or probability of a
higher cost (revenue requirement) relative to the expected outcome.
This study included multiple risk iteration runs performed at five year intervals with three
key price variables (risk factors) being subjected to this stochastic-based risk analysis. The
results take the form of a distribution of possible revenue requirement outcomes for each plan for
each year. Table 5-5 shows the input variables or risk factors within this IRP stochastic analysis
and the historical correlative relationships to each other. The range of values associated with the
variable inputs is shown in Figure 5-8.
Table 5-5: Risk Factors and Their Relationships
Natural Gas
Coal
Power
Standard Deviation
Natural Gas
1
Coal
0.18
1
Power
0.47
0.53
1
19.0%
6.4%
14.7%
The Preferred Portfolio was evaluated and compared to Early Solid-Fuel Retirement
Portfolio, which adds significant NGCC replacement capacity by 2020 but has lower levels of
DSM and Utility Solar than the Preferred Portfolio. This portfolio was selected to provide a
distinctly different resource profile, and therefore different revenue requirements, than those in
the Preferred Portfolio.
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DRAFT 2015 Integrated Resource Plan
Figure 5-8: Variable Input – Range of Values
Natural Gas $/mmBTU
$25.00
$20.00
$15.00
$10.00
$5.00
$0.00
2016
2021
Low
2026
High
2031
2036
Mean
5.3.1 Stochastic Modeling Process and Results
For each portfolio, for the three representative periods evaluated, the differential between
the median and 95th percentile result from the multiple runs was identified as Revenue
Requirement at Risk (RRaR). The 95th percentile is a level of required revenue sufficiently high
that it will be exceeded, assuming the given plan is adopted, only five percent of the time. Thus,
it is 95% likely that those higher-ends of revenue requirements would not be exceeded. The
larger the RRaR, the greater the likelihood that customers could be subjected to higher costs
relative to the portfolio’s mean or expected cost. Conversely, there is equal likelihood costs may
be lower than the median value. These higher or lower costs are generally the result of the
difference, or spread, between fuel prices and resultant SPP market energy prices. The greater
that spread, the more “margin” is enjoyed by the Company and its customers. Figure 5-9
115
DRAFT 2015 Integrated Resource Plan
illustrates the RRaR and the expected value graphically displayed for evaluated years 2021,
2026, and 2031.
Figure 5-9: RRaR and Expected Value
2021 Risk Profile For Selected Plans
$ in 1000s
$2,000,000
RRaR Early Coal Retirement Portfolio
$1,800,000
$1,600,000
$1,400,000
RRaR‐ Preferred Portfolio
$1,200,000
$1,000,000
$800,000
$600,000
$400,000
$200,000
$0
1%
8%
15%
21%
28%
35%
41%
48%
Early Coal
55%
61%
68%
75%
81%
88%
95%
Preferred
2026 Risk Profile for Selected Plans
$ in 1000s
$3,000,000
$2,500,000
RRaR Early Coal Retirement Portfolio
$2,000,000
RRaR‐ Preferred Portfolio
$1,500,000
$1,000,000
$500,000
$0
1%
8%
15%
21%
28%
35%
41%
48%
Early Coal Retirement Plan
116
55%
61%
68%
Preferred Plan
75%
81%
88%
95%
DRAFT 2015 Integrated Resource Plan
Figure 5-9: RRaR and Expected Value (cont’d)
2031 Risk Profile forSelected Plans
$ in 1000s
$3,000,000
RRaR Early Coal Retirement Portfolio
$2,500,000
RRaR‐ Preferred Portfolio
$2,000,000
$1,500,000
$1,000,000
$500,000
$0
1%
8%
15%
21%
28%
35%
41%
48%
Early Coal
55%
61%
68%
75%
81%
88%
95%
Preferred
The differences in RRaR between the portfolios are summarized in Table 5-6 which
illustrates the over $250 million in annual savings between the Preferred Portfolio and the Early
Solid-Fuel Retirement Portfolio.
The addition of NGCC plants, which have greater load
following capability and operate a lower capacity factors than coal plants, works to slightly
reduce the risk or revenue requirement volatility in the Early Solid-Fuel Retirement Portfolio.
The order of magnitude of this difference is relatively small when compared to the overall cost of
the Early Solid-Fuel Retirement Portfolio versus the Preferred Portfolio, as is readily apparent in
Figure 5-9. This relative cost difference is due, in large part, to the carrying cost of
approximately $200 million/year on the incremental capital investment in new NGCC plants
required to replace the retiring coal plants. Also note that the Early Solid-Fuel Retirement
Portfolio has less upside potential (in the form of lower costs versus the mean), than the
Preferred Portfolio.
117
DRAFT 2015 Integrated Resource Plan
Table 5-6
Revenue Requirement at Risk
$ in1000s
2021
Preferred Portfolio 192,399
Early Coal Retirement Portfolio 108,705
Risk Premium (Preferred vs Early Coal) 83,694
Expected Savings (Preferred vs Early Coal) 307,401
Expected Savings vs. Risk Premium Multiple
3.67 X
2026
337,169
244,374
92,795
275,649
2.97 X
2031
420,450
312,670
107,780
267,060
2.48 X
In comparing the RRaR associated with the Preferred Portfolio relative to the Early SolidFuel Retirement portfolio in the sample years 2021, 2026 and 2031, the differences appear to
grow over time (partially due to the incorporation of an assumed carbon tax in 2022), however,
this difference, or “Risk Premium”, is well below the large cost difference between the two
portfolios. The “option value” of a risk premium is typically considered to be the amount of
money which an entity would consider spending—over-and-above the modeled discrete
(intrinsic) value—in order to preserve or “hold” the riskier portfolio. However, in this case the
expected discrete savings associated with the Preferred Portfolio, for each year analyzed, is
significantly greater than the identified risk premium (RRaR) which would indicated the
Preferred Portfolio remains clearly the superior alternative. A simpler explanation may be, using
2026 as an example, that a reasonable person would not pay over $275 million in additional costs
(by picking the Early Solid-Fuel Retirement Portfolio) to avoid the risk of potentially incurring
less than $93 million in incremental cost under the Preferred Portfolio.
Based on the risk modeling performed, it is reasonable to conclude that the Preferred
Portfolio represents a reasonable combination of expected costs and risk relative to the cost-risk
profile of the Early Solid-Fuel Retirement portfolio, which has more significant near-term capital
expenditures.
5.4 Preferred Portfolio Selection
The Preferred Portfolio is the Plexos® model-optimized portfolio established under ‘Base’
long-term commodity pricing and ‘Base’ load forecast. This plan adequately addresses
SWEPCO’s need for capacity and energy, and includes the following practical considerations.
118
DRAFT 2015 Integrated Resource Plan

It defers the need for investment in capital-intensive fossil generation until 2023.

It recognizes and includes the benefit of customer-owned solar generation. While
not selected as an optimal resource based on the utility’s cost perspective, given
the economics from the perspective of individual customers under current net
metering provisions, it is reasonable to expect some (albeit small in the context of
SWEPCO’s overall long-term resource needs) level of adoption of this resource
by SWEPCO customers.

Economical VVO and incremental customer-based EE programs were added, the
latter being over-and-above the levels of EE resources already incorporated into
SWEPCO’s load forecast. These programs are a key factor in delaying the need
for natural gas fired peaking capacity.

Utility scale solar capacity and additional wind energy are added beginning in
2020, recognizing the declining cost trend associated with each of these
technologies.
The Preferred Portfolio offers SWEPCO significant flexibility should future conditions
differ considerably from its assumptions. For example, as EE programs are implemented,
SWEPCO will gain insight into customer acceptance and develop hard data as to the impact
these programs have on load growth. This will assist SWEPCO in determining whether to
expand program offerings, change incentive levels for programs, or target specific customer
classes for the best results. If current long-term renewable costs assumptions ultimately increase,
SWEPCO could either accelerate or delay a more traditional new peaking capacity build, which
has a relatively short lead time to implement. If load growth is significantly greater than planned,
or if a large number of older gas-steam units are forced to retire sooner than already planned,
SWEPCO could consider adding a NGCC facility in place of peaking capacity. Likewise, if
ultimate CPP---or other solid-fuel inhibiting---rulemaking dictate the early depletion of low-cost
solid-fuel capacity from SWEPCO’s portfolio, the prospect of the addition of one (or more)
NGCC facilities sometime next decade could be reconsidered.
119
DRAFT 2015 Integrated Resource Plan
In sum, the Preferred Portfolio allows SWEPCO the flexibility to adapt to changes in
assumptions without being locked into costly near term capacity additions. Changes to
SWEPCO’s existing portfolio associated with this Preferred Portfolio are described in greater
detail in Section 6 of this report.
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DRAFT 2015 Integrated Resource Plan
6.0 Conclusions and Recommendations
6.1 Capacity and Energy Plan
SWEPCO’s current capacity resource position for the 2015 SPP planning year is illustrated
in the following TABLE 6-1, which is a summary of Exhibit D:
TABLE 6-1
2015 Peak Demand and Capability
(Projected) Peak Demand (MW)
5,259
(CDR Section “7A”;)
Less: DSM-DR
93
(CDR Sections “7B & 7D”;)
Less: AEP-West Load Diversity (w/ PSO)
24
(CDR Sections “7F”)
Plus: Sales with Reserves
54
(CDR Section “8”)
175
(CDR Section “9”)
Less: Purchases with Reserves
(A) = Load Responsibility
5,022 MW (CDR Section “10”)
Generating Capability (MW)
5,685
Less: Sales without Reserves
18
(CDR Section “4”)
Plus: Purchases without Reserves
641
(CDR Section “5”)
(B) = Total Capability
(CDR Sections “1 & 2” )
6,308 MW (CDR Section “6”)
(C) = Reserve Capacity MW = (B)-(A)
1,286
(CDR Section “11”)
(D) = Reserve Margin % = (B)/(A)-1
25.6%
(CDR Section “12”)
(E) = Capacity Margin % = (C)/(B)
20.4%
(CDR Section “13”)
(F)= Reserve above Min. Cap. Margin
601 MW (CDR Section “14”)
(F) = ( B)-[(A)/(1-12%)]
This single-year (2015) result excerpted from Exhibit D in the Appendix indicates that
SWEPCO is currently expected to have a modest (601 MW) capacity margin above the
minimum SPP criterion by virtue of its “Total Capability” exceeding its “Load Responsibility”
by 20.4 percent, or above that 12 percent SPP-required minimum. This margin is “modest”
121
DRAFT 2015 Integrated Resource Plan
considering that in the subsequent (2016) SPP planning period, SWEPCO is scheduled to retire
the 528-MW Welsh Unit 2.
6.2 Plan Summary
The optimization results and associated risk modeling of this IRP demonstrate that for
SWEPCO, as a stand-alone entity in the SPP RTO, the addition of wind, solar, and customer and
grid energy efficiency resources serves to reduce overall costs relative to other alternatives. The
Preferred Portfolio results in reasonable costs when compared to other portfolios while reflecting
a level of distributed (solar) generation that is reasonable to expect will emerge under current
cost assumptions and net metering arrangements. The following are summary highlights of the
Preferred Portfolio.

Maintains, upon recognition of required and potentially-emerging environmental
control investments, SWEPCO’s solid fuel units at Welsh Units 1 & 3, Flint Creek
and Pirkey, in addition to its share of energy and capacity from the non-SWEPCO
operated Dolet Hills unit.

Retires Welsh Unit 2 in 2016.

Retires 722 MW of older gas-steam units beginning in 2016 through 2030.

Adds 200 MW per year of wind energy beginning in 2021, reduced to 100 MW per
year for 2027 through 2031; for a total of 1,700 MW (nameplate) of wind over the
20-year planning period.

Implements customer and grid energy efficiency, including Volt VAR Optimization
(VVO) programs so as to reduce energy requirements by 1,121 GWh (or 5% of
projected energy needs) and capacity requirements by 410 MW by 2035.

Adds 50 MW per year of utility-scale solar energy beginning in 2020; for a total of
800 MW (nameplate) of utility-scale solar over the 20-year planning period.

Recognizes additional distributed solar capacity will be added by SWEPCO’s
customers, starting in 2015, of about 1 MW (nameplate) and ramping up to about 50
MW (nameplate) by 2035.

Adds 207 MW of natural gas peaking resources over the 20-year planning period; 50
MW of reciprocating engine technology in 2023 and 157 MW of small frame
technology in 2025.

Continues operation of SWEPCO’s newest plant additions – the environmentallycompliant Turk solid-fuel unit, as well as the Stall natural gas combined cycle (CC)
and Mattison natural gas simple cycle (SC) facilities.
122
DRAFT 2015 Integrated Resource Plan
Specific SWEPCO capacity and energy production changes over the 20-year planning
period associated with the Preferred Portfolio are shown in Figure 6-1 and Figure 6-2,
respectively.
Figure 6-1
SWEPCO SPP Capacity Changes
123
DRAFT 2015 Integrated Resource Plan
Figure 6-2
SWEPCO Energy Production Changes
124
DRAFT 2015 Integrated Resource Plan
Figures 6-1 and 6-2 indicate that this Preferred Portfolio would reduce SWEPCO’s reliance
on solid fuel-based and gas-steam generation as part of its portfolio of resources, and increase
reliance on demand-side and renewable resources, thereby enhancing fuel diversity. Specifically,
over the 20-year planning horizon the Company’s capacity mix attributable to solid fuel-fired
assets would decline from 46% to 31%, and gas-steam assets decline from 25% to 13%. Newer
combined cycle and peaking gas assets edged up from 12% to13%, renewables (wind, utility and
distributed solar, based on nameplate ratings) increase from 6.9% to 37%, and, similarly, demandside and energy-efficiency measures increase from 1% to 6% over the planning period.
SWEPCO’s energy output attributable to solid fuel-fired generation shows a significant decrease
from 81% to 49% over the period. The Preferred Portfolio highlights the fact that the Company
can meet its future requirements without more capital intensive baseload generation, relying
instead on utility and customer owned renewable resources, demand-side activity, and a modest
amount of peaking capacity. Moreover, the layers of carbon-free energy resources being added as
part of this planning process would serve to hedge SWEPCO’s exposure to natural gas price and
SPP energy market volatility, while producing a lower cost solution than one that includes greater
reliance on new gas assets.
Figure 6-3 and Figure 6-4 show the changes in capacity and energy mix on an annual basis,
relative to capacity and energy requirements. Again, recognizing that renewable resources are
“intermittent” in nature and, with that, are only recognized by SPP—for purposes of meeting
reserve margin criterion—for a small percentage of their full nameplate ratings when
determining “firm” capacity; the SPP capacity contribution from renewable resources is fairly
modest. However, such renewables resources can provide a significant volume of energy,
specifically when attributable to wind. SWEPCO’s Plexos® optimization modeling selected those
wind resources because they add more relative value (i.e., lowered SWEPCO’s net energy cost)
than alternative resources, including the purchase of energy from the SPP market.
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DRAFT 2015 Integrated Resource Plan
Figure 6-3
SWEPCO SPP Capacity Position
7,000
SWEPCO Preferred Plan Capacity Position
*DSM shown as a resource, not a load reduction
*Renewables capacity at SPP rating, not nameplate
6,000
5,000
DSM/EE/Inter
4,000
Net Purchases
MW
Renewables/DG
Gas Steam
Gas CT/CC
3,000
Solid Fuel
Load Obligation (Excl DSM)
2,000
1,000
0
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Figure 6-4
SWEPCO Energy Position
40,000
SWEPCO Preferred Plan Energy Position (GWh)
*DSM shown as a resource, not a load reduction
35,000
30,000
25,000
EE/VVO
GWh
Solar (utility +DG)
Wind
20,000
Gas CC/CT
Gas Steam
Solid Fuel
Load w/o EE
15,000
10,000
5,000
0
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
To provide additional granularity with regard to SWEPCO’s energy position under the
Preferred Portfolio, Figure 6-5 and Figure 6-6 show the monthly energy mix for 2020 and 2025,
respectively. As can been observed from the monthly data, the addition of renewable resources
126
DRAFT 2015 Integrated Resource Plan
significantly reduces SWEPCO’s need to rely on market purchases and provides an opportunity
to capitalize on market sales.
Figure 6-5
SWEPCO 2020 Energy Position – By Month
3,500
2020 SWEPCO Preferred Plan Gen Vs. Load
By Month
3,000
2,500
EE/VVO
Solar
2,000
GWh
Wind
Gas Steam
1,500
CC/CT Gas
Solid Fuel
1,000
Load
500
‐
Jan‐20
Feb‐20
Mar‐20
Apr‐20
May‐20
Jun‐20
Jul‐20
Aug‐20
Sep‐20
Oct‐20
Nov‐20
Dec‐20
Figure 6-6
SWEPCO 2025 Energy Position – By Month
3,500
3,000
2025 SWEPCO Preferred Plan Gen Vs. Load
By Month
2,500
EE/VVO
Solar
2,000
GWh
Wind
Gas Steam
1,500
CC/CT Gas
Solid Fuel
1,000
Load
500
‐
Jan‐25
Feb‐25
Mar‐25
Apr‐25
May‐25
Jun‐25
Jul‐25
Aug‐25
Sep‐25
Oct‐25
Nov‐25
Dec‐25
The following Table 6-2 provides a summary of the Preferred Portfolio which resulted
from resource optimization modeling under the “Base” case commodity pricing scenario:
127
128
2030
2031
2032
2033
2034
2035
10
11
12
13
14
15
16
17
18
19
20
(F)
(1,194)
(1,194)
(1,194)
(1,194)
(1,194)
(1,194)
(916)
(1,026)
(1,026)
(1,026)
(1,026)
(L)
(K)
(J)
(I)
(H)
(G)
157
157
157
157
157
157
157
157
157
157
157
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
MW
MW
‐
(61)
(589)
(589)
(589)
(589)
(699)
(699)
(699)
(808)
(808)
(Frame) CTs
Coal & Gas‐
Steam
RETIREMENTS
(Cumulative)
(2)
101
101
101
101
101
101
50
50
50
50
50
‐
‐
‐
‐
‐
‐
‐
‐
‐
50
50
MW
Recip Engines
New‐Build
(3)
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
MW
NGCC
(4)
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
‐
126
126
126
126
126
‐
‐
‐
‐
MW
(ST) PPA
(5)
(7)
(8)
(9)
35
35
35
35
35
6
12
17
22
25
28
31
33
34
35
35
MW
109
128
145
168
196
‐
‐
‐
8
18
28
36
56
77
97
103
MW
New
51
63
73
83
92
‐
‐
‐
26
38
38
38
38
51
51
51
VVO
376 35 221 100
387 35 240 100
397 35 256 100
409 35 274 100
419 35 292 100
429 35 314 100
567
337
'TOTAL' Energy Efficiency (2016‐3035)
319
331
342
352
363
183
198
208
220
231
242
253
267
281
295
307
MW
'Embedded' Federal EE Existing DSM Regulations (B) Programs(C) Energy Efficiency (EE)
(C) 81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
MW
Programs
Existing DSM DR
(10)
160
170
170
170
170
170
100
120
130
140
150
‐
‐
‐
‐
‐
‐
‐
20
40
60
80
MW
Wind (D)
(11)
(12)
Solar(E)
(13)
233
255
276
297
318
339
127
148
170
191
212
‐
‐
‐
‐
‐
‐
21
42
64
85
106
MW
3.4
3.7
4.0
4.3
4.7
5.0
2.1
2.4
2.6
2.9
3.1
0.1
0.4
0.5
0.7
0.8
1.0
1.1
1.3
1.5
1.7
1.9
MW
Utility‐Scale Distributed
Preferred Portfolio
(Cumulative) Firm Capacity Resource ADDITIONS
(6)
(103)
(52)
(15)
24
64
107
(203)
(241)
(183)
(118)
(49)
87
32
(365)
(326)
(300)
(287)
(364)
(427)
(351)
(347)
(300)
MW
CHANGE
(P) (Cumul.)
NET
'RESOURCE' (14)=(1)to(13), ex(6)
(16)
110
134
159
158
168
194
157
90
118
165
188
784
602
121
105
299
224
262
91
127
83
108
MW
Above
SPP Minimum Rqrmnt(Q) 16.1%
16.7%
17.2%
17.2%
17.4%
18.0%
17.2%
15.7%
16.3%
17.4%
17.9%
29.9%
25.6%
16.0%
15.7%
20.5%
18.7%
19.7%
15.7%
16.6%
15.5%
16.1%
Margin %
SWEPCO Reserves
Resulting
(15)
(Q)
Excludes cumulative annual changes in SWEPCO SPP 'Load Responsibility' (coincident peak demand) and 3rd‐party resources… which also impacts relative capacity resource position.
SPP minimum criterion @ 13.6% as a function of peak demand.
(P)
(C)
(B)
SPP Planning Year is effective 6/1/XXXX.
Represents estimated energy efficiency levels already 'embedded' into SWEPCO's long‐term load & peak demand forecast based on emergence of PRIOR‐ESTABLISHED Federal efficiency standareds (EPAct 2005; 2007 EISA, 2009 ARRA). Represents estimated contribution from current/known SWEPCO DSM‐EE and Demand Response (Interruptible, DLC/ELM) program activity also reflected in the Company's long‐term load and demand forecast (from 'Going‐In' SWEPCO CDR) .
(D)
Due to the intermittency of wind resources, only 10% of wind resource 'nameplate' MW rating are included for capacity resource determination purposes.
(E)
Due to the intermittency of solar resources, only 42.4% of solar resource 'nameplate' MW rating are included for capacity resource determination purposes.
RETIREMENTS:
(F)
(L)
Lieberman 1 retirement & Knox Lee 4 50% derate assumed 12/2014.
Wilkes 1 retirement assumed 12/2029.
(G)
(M)
Welsh Unit 2 retirement effective approximately June 1, 2016.
Wilkes 2 retirement assumed 12/2035.
(H)
Lieberman 2, Lone Star & Knox Lee 4 retirement assumed 12/2019.
(I)
Lieberman 3 retirement assumed 12/2022.
(J)
Lieberman 4 retirement assumed 12/2024.
(K)
Arsenal Hill 5 retirement assumed 12/2025.
(A)
2025
2026
2027
2028
2029
1
2
3
4
5
6
7
8
9
Year(A)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
(LA) IRP Period
Yr.
SPP Planning MW
(1)
(DRAFT) 2015 Integrated Resource Plan
Cumulative Resource Changes
Southwestern Electric Power Company
(18)
(19)
1,600
1,700
1,700
1,700
1,700
1,700
1,000
1,200
1,300
1,400
1,500
‐
‐
‐
‐
‐
‐
‐
200
400
600
800
MW
MW
21.0
23.5
26.0
28.5
31.0
1.0
3.5
5.0
6.5
8.0
9.5
11.0
13.0
15.0
17.0
19.0
550 34.0
600 37.0
650 40.0
700 43.0
750 46.5
800 50.0
847
'TOTAL' Solar (2016‐2035)
300
350
400
450
500
‐
‐
‐
‐
‐
‐
51
100
150
200
250
MW
Utility‐Scale Distributed
(Cumulative) 'NAMEPLATE' ADDITIONS
(D)
Wind
Solar(E)
(17)
DRAFT 2015 Integrated Resource Plan
Table 6-2
DRAFT 2015 Integrated Resource Plan
6.2.1 SWEPCO Five Year Action Plan
Steps to be taken by SWEPCO in the near future to implement this plan include:
1. Begin (or continue) the planning and regulatory actions necessary to
implement economic energy efficiency programs in each state SWEPCO
serves.
2. Continue to investigate market prices for renewable resources.
3. Consider opportunities to pilot utility-scale solar projects to acquire
implementation and operational experience.
4. Seek out opportunities for economic additions of wind generation.
5. Continue to evaluate gas-steam unit ongoing operating and maintenance
costs, in addition to equipment liability issues to determine most likely
candidates for near term retirements.
6. Complete solid fuel plant retrofit projects already underway.
7. Monitor the status of GHG rules and state implementation plans to be
ready to adjust future actions accordingly.
6.3 Conclusion
Exhibit D represents the “Going-in” capacity position before the ultimate determination of
how capacity deficiencies would be met.
SWEPCO has set forth a Plan that meets the
requirements of its customers in a least reasonable cost fashion as reflected in Exhibit E.
The pursuit of renewable resources has significant economic advantages, particularly after
considering the relative impacts associated with three of the more critical “driving” economic
risk parameters, the potential future cost of natural gas, the timing of CO2/carbon pricing, and the
future costs to construct the available options. In addition, the Company continues to operate
demand-side programs in its Arkansas and Texas jurisdictions. SWEPCO will continue to
evaluate supply and demand-side options to meet the long-term needs of its customers in a costeffective and reliable manner.
Inasmuch as there are many assumptions, each with its own degree of uncertainty, which
had to be made in carrying out the resource evaluations, changes in these assumptions could
result in modifications in the resource plan reflected for SWEPCO. The resource plan presented
129
DRAFT 2015 Integrated Resource Plan
in this IRP is sufficiently flexible to accommodate possible changes in key parameters, including
load growth, environmental compliance assumptions, fuel costs, and construction cost estimates.
As such, changes and assumptions are recognized, updated, and refined, with input information
reevaluated and resource plans modified as appropriate.
This 2015 SWEPCO IRP provides for reliable electric utility service, at reasonable cost,
through a combination of existing resources, renewable energy and demand-side programs.
SWEPCO will provide for adequate capacity and energy resources to serve its customers' peak
demand, energy requirement and required SPP reserve margin needs throughout the forecast
period.
130
DRAFT 2015 Integrated Resource Plan
APPENDIX
SUPPORTING TABLES
131
DRAFT 2015 Integrated Resource Plan
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132
DRAFT 2015 Integrated Resource Plan
Exhibit A: Stakeholder Comments and Responses
STAKEHOLDER
Topic
1 DSM
Alliance for Affordable Energy
Sierra Club
Evaluate DSM Potential
Model distributed generation and energy efficiency as resources not as exogenous demand reduction
Fully Model DSM & Supply Resources (SR)
Disclose the costs of EE and the underlying assumption
Southern Wind Energy Association
The declining price of renewable resources must be included in forward‐looking modeling
Fully acount for costs &benefits of DSM and All modeling runs should meet any mandated EE, DSM and SR renewable goals (goals should be the minimum)
Use Cost‐Benefit Tests
Develop a supply curve for EE and disclose for Commission and Stakeholders
Provide assumptions made and supporting documentation about state net metering policy
2 Renewable Energy Resources
Cost Estimates for Wind Energy
Include reasonable assumptions for anticipated decreases Use SWEA's wind energy inputs provided in Figure 1
in production cost and increases in capacity factors
Use State‐of‐the‐Art Technology to establish Reasonable Capacity factors use near‐term utility‐scale wind energy resources : SPP/HVDC, MISO West and Onshore Louisiana resources. MISO West and SPP/HVDC all‐in cost should be close to $40‐$50/MWh and $44 for Onshore LA.
favors SWEPCO's current plan to use a declining wind energy cost scale over time. wind energy capacity factors should increase while LCOEs should decrease over the IRP timeframe. Model LA Coastal, Upland and Out‐of‐
Region Wind separately
3 Solar
Use NREL Cost and Capacity Factors
4 Net Metering
Provide assumptions made and supporting documentation about state net metering policy
5 Combined Heat and Power Consider opportunities for investment in CHP 6 Carbon Pricing
Accelerate timeframe for inclusion of carbon price in model.
Use nonzero CO2 price in all scenarios except a single scenario
Carbon price between $11.60 and $34.00/tonne starting in 2016
Update CO2 price forecast using a range of reference forecasts, including more sophisticated forecast by industry experts such as Synapse Energy Economics
Consider sensitivities independently of other variables and then in combination with select variables
Social cost for Carbon
Modeling CO2 should influence unit dispatch and not be treated as an after‐the ‐fact cost
7 Risk and Reliability Criteria Further dialogue, provide a table capturing primary risk and reliability considerations for each resource option, never justify a resource on basis of risk or reliability over cost without a clear set of criteria being used to support finding 8 Model Structure and Sensitivity vs. Scenarios
Favor sensitivities over scenarios that fix multiple variables
Decouple commodity prices, emission prices and other assumptions. Choose the most important sensitivities and provide reasonable corner member of these sensitivities
For variables with high, mid and low forecasts, run sensitivities that are independent of the other variables and then in combination with other variables to combine uncertainties
Allow model to pick partial blocks of resources and pick reasonable blocks of other resources where capacity can be shared between utilities
133
DRAFT 2015 Integrated Resource Plan
Exhibit A: IRP Stakeholder Comments (Cont’d)
STAKEHOLDER
Topic
9 Environmental Regulations and Compliance Risk
Alliance for Affordable Energy
Sierra Club
Southern Wind Energy Association
Include an analysis of multiple environmental compliance sensitivities, including schedules, pollutant categories, timing and cost
Sensitivities for each potential regulation should be conducted independently of other variables
as required by LPSC, develop an environmental compliance scenario that models alternative environmental compliance futures
10 Retirement Potential
Allow model to determine unit retirement decisions endogenously
Make retirement decisions in the context of portfolio replacement options rather than single one‐off replacement assumptions
Develop decommissioning estimates with stakeholder and commission input and represent in terms of net costs
11 Transparency and Reporting Requirements
Provide documentation for historical data and assumptions that are enumerated in the PSC order
Provide all data points used for wind energy with references and reflect current market prices and practices
Provide all data at the first step of the stakeholder engagement process update promptly throughout the process
Use actual costs as benchmarks for model inputs. EIA data tends to overestimate cost while underestimating performance of wind resources Only items that are truly sensitive business information should be treated as such
12 Action Plan and Sustainability
134
DRAFT 2015 Integrated Resource Plan
Exhibit A: IRP Stakeholder Comments (Cont’d)
STAKEHOLDER
Topic
Gulf States Renewable Energy Industries Association
Entegra Power Group
1 DSM
The declining price of renewable resources must be included in forward‐looking modeling.
Southeast Energy Efficiency Alliance
Considering EE as a resource provides an opportunity to achieve Cost info provide on slide 23 (EE cost expected to increase) runs counter to national cost trajectories. (ACEEE study)
2 Renewable Energy Resources
A lack of early first‐hand experience by SWEPCo with integrating solar and wind energy technologies into the supply mix will be a liability to ratepayers, keeping costs and volatility high unnecessarily
Refer to the National Renewable Energy's website for important considerations that are unique to renewable generation technology. Higher capacity factor when combined with geographic and technological smoothing designs, employing time‐of‐
use metering when high peak demand is a concern, integrate PV, CSP in a pattern that optimizes the correlation of load patterns, resulting in a westerly facing array.
3 Solar
DG sources (especially installed DG solar) are becoming more prevalent and will impact system planning beyond simple demand side management considerations. The contribution to peak capacity cannot be ignored.
4 Net Metering
5 Combined Heat and Power
6 Carbon Pricing
Reflect fast‐moving CO2 guideline timeline. Begin to phase in CO2 cost in 2020, rather than 2021.
7 Risk and Reliability Criteria
8 Model Structure and Sensitivity vs. Scenarios
9 Environmental Regulations and Compliance Risk
10 Retirement Potential
11 Transparency and Reporting Requirements
12 Action Plan and Sustainability
Conduct a technical conference and RFC specific to a proper renewable resource analysis would be an appropriate action at this time Entegra is interested in the specific implementation actions
required during the first five years of the planning period.
Entegra looks forward to the RFP solicitation process and
timetable to meet any resource needs. 135
Sustainability (the fourth priority that emerges when price stability, reduced quality of life and environmental inertness of clean energy sources are considered) is a crucial consideration that reflects true long‐term planning and should receive equal status with other priorities in the evaluation process. DRAFT 2015 Integrated Resource Plan
Exhibit A - IRP Stakeholder Response
SWEPCO carefully considered stakeholder comments and incorporated or accommodated those
that it reasonably could. Listed below are subjects that stakeholders requested SWEPCO to
consider:
DSM
SWEPCO evaluated DSM potential by examining usage by customer class and determining
potential energy savings for programs associated with those usage characteristics. SWEPCO
modeled DSM programs as resources, competing alongside other resources. The model selected
DSM programs that were economic in each year. DSM assumptions are being provided to the
stakeholders for their review and input.
Wind and Solar Energy
Wind energy was modeled as a PPA in three tiers, representing various geographic regions,
capacity factors and firm capacity values. Utility scale solar was modeled using a declining cost
curve, using a combination of AEP market intelligence and Bloomberg New Energy projections
of future cost declines. Distributed solar was included in the plan, however because customers
with distributed solar are assumed to pay the avoided retail rate, while SWEPCO only avoids the
SPP energy cost (less line losses), it is not an economic resource from a modeling perspective.
Therefore SWEPCO forced in distributed solar to the plan at an increasing rate over time,
recognizing that the customers’ economics are not the same as SWEPCO’s.
Carbon Pricing
With the issuance of the EPA’s CPP, predicting CO2 pricing will be a difficult and contentious
topic. Clearly there will be no cost associated with CO2 prior to 2020. A cursory review of the
state agency comments submitted to the EPA suggest that if EPA does not withdraw or
significantly modify the CPP, there will be legal challenges, which could further delay
implementation. Various other parties challenged many of the technical aspects of the rule,
specifically the reliability and infrastructure challenges associated with beginning to comply with
the rule in 2020. The final rule is set to be published during the summer of 2015, prior to the
final version of this IRP being submitted in August. State implementation plans will be
submitted one to three years later, with approval by the EPA up to a year after that. Until SIPs
136
DRAFT 2015 Integrated Resource Plan
have at least been proposed, it will be difficult for SWEPCO to determine what impact, if any,
the final CPP will have in the near term actionable period.
Modeling Structure and Sensitivities
SWEPCO used five pricing scenarios, two of which were specifically related to CO2 – no carbon
and high carbon, to select optimal portfolios. SWEPCO identified its preferred portfolio, then
created two distinct portfolios to evaluate a wide range of variation among the input variables
using a stochastic process. SWEPCO believes this stochastic evaluation is superior to one off
sensitivity or discrete evaluations, in that it provides an understanding of the level of risk
inherent in different portfolios.
Retirement Potential
SWEPCO did not model retirement decisions. The likely retirement candidates are the older, less
efficient gas steam units which have very low fixed costs. Until a catastrophic event occurs at
one of these units, SWEPCO will likely keep them operational because of the capacity value they
provide. SWEPCO did recognize that, given the age of these units, many of them will retire over
the planning period, and reflects those retirements in its plan. SWEPCO did include two
sensitivity portfolios, one of which advanced the retirement of gas-steam units, the other retiring
two coal units in 2020.
Transparency
SWEPCO has strived to make all model input data public. If there is specific data that a
stakeholder requires, they may request it and SWEPCO will honor that request to the extent that
providing such data will not violate any non-disclosure agreements SWEPCO has in place.
Sustainability
SWEPCO developed its plan based on economics, incorporating a cost penalty for CO2
emissions. The resulting plan being proposed by SWEPCO relies primarily on renewable
resources and energy efficiency measures, with only modest additions of peaking capacity.
SWEPCO’s energy profile under the Preferred Portfolio shows a significant increase in
renewable energy generation over the planning horizon.
137
DRAFT 2015 Integrated Resource Plan
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138
DRAFT 2015 Integrated Resource Plan
Exhibit B: Projected DR and EE
Year
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Interruptible DR
Total SWEPCO
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
Active DR
Louisiana Total Jurisdiction SWEPCO
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
0
27
Louisiana Jurisdiction
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Passive EE Programs
Total DR
Total Louisiana Total Louisiana SWEPCO Jurisdiction SWEPCO Jurisdiction
12
1
93
11
17
3
98
13
22
4
102
14
25
6
106
16
28
7
109
17
31
8
112
18
33
9
114
19
34
10
115
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
35
10
116
20
10 Year Forecast (2015‐2024):
Total Growth
Compund Annual Growth Rate 23
2.5%
9
7.1%
20 Year Forecast (2015‐2034):
Total Growth
Compund Annual Growth Rate 23
1.2%
9
3.3%
139
DRAFT 2015 Integrated Resource Plan
Exhibit C: Long-Term Commodity Price Forecast
Summary of Long‐Term Commodity Price Forecast Scenarios
Annual Average (Nominal Dollars )
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Base
5.47
5.83
6.01
6.12
6.19
6.43
6.75
7.18
7.30
7.51
7.75
7.85
8.04
8.22
8.41
8.52
Base
47.93
53.00
55.91
56.96
58.35
60.60
64.14
71.59
73.41
76.11
78.77
79.76
82.49
84.68
86.60
89.22
Natural Gas (Henry Hub)
Coal (PRB 8800 0.8#)
CO2
Lower Band
5.14
5.13
5.29
5.39
5.45
5.66
5.94
6.32
6.43
6.61
6.82
6.91
7.08
7.23
7.40
7.50
$/mmBtu
Higher High Band
Carbon
5.91
5.49
6.71
5.85
6.91
6.03
7.04
6.14
7.12
6.21
7.40
6.45
7.77
6.77
8.26
7.32
8.40
7.45
8.63
7.66
8.91
7.90
9.03
8.01
9.25
8.20
9.45
8.39
9.67
8.57
9.80
8.69
Lower Band
11.88
11.62
11.83
12.04
12.69
13.63
13.59
14.40
14.93
14.72
14.68
14.85
15.08
15.29
15.74
17.69
$/Ton FOB
Higher High Band
Carbon
15.80
13.30
15.44
13.00
15.72
13.24
16.01
13.47
16.87
14.20
18.12
15.26
18.06
15.21
19.14
15.30
19.85
15.87
19.57
15.64
19.52
15.60
19.75
15.78
20.05
16.03
20.33
16.25
20.93
16.73
23.52
18.79
Lower Band
0.00
0.00
0.00
0.00
0.00
0.00
0.00
15.08
15.28
15.48
15.67
15.88
16.08
16.29
16.50
16.72
$/metric tonne
Higher High Band
Carbon
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
15.08
25.00
15.28
25.32
15.48
25.65
15.67
25.99
15.88
26.32
16.08
26.66
16.29
27.02
16.50
27.37
16.72
27.72
No Carbon
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Plausible Worst
0.00
0.00
0.00
0.00
0.00
25.00
25.47
25.96
26.47
27.00
27.52
28.08
28.62
29.18
29.74
40.00
Lower Band
45.00
47.08
49.68
50.71
52.23
54.58
57.35
64.67
66.19
67.99
70.19
71.66
74.22
75.90
77.40
79.39
Power On‐Peak (SPP )
$/MWh
Higher High Band
Carbon
50.95
47.81
59.49
53.30
61.93
55.44
63.56
57.01
65.75
58.53
68.02
60.86
72.27
64.10
79.89
76.44
82.27
78.23
84.82
80.50
87.77
82.75
90.12
84.46
93.51
86.88
96.38
90.07
98.42
91.99
100.55
94.05
Lower Band
29.14
31.53
33.83
34.71
36.48
37.81
39.64
49.29
50.43
52.12
53.62
54.42
55.79
57.01
58.52
59.92
Power Off‐Peak (SPP)
$/MWh
Higher High Carbon
Band
33.54
31.03
38.96
35.28
42.39
38.07
44.31
39.35
46.38
41.10
48.37
42.84
51.40
45.41
61.19
60.48
62.66
62.05
64.92
63.55
67.10
65.60
68.98
66.84
71.75
69.21
73.24
70.37
75.89
72.55
77.76
74.04
Lower Band
30.54
31.14
31.75
32.33
32.96
33.57
34.19
34.83
35.47
36.12
36.75
37.39
38.04
38.70
39.37
40.06
SPP Capacity Price
$/MW‐day
Higher High Band
Carbon
37.63
25.00
59.33
37.25
82.02
51.81
105.61
66.96
130.33
82.82
155.98
99.29
182.69
116.44
210.50
134.30
239.45
152.88
269.57
172.22
300.61
192.15
332.83
212.83
366.27
234.31
386.73
256.59
394.85
279.70
403.15
303.67
No Carbon
26.62
32.15
37.91
43.89
50.15
56.63
63.36
70.36
77.65
85.21
92.99
101.05
109.41
118.07
127.05
136.36
Plausible Worst
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
No Carbon
5.45
5.81
5.99
6.10
6.17
6.41
6.62
6.81
7.00
7.19
7.40
7.61
7.80
7.97
8.15
8.27
No Carbon
48.48
53.30
55.46
57.04
58.73
60.81
63.70
64.02
66.00
68.17
70.52
73.23
75.37
77.34
79.83
81.16
Plausible Worst
5.41
5.76
5.93
6.05
6.11
6.66
6.98
7.29
7.42
7.63
7.87
7.99
8.20
8.37
8.56
9.17
Plausible Worst
43.89
47.60
50.33
52.16
54.69
69.65
73.04
75.00
76.39
78.93
81.63
83.47
86.79
88.68
91.01
100.19
Base
13.50
13.20
13.44
13.68
14.42
15.49
15.44
16.36
16.97
16.73
16.68
16.88
17.14
17.38
17.89
20.10
Base
30.98
35.50
37.99
39.47
41.11
43.05
45.34
54.52
55.90
57.84
59.67
61.29
63.15
64.68
66.55
68.29
140
No Carbon
13.60
13.30
13.54
13.78
14.53
15.61
15.56
16.69
17.31
17.06
17.01
17.22
17.48
17.73
18.25
20.50
No Carbon
32.24
36.08
38.53
40.15
41.74
43.58
45.21
45.19
47.12
49.13
50.60
52.31
54.34
55.56
57.73
59.19
Plausible Worst
13.50
13.20
13.44
13.68
14.42
15.49
15.44
16.36
16.97
16.73
16.68
16.88
17.14
17.38
17.89
20.10
Plausible Worst
28.33
30.88
32.65
34.38
36.33
55.49
57.93
59.06
60.18
62.16
63.87
65.45
67.83
69.87
71.53
81.08
Base
0.00
0.00
0.00
0.00
0.00
0.00
0.00
15.08
15.28
15.48
15.67
15.88
16.08
16.29
16.50
16.72
Base
26.47
33.73
41.30
49.16
57.40
65.93
74.80
84.04
93.64
103.63
113.90
124.55
135.60
147.06
158.95
171.26
DRAFT 2015 Integrated Resource Plan
Exhibit D: Capability, Demand and Reserve (CDR) “GOING-IN”
(1 of 2)
141
DRAFT 2015 Integrated Resource Plan
Exhibit D: Capability, Demand and Reserve (CDR) “GOING-IN”
(2 of 2)
142
DRAFT 2015 Integrated Resource Plan
Exhibit E: Capability, Demand and Reserve (CDR) “FINAL”
(1 of 2)
143
DRAFT 2015 Integrated Resource Plan
Exhibit E: Capability, Demand and Reserve (CDR) “FINAL”
(2 of 2)
144
DRAFT 2015 Integrated Resource Plan
Exhibit F: IRP Screened Supply-Side Resources
AEP System-West Zone
New Generation Technologies
Key Supply-Side Resource Option Assumptions (a)(b)(c)
Installed
Cost (d)
Trans.
Cost (e)
Full Load
Heat Rate
Variable
O&M
Fixed
O&M
SO2
Emission Rates
NOx
CO2
Capacity
Factor
Overall
Availability
Std. ISO
Winter
Summer
($/kW)
($/kW)
(HHV,Btu/kWh)
($/MBtu)
($/MWh)
($/kW-yr)
(Lb/mmBtu)
(Lb/mmBtu)
(Lb/mmBtu)
(%)
(%)
1,610
1,620
1,540
6,600
63
10,500
1.1
5.5
94.9
0.0000
0.000
0.00
90
95
540
490
550
490
530
480
8,100
7,600
28
31
12,500
10,300
3.2
3.2
9.5
9.1
71.1
73.3
0.1000
0.0638
0.070
0.062
21.3
21.3
85
85
88
88
624
780
650
820
560
906
1,300
1,200
60
60
6,900
6,700
7.8
7.8
2.9
2.8
11.6
10.3
0.0007
0.0007
0.009
0.007
116.0
116.0
60
60
89
89
164
420
90
200
200
170
430
90
200
200
160
410
90
190
200
800
800
1,000
1,200
1,100
57
59
60
59
60
12,200
10,300
9,600
9,000
8,900
7.8
7.8
7.8
7.8
7.8
8.9
4.3
3.3
4.3
6.4
12.2
9.2
10.4
16.5
9.5
0.0007
0.0007
0.0007
0.0007
0.0007
0.033
0.007
0.093
0.007
0.018
116.0
116.0
116.0
116.0
116.0
3
3
3
25
3
93
93
96
95
94
Capability (MW) (g)
Type
Base Load
Nuclear
Base Load (90% CO2 Capture New Unit)
Pulv. Coal (Ultra-Supercritical) (PRB)
IGCC "F" Class (PRB)
Fuel
Cost (f)
Base / Intermediate
Combined Cycle (2X1 "F" Class)
Combined Cycle (2X1 "G" Class, w/duct firing & inlet cooling)
Peaking
Combustion Turbine (2 - "E" Class)
Combustion Turbine (2 - "F" Class, w/inlet cooling)
Aero-Derivative (2-Small Machines)
Aero-Derivative (2 - Large Machines)
Recip Engine Farm (22 Engines)
Notes: (a) Installed cost, capability and heat rate numbers have been rounded.
(b) All costs in 2014 dollars. Assume 1.9% escalation rate for 2014 and beyond.
(c) $/kW costs are based on nominal capability.
(d) Total Plant & Interconnection Cost w/AFUDC (AEP-West rate of 7.83%,site rating $/kW).
(e) Transmission Cost ($/kW,w/AFUDC).
(f) Levelized Fuel Cost (40-Yr. Period 2015-2054)
(g) All Capabilities are at 1,000 feet above sea level
145
DRAFT 2015 Integrated Resource Plan
This page is intentionally blank
146
DRAFT 2015 Integrated Resource Plan
Exhibit G: Section 2 Tables
Table 2-1
Southwestern Electric Power Company
Actual and Forecast Internal Energy Requirements (GWh)
By Customer Class After DSM/EE Effects
Year
Actual
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Other**
Internal
Growth
Growth
Growth
Energy
Growth
Energy
Growth
Residential Rate Commercial Rate Industrial Rate Requirements Rate Requirements Rate
5,258
5,644
5,539
5,628
5,694
5,587
6,361
6,908
6,301
6,431
‐‐‐
7.3
‐1.9
1.6
1.2
‐1.9
13.9
8.6
‐8.8
2.1
5,694
5,791
5,732
5,971
5,994
5,957
6,141
6,280
6,103
6,011
‐‐‐
1.7
‐1.0
4.2
0.4
‐0.6
3.1
2.3
‐2.8
‐1.5
5,287
5,555
5,643
5,607
5,402
4,460
5,230
5,408
5,661
5,612
‐‐‐
5.1
1.6
‐0.6
‐3.7
‐17.4
17.2
3.4
4.7
‐0.9
6,033
6,607
6,951
6,663
6,677
6,945
7,495
7,480
7,123
7,429
Forecast
2014*
6,288
‐2.2
5,996
‐0.2
5,876
4.7
7,549
2015
6,412
2.0
6,073
1.3
5,753
‐2.1
7,742
2016
6,457
0.7
6,084
0.2
5,940
3.2
7,842
2017
6,478
0.3
6,098
0.2
6,018
1.3
7,943
2018
6,501
0.3
6,111
0.2
6,090
1.2
4,208
2019
6,548
0.7
6,150
0.6
6,320
3.8
4,210
2020
6,586
0.6
6,174
0.4
6,491
2.7
3,673
2021
6,630
0.7
6,216
0.7
6,549
0.9
3,699
2022
6,681
0.8
6,263
0.8
6,605
0.8
3,726
2023
6,728
0.7
6,315
0.8
6,666
0.9
3,754
2024
6,764
0.5
6,359
0.7
6,726
0.9
3,778
2025
6,808
0.6
6,398
0.6
6,776
0.7
3,798
2026
6,852
0.6
6,435
0.6
6,817
0.6
3,816
2027
6,894
0.6
6,472
0.6
6,859
0.6
3,835
2028
6,940
0.7
6,510
0.6
6,899
0.6
3,856
2029
6,985
0.6
6,545
0.5
6,938
0.6
3,873
2030
7,027
0.6
6,570
0.4
6,979
0.6
3,889
2031
7,071
0.6
6,593
0.3
7,024
0.6
3,902
2032
7,116
0.6
6,615
0.3
7,069
0.6
3,914
2033
7,158
0.6
6,635
0.3
7,111
0.6
3,929
2034
7,198
0.6
6,650
0.2
7,150
0.5
3,938
2035
7,237
0.5
6,659
0.1
7,185
0.5
3,946
Note: *2014 data are nine months acutal and three months forecast.
**Other energy requirements include other retail sales, wholesale sales and losses.
‐‐‐
9.5
5.2
‐4.2
0.2
4.0
7.9
‐0.2
‐4.8
4.3
22,273
23,596
23,865
23,868
23,767
22,949
25,227
26,077
25,188
25,483
‐‐‐
5.9
1.1
0.0
‐0.4
‐3.4
9.9
3.4
‐3.4
1.2
1.6
2.6
1.3
1.3
‐47.0
0.1
‐12.7
0.7
0.7
0.7
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.3
0.3
0.4
0.2
0.2
25,710
25,980
26,323
26,537
22,910
23,228
22,925
23,094
23,275
23,463
23,628
23,781
23,920
24,060
24,205
24,340
24,465
24,589
24,713
24,832
24,937
25,027
0.9
1.1
1.3
0.8
‐13.7
1.4
‐1.3
0.7
0.8
0.8
0.7
0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.4
0.4
Compound Annual Growth Rate 2004‐2013
2.3
0.6
0.7
2.3
1.5
Compound Annual Growth Rate 2015‐2035
0.6
0.5
1.1
‐3.3
‐0.2
147
DRAFT 2015 Integrated Resource Plan
Table 2-2
Southwestern Electric Power Company‐Louisiana
Actual and Forecast Retail Sales (GWh)
By Customer Class After DSM/EE Effects
Other
Retail
Growth
Rate
Retail
Sales
Growth
Rate
‐‐‐
13.2
‐3.4
1.3
‐6.3
‐7.3
22.2
14.1
‐2.1
‐5.6
39
39
39
39
39
39
39
40
40
40
‐‐‐
‐1.7
0.3
1.1
0.7
‐0.5
‐0.2
1.8
0.5
‐0.9
5,354
5,651
5,577
5,682
5,636
5,624
6,290
6,994
6,549
6,523
‐‐‐
5.5
‐1.3
1.9
‐0.8
‐0.2
11.8
11.2
‐6.4
‐0.4
Forecast
2014*
2,988
‐1.8
2,412
‐0.6
1,038
1.7
2015
3,050
2.1
2,435
0.9
1,193
14.9
2016
3,062
0.4
2,449
0.6
1,296
8.7
2017
3,070
0.3
2,452
0.1
1,306
0.7
2018
3,080
0.3
2,457
0.2
1,314
0.7
2019
3,103
0.7
2,475
0.7
1,480
12.6
2020
3,117
0.5
2,479
0.2
1,598
8.0
2021
3,137
0.6
2,492
0.5
1,607
0.6
2022
3,160
0.8
2,509
0.7
1,617
0.6
2023
3,183
0.7
2,527
0.7
1,627
0.6
2024
3,202
0.6
2,545
0.7
1,636
0.6
2025
3,224
0.7
2,561
0.6
1,644
0.5
2026
3,246
0.7
2,574
0.5
1,650
0.4
2027
3,267
0.6
2,589
0.6
1,657
0.4
2028
3,289
0.7
2,603
0.5
1,664
0.4
2029
3,310
0.7
2,616
0.5
1,670
0.4
2030
3,331
0.6
2,625
0.3
1,677
0.4
2031
3,351
0.6
2,632
0.2
1,683
0.3
2032
3,372
0.6
2,637
0.2
1,688
0.3
2033
3,392
0.6
2,641
0.2
1,693
0.3
2034
3,412
0.6
2,642
0.0
1,699
0.3
2035
3,430
0.6
2,639
‐0.1
1,704
0.3
Note: *2014 data are nine months acutal and three months forecast.
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
40
0.5
0.0
0.2
0.0
0.0
0.3
‐0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6,476
6,735
6,858
6,869
6,893
7,111
7,242
7,278
7,328
7,379
7,425
7,470
7,512
7,554
7,597
7,639
7,675
7,707
7,739
7,768
7,794
7,814
‐0.7
4.0
1.8
0.2
0.3
3.2
1.8
0.5
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.3
0.3
Year
Actual
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Residential
2,228
2,384
2,368
2,383
2,405
2,382
2,804
3,291
2,990
3,041
Growth
Growth
Growth
Rate Commercial Rate Industrial Rate
‐‐‐
7.0
‐0.7
0.6
0.9
‐0.9
17.7
17.3
‐9.1
1.7
2,265
2,277
2,275
2,345
2,344
2,417
2,439
2,525
2,453
2,428
‐‐‐
0.5
‐0.1
3.0
0.0
3.1
0.9
3.5
‐2.9
‐1.0
822
931
899
911
853
791
967
1,103
1,080
1,020
Compound Annual Growth Rate 2004‐2013
3.5
0.8
2.4
0.1
2.2
Compound Annual Growth Rate 2015‐2035
0.6
0.4
1.8
0.0
0.7
148
DRAFT 2015 Integrated Resource Plan
Table 2-3
Southwestern Electric Power Company
Winter, Summer and Annual Peak Demand (MW)
Internal Energy Requirements (GWh) and Load Factor (%)
After DSM/EE Effects
Year
Actual
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Preceding
Summer Winter
Annual
Peak
Peak
Peak
Demand Demand Demand
4,485
4,725
4,912
4,924
4,950
4,750
4,994
5,554
5,205
5,048
3,631
3,635
3,895
4,186
3,992
3,909
4,539
4,823
4,080
4,178
4,485
4,725
4,912
4,924
4,950
4,750
4,994
5,554
5,205
5,048
Internal
Energy
Requirements
Load
Factor
22,273
23,596
23,865
23,868
23,767
22,949
25,227
26,077
25,188
25,483
56.5
57.0
55.5
55.3
54.7
55.2
57.7
53.6
55.1
57.6
Forecast
2014
4,836
4,919
4,919
25,710
59.7
2015
5,248
4,308
5,248
25,980
56.5
2016
5,278
4,641
5,278
26,323
56.8
2017
5,337
4,691
5,337
26,537
56.8
2018
4,606
4,392
4,606
22,910
56.8
2019
4,682
3,942
4,682
23,228
56.6
2020
4,579
3,867
4,579
22,925
57.0
2021
4,626
3,913
4,626
23,094
57.0
2022
4,664
3,944
4,664
23,275
57.0
2023
4,709
3,971
4,709
23,463
56.9
2024
4,728
3,985
4,728
23,628
56.9
2025
4,772
4,028
4,772
23,781
56.9
2026
4,801
4,054
4,801
23,920
56.9
2027
4,830
4,081
4,830
24,060
56.9
2028
4,850
4,093
4,850
24,205
56.8
2029
4,894
4,126
4,894
24,340
56.8
2030
4,920
4,150
4,920
24,465
56.8
2031
4,946
4,174
4,946
24,589
56.8
2032
4,959
4,183
4,959
24,713
56.7
2033
4,997
4,222
4,997
24,832
56.7
2034
5,026
4,236
5,026
24,937
56.6
2035
5,043
4,255
5,043
25,027
56.6
Note: *2014 data are nine months acutal and three months forecast.
Compound Annual Growth Rate 2004‐2013
1.3
1.6
1.3
1.5
0.2
Compound Annual Growth Rate 2015‐2035
‐0.2
‐0.1
‐0.2
‐0.2
0.0
149
DRAFT 2015 Integrated Resource Plan
Table 2-4 (1 of 4)
Southwestern Electric Power Company
Actual Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
479.3
429.6
319.6
285.8
412.2
531.6
625.2
601.7
474.9
395.0
287.3
416.1
489.5
365.5
348.4
296.1
390.7
616.8
722.8
718.1
558.0
407.1
272.5
458.2
420.4
381.7
386.0
346.7
393.0
534.4
700.1
788.5
468.2
349.2
322.4
448.2
409.5
410.1
400.0
439.4
553.8
513.7
573.7
558.3
529.5
487.0
401.7
417.6
427.2
380.4
422.2
425.4
527.1
567.6
580.8
619.5
517.1
476.6
430.5
416.6
390.7
414.4
422.1
461.8
488.0
515.1
587.8
640.1
479.8
476.4
442.5
412.9
391.3
398.6
422.3
443.7
479.5
427.2
452.6
500.8
403.4
476.2
432.4
458.9
402.3
411.2
471.8
435.2
499.4
486.9
455.9
523.0
463.8
451.1
493.8
460.9
415.8
443.3
450.8
473.1
509.6
472.9
480.2
504.4
430.4
509.3
490.7
462.9
546.5
534.0
421.7
408.7
399.2
505.3
602.1
529.6
566.0
454.9
479.9
585.6
544.9
464.6
443.4
420.4
474.7
604.7
654.0
688.2
724.4
484.8
496.4
606.0
551.5
523.4
460.0
465.9
638.6
691.9
734.4
709.2
626.4
474.0
480.6
595.2
1,826.7
1,772.2
1,563.7
1,577.7
1,844.8
1,977.9
2,253.7
2,190.4
1,973.7
1,813.0
1,601.3
1,878.2
1,863.9
1,621.7
1,685.9
1,577.1
1,891.9
2,276.1
2,413.5
2,548.8
2,263.3
1,819.6
1,693.1
1,941.6
1,778.4
1,762.8
1,718.8
1,747.5
2,029.2
2,214.3
2,502.5
2,642.2
2,004.8
1,808.9
1,736.2
1,919.2
*Other energy requirements include other retail sales, wholesale sales and losses.
150
DRAFT 2015 Integrated Resource Plan
Table 2-4 (2 of 4)
Southwestern Electric Power Company
Actual Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
519.6
453.0
353.8
301.4
421.0
516.5
595.9
719.3
565.1
415.3
312.3
455.1
563.7
436.2
390.7
297.0
386.4
608.1
704.2
658.6
446.7
333.9
317.7
550.7
517.8
388.3
377.0
332.1
389.5
577.2
748.2
630.4
464.9
328.0
295.1
538.6
458.9
390.0
434.6
447.9
518.9
542.8
560.0
650.0
538.1
511.4
453.7
464.1
458.7
420.2
455.1
433.9
524.3
578.2
625.0
563.2
508.8
491.7
442.1
492.3
419.7
376.2
467.0
446.1
517.2
610.5
589.8
601.0
523.1
475.4
433.7
497.6
472.3
391.4
471.4
471.8
524.2
494.9
484.7
521.3
432.5
459.4
448.2
434.6
408.8
409.2
409.4
481.9
490.0
474.5
482.6
450.2
456.7
470.5
450.5
417.6
321.1
322.8
376.2
364.1
403.3
421.4
346.5
411.4
371.5
378.1
369.4
374.6
643.6
576.2
463.8
446.0
467.8
601.0
608.5
729.1
594.7
502.4
475.9
553.5
671.7
504.5
496.4
448.7
531.3
605.1
715.3
722.4
508.2
468.2
475.2
530.3
729.1
508.7
515.6
460.9
501.8
650.7
702.5
680.7
545.0
463.0
485.0
701.8
2,094.4
1,810.6
1,723.6
1,667.1
1,932.0
2,155.2
2,249.1
2,619.8
2,130.5
1,888.5
1,690.1
1,907.4
2,102.9
1,770.1
1,751.6
1,661.5
1,931.9
2,265.9
2,527.1
2,394.4
1,920.4
1,764.2
1,685.5
1,990.9
1,987.7
1,596.0
1,735.8
1,603.2
1,811.8
2,259.8
2,387.1
2,323.5
1,904.5
1,644.4
1,583.2
2,112.6
*Other energy requirements include other retail sales, wholesale sales and losses.
151
DRAFT 2015 Integrated Resource Plan
Table 2-4 (3 of 4)
Southwestern Electric Power Company
Actual Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
650.6
505.4
443.0
294.3
405.6
690.5
752.2
767.1
586.8
422.6
299.7
543.4
656.7
575.3
372.0
405.2
479.0
761.2
904.1
931.2
536.2
384.9
356.0
545.8
567.8
417.4
396.9
368.8
514.8
686.5
784.0
790.3
545.3
378.2
353.4
497.7
453.3
466.7
418.5
442.6
534.2
621.4
622.4
655.1
552.7
507.9
410.5
455.9
458.9
440.2
466.5
483.7
533.4
646.6
649.1
691.4
490.9
500.1
464.7
454.8
429.1
422.7
458.8
484.4
574.7
584.1
610.6
632.2
521.3
484.9
442.1
458.3
346.8
371.3
403.9
439.8
470.6
472.6
407.1
510.6
429.5
446.4
517.7
413.3
404.6
380.1
466.8
460.8
473.7
490.4
468.0
500.6
403.9
472.8
464.4
422.4
402.9
420.6
494.2
474.1
526.7
512.5
484.8
486.7
476.4
473.6
455.6
452.5
725.4
629.8
537.2
461.1
660.0
634.5
710.8
782.7
625.0
498.5
547.6
682.6
727.1
546.4
501.7
468.2
550.1
705.2
828.5
830.9
697.8
491.4
478.3
655.0
597.0
563.5
473.0
455.8
568.5
660.1
769.7
700.1
649.2
525.5
545.2
615.2
2,176.2
1,973.2
1,802.5
1,637.8
2,070.5
2,419.0
2,492.5
2,715.5
2,194.0
1,875.3
1,775.4
2,095.2
2,247.3
1,942.0
1,806.9
1,818.0
2,036.2
2,603.3
2,849.8
2,954.0
2,128.8
1,849.2
1,763.4
2,078.0
1,996.7
1,824.2
1,822.9
1,783.2
2,184.8
2,443.2
2,649.1
2,609.3
2,192.2
1,862.1
1,796.3
2,023.7
*Other energy requirements include other retail sales, wholesale sales and losses.
152
DRAFT 2015 Integrated Resource Plan
Table 2-4 (4 of 4)
Southwestern Electric Power Company
Actual Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2014
2014
2014
2014
2014
2014
2014
2014
2014
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
630.1
390.8
472.8
390.3
429.8
626.6
695.3
750.2
635.5
414.8
357.0
638.2
711.6
550.0
485.4
312.2
389.6
576.0
640.8
750.8
557.6
442.5
393.1
443.7
453.6
519.0
582.6
548.7
635.5
561.1
482.6
478.0
470.3
488.7
434.6
470.0
407.0
470.6
567.8
556.2
690.1
498.4
409.2
398.2
451.3
465.4
501.3
498.6
467.2
513.5
461.9
456.0
525.1
464.5
454.8
437.0
485.6
563.0
502.9
498.7
477.3
590.8
442.6
646.6
625.7
526.4
479.5
561.6
657.2
757.5
736.1
655.7
519.8
565.2
697.9
723.5
610.9
622.3
517.2
602.7
618.5
722.3
505.5
710.3
2,128.4
1,807.7
1,894.1
1,788.9
2,011.6
2,365.0
2,468.6
2,635.3
2,314.3
1,873.2
1,925.3
2,270.8
2,378.6
2,032.5
2,063.3
1,799.5
1,965.7
2,261.0
2,396.6
2,537.2
2,208.9
*Other energy requirements include other retail sales, wholesale sales and losses.
153
DRAFT 2015 Integrated Resource Plan
Table 2-5 (1 of 5)
Southwestern Electric Power Company
Forecast Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2014
2014
2014
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2017
2018
2018
2018
2018
2018
2018
2018
2018
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
402.0
350.5
561.9
625.9
448.6
424.9
362.7
490.0
625.6
764.9
765.0
582.7
391.8
354.2
575.5
624.3
479.5
404.3
361.1
496.7
636.3
764.8
772.7
581.0
388.8
375.7
572.4
631.4
454.4
422.8
374.3
494.2
638.2
771.4
779.3
582.9
392.9
363.6
572.8
644.3
465.3
416.3
365.1
499.5
639.0
788.7
791.4
501.9
446.7
464.0
431.8
406.8
452.6
444.7
567.9
561.2
611.0
640.6
547.4
485.1
445.1
479.3
428.6
429.0
429.7
437.7
569.6
566.5
600.8
640.7
545.7
483.9
470.2
481.1
438.4
404.9
445.7
453.3
566.1
566.7
604.6
644.5
546.6
489.4
456.0
481.3
449.5
414.8
441.2
443.6
572.0
566.1
618.5
653.0
497.4
481.8
444.0
407.5
404.6
457.3
459.8
523.7
504.4
499.4
533.4
481.8
509.3
499.3
472.5
433.4
441.5
475.6
483.7
550.9
515.8
504.5
542.5
488.1
512.2
516.6
475.0
442.8
439.0
487.9
496.8
555.3
522.0
512.3
550.6
494.6
521.2
515.5
480.5
453.0
447.9
491.6
497.4
563.5
527.4
523.7
560.2
154
572.7
585.1
763.1
719.1
622.8
544.0
531.9
460.4
661.4
801.5
818.0
681.8
598.9
585.0
717.0
729.6
647.2
608.6
544.5
469.2
646.7
810.2
827.6
690.0
606.3
536.2
725.7
740.8
642.1
583.0
520.1
500.4
662.3
820.8
838.4
699.2
614.8
585.7
735.2
360.2
319.3
326.6
302.2
217.3
337.9
421.0
428.8
1,974.1
1,864.1
2,233.0
2,184.3
1,882.7
1,878.9
1,799.1
2,042.1
2,352.6
2,676.8
2,757.0
2,293.7
1,985.1
1,883.5
2,244.4
2,215.9
1,997.1
1,918.1
1,827.0
2,086.3
2,365.4
2,680.3
2,783.5
2,304.8
1,991.2
1,898.7
2,254.2
2,253.5
1,940.5
1,939.4
1,844.5
2,116.1
2,389.1
2,709.2
2,812.8
2,323.2
2,018.3
1,920.8
2,269.8
1,907.0
1,647.2
1,675.7
1,608.3
1,852.4
2,070.4
2,351.9
2,433.5
DRAFT 2015 Integrated Resource Plan
Table 2-5 (2 of 5)
Southwestern Electric Power Company
Forecast Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2018
2018
2018
2018
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2022
2022
2022
2022
2022
2022
2022
2022
2022
2022
2022
2022
2023
2023
2023
2023
2023
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
582.7
402.0
358.4
548.0
644.3
466.0
424.3
373.3
502.3
648.8
797.7
795.1
580.3
405.1
358.6
552.3
647.4
494.1
417.5
364.3
503.5
647.8
798.9
794.3
593.7
402.2
358.6
563.5
649.2
473.4
430.9
378.5
510.8
652.2
804.6
805.3
592.3
404.5
364.9
563.4
656.8
476.8
431.4
381.9
514.4
657.7
808.6
814.5
595.5
407.8
367.3
568.2
664.0
479.9
431.5
384.9
517.9
546.5
502.9
453.4
449.9
450.2
414.4
447.4
451.9
574.6
574.7
624.0
654.6
544.1
506.8
454.6
452.8
450.3
438.8
443.5
443.7
574.7
570.9
623.3
652.6
554.8
503.2
453.2
465.3
451.2
420.2
454.1
458.2
583.2
574.5
627.6
661.6
553.8
506.2
461.4
464.0
457.0
423.4
455.1
462.4
587.1
579.2
630.2
668.9
556.5
510.1
464.7
468.7
463.4
427.2
456.6
466.9
591.7
499.9
534.1
521.5
469.6
458.7
452.7
498.8
506.1
569.7
557.6
553.0
588.0
526.0
562.7
548.7
497.4
485.3
488.9
524.6
528.6
595.9
560.8
556.8
591.5
534.4
565.2
551.4
507.3
489.1
484.7
531.6
538.7
603.5
565.8
562.0
598.8
537.3
569.9
558.8
509.2
494.4
488.8
535.1
543.5
608.2
570.8
565.9
604.8
541.4
574.8
563.2
514.0
499.8
493.1
538.8
548.6
613.5
155
385.8
334.1
319.2
455.3
362.0
320.9
306.1
289.1
212.2
325.8
425.6
432.5
410.0
338.1
322.6
465.4
308.2
278.6
293.0
282.8
166.2
294.2
368.6
373.1
341.9
301.3
282.9
382.8
309.2
278.5
282.8
259.1
162.5
302.4
370.5
376.1
361.3
303.2
286.1
407.1
311.8
280.6
291.7
252.8
172.0
303.7
372.3
379.1
365.7
305.7
288.4
402.4
314.6
282.9
299.6
249.8
181.6
2,014.9
1,773.1
1,652.5
1,922.8
1,915.2
1,653.9
1,676.6
1,620.4
1,858.8
2,106.9
2,400.3
2,470.3
2,060.3
1,812.8
1,684.5
1,967.9
1,891.2
1,700.4
1,678.6
1,619.4
1,840.4
2,073.8
2,347.6
2,411.6
2,024.9
1,771.9
1,646.1
1,918.9
1,898.8
1,656.9
1,699.3
1,634.5
1,859.9
2,094.9
2,364.6
2,441.8
2,044.8
1,783.8
1,671.2
1,943.7
1,920.0
1,669.6
1,713.3
1,640.6
1,881.6
2,111.3
2,377.0
2,467.2
2,059.0
1,798.4
1,683.6
1,953.4
1,941.7
1,683.2
1,726.5
1,650.2
1,904.7
DRAFT 2015 Integrated Resource Plan
Table 2-5 (3 of 5)
Southwestern Electric Power Company
Forecast Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2023
2023
2023
2023
2023
2023
2023
2024
2024
2024
2024
2024
2024
2024
2024
2024
2024
2024
2024
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2025
2026
2026
2026
2026
2026
2026
2026
2026
2026
2026
2026
2026
2027
2027
2027
2027
2027
2027
2027
2027
2027
2027
2027
2027
2028
2028
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
662.5
815.4
821.1
597.9
412.6
369.5
570.8
664.3
508.1
424.1
373.4
521.5
666.6
823.4
820.8
602.4
415.8
367.2
576.8
670.5
484.3
438.5
389.2
525.2
670.8
832.0
826.9
601.9
420.1
369.6
579.0
671.6
487.2
444.3
392.0
528.9
674.7
837.1
833.0
605.1
420.8
373.9
583.0
672.8
490.0
449.7
394.7
532.3
679.0
839.7
841.0
608.2
421.2
378.8
586.2
677.4
520.3
583.9
635.8
674.5
559.2
516.4
468.4
470.9
464.7
453.6
453.5
456.7
596.7
588.1
642.7
674.3
563.6
520.9
466.3
478.3
469.9
432.9
465.5
473.5
600.8
591.2
649.0
678.2
562.6
525.6
469.5
479.7
470.7
435.4
471.2
476.8
604.6
593.8
651.9
682.2
564.5
526.0
474.2
483.3
471.4
437.8
476.6
480.1
608.3
597.1
653.1
688.2
566.9
526.4
480.1
486.2
474.7
464.8
576.1
571.5
610.8
546.0
581.3
568.7
518.1
503.6
507.3
541.8
547.1
619.3
581.3
577.7
614.3
551.2
586.7
570.8
524.6
508.6
501.5
548.9
557.8
624.1
585.5
583.2
619.0
553.6
591.6
574.9
527.3
511.0
504.7
553.5
561.3
628.1
588.9
586.6
623.0
556.6
594.0
579.1
530.7
513.1
507.6
557.8
564.8
631.8
592.4
589.3
627.8
559.8
596.4
583.9
533.8
516.4
520.7
156
305.1
374.9
381.7
366.4
308.8
290.7
397.7
316.6
287.4
301.2
290.5
174.2
290.3
377.9
382.9
362.9
311.1
291.6
391.4
318.5
286.7
286.2
263.9
172.3
303.4
380.0
384.3
386.7
312.9
292.8
410.6
319.5
288.2
286.8
264.0
165.4
312.2
381.6
386.1
391.4
314.0
294.8
411.9
320.5
289.7
285.0
262.8
166.6
314.2
382.7
388.4
394.4
315.0
297.2
418.8
322.4
293.7
2,127.6
2,397.6
2,488.0
2,069.6
1,819.1
1,697.3
1,957.6
1,949.2
1,756.5
1,720.6
1,667.8
1,911.8
2,126.4
2,421.7
2,492.2
2,080.2
1,834.5
1,695.8
1,971.0
1,967.4
1,705.4
1,739.1
1,684.4
1,922.4
2,150.9
2,444.1
2,508.4
2,104.8
1,850.2
1,706.8
1,996.5
1,972.8
1,715.4
1,756.0
1,694.2
1,927.0
2,169.7
2,457.2
2,524.3
2,117.6
1,854.8
1,721.9
2,008.9
1,977.8
1,725.2
1,769.0
1,702.3
1,939.0
2,182.7
2,464.8
2,545.5
2,129.4
1,859.0
1,740.1
2,025.1
1,990.9
1,799.6
DRAFT 2015 Integrated Resource Plan
Table 2-5 (4 of 5)
Southwestern Electric Power Company
Forecast Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2028
2028
2028
2028
2028
2028
2028
2028
2028
2028
2029
2029
2029
2029
2029
2029
2029
2029
2029
2029
2029
2029
2030
2030
2030
2030
2030
2030
2030
2030
2030
2030
2030
2030
2031
2031
2031
2031
2031
2031
2031
2031
2031
2031
2031
2031
2032
2032
2032
2032
2032
2032
2032
2032
2032
2032
2032
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
438.7
384.7
535.4
683.8
841.2
847.7
614.9
425.2
379.9
591.0
687.0
495.8
450.3
400.4
539.3
688.4
852.0
855.3
610.9
431.9
383.1
590.5
691.3
498.9
453.0
402.8
542.9
692.6
860.2
858.1
613.1
434.8
383.1
596.5
694.8
502.0
456.5
405.6
546.6
696.5
865.8
860.9
618.2
437.1
383.9
602.7
693.2
530.4
449.1
400.0
550.3
700.5
865.1
866.7
627.1
433.8
389.8
468.9
470.2
611.3
600.7
652.8
692.7
571.9
530.6
481.2
490.4
482.0
443.1
477.9
486.4
615.0
603.9
660.7
697.7
567.6
537.7
485.4
488.0
484.3
444.9
479.8
488.3
617.9
606.1
665.6
697.9
568.0
539.7
484.2
493.3
485.7
446.6
482.3
490.4
620.5
607.6
668.0
698.0
570.5
540.8
483.6
498.6
482.5
470.3
476.0
483.7
623.1
609.0
664.6
700.6
576.2
535.7
488.6
557.5
562.0
635.3
596.1
591.3
632.0
564.0
600.4
586.4
537.4
521.2
513.4
562.4
571.4
638.9
599.4
596.6
636.3
564.4
605.7
590.6
537.6
524.2
516.3
565.5
574.6
642.7
603.0
601.2
639.4
567.3
609.4
592.5
542.7
527.4
519.6
569.3
578.3
646.9
606.6
605.3
642.7
571.5
613.0
595.0
548.0
528.8
532.3
570.1
578.0
651.1
610.4
607.2
647.2
577.0
613.8
600.0
157
308.6
278.4
178.3
308.8
383.8
390.6
379.6
317.1
298.5
395.9
324.6
292.6
292.3
257.7
183.9
308.9
386.1
392.3
398.5
319.3
299.9
416.3
326.1
293.9
287.9
265.3
182.5
305.4
388.2
393.2
409.4
320.7
300.5
416.2
327.2
295.1
288.7
266.2
174.7
313.1
389.6
393.9
414.6
321.7
301.1
416.0
327.5
298.1
313.5
279.5
161.5
315.7
389.9
395.7
401.1
321.5
303.3
1,773.6
1,695.3
1,960.3
2,189.4
2,469.1
2,563.0
2,130.4
1,873.2
1,746.0
2,014.6
2,014.8
1,744.9
1,782.9
1,715.9
1,977.1
2,200.6
2,495.4
2,581.7
2,141.4
1,894.5
1,759.0
2,032.3
2,025.9
1,754.0
1,786.1
1,731.0
1,986.0
2,207.1
2,515.2
2,588.7
2,157.9
1,904.6
1,760.3
2,048.7
2,035.1
1,763.3
1,796.8
1,740.5
1,988.7
2,223.8
2,528.7
2,595.5
2,174.9
1,912.6
1,763.6
2,065.3
2,032.1
1,831.1
1,808.8
1,741.1
1,986.1
2,235.6
2,526.8
2,610.1
2,181.3
1,904.8
1,781.6
DRAFT 2015 Integrated Resource Plan
Table 2-5 (5 of 5)
Southwestern Electric Power Company
Forecast Internal Energy Requirements (GWh)
By Customer Class
Other*
Internal
Energy
Energy
Year Month Residential Commercial Industrial Requirements Requirements
2032
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2033
2034
2034
2034
2034
2034
2034
2034
2034
2034
2034
2034
2034
2035
2035
2035
2035
2035
2035
2035
2035
2035
2035
2035
2035
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
609.4
700.3
507.8
465.7
411.8
553.5
704.7
870.3
877.6
624.1
439.0
394.0
609.3
706.9
510.6
465.7
414.5
556.6
708.7
875.8
882.8
625.8
443.9
396.1
611.1
710.7
513.4
468.2
416.9
559.9
712.6
883.2
886.9
626.1
448.8
397.7
613.0
504.3
486.8
449.1
488.9
494.9
625.1
610.9
666.7
707.6
571.8
539.8
492.6
501.2
490.6
450.5
487.9
496.5
626.6
612.3
668.7
709.4
571.0
543.3
493.6
500.2
491.4
451.1
488.7
497.2
628.0
613.0
671.8
709.7
568.6
546.4
493.7
499.5
553.5
533.2
526.0
577.6
585.8
654.8
614.0
610.8
653.0
578.0
618.4
604.7
554.1
537.2
528.9
579.8
589.1
658.2
617.5
614.5
656.8
580.6
622.8
608.0
556.1
540.2
531.7
582.7
592.0
661.6
620.6
618.6
659.9
582.3
626.9
610.5
557.7
158
406.7
328.8
297.1
292.3
255.1
175.5
322.2
390.6
397.7
421.3
322.8
304.4
420.9
330.3
298.0
298.4
250.8
184.2
321.8
391.7
398.7
420.3
324.3
305.2
414.8
331.1
298.6
292.7
257.0
184.1
315.6
393.0
399.3
422.9
325.4
305.5
420.9
2,074.0
2,049.1
1,780.0
1,824.6
1,747.6
2,008.8
2,251.8
2,538.4
2,635.8
2,195.2
1,919.9
1,795.8
2,085.4
2,065.0
1,788.0
1,831.7
1,750.9
2,025.5
2,260.2
2,550.7
2,647.8
2,197.7
1,934.3
1,803.0
2,082.2
2,073.4
1,794.8
1,832.3
1,763.1
2,033.5
2,261.8
2,566.7
2,655.8
2,199.8
1,947.5
1,807.5
2,091.0
DRAFT 2015 Integrated Resource Plan
Table 2-6
Southwestern Electric Power Company
Actual and Weather Normal Energy Sales (GWh) And Peak Demand (MW) vs. 2012 IRP Forecast
2012 IRP Forecast
Actual
Difference
2012
2013
2012
2013
2012
2013
6,011
5,612
81
6,299
200
103
52
1
371
63
254
187
2
303
3.2%
1.7%
0.9%
1.3%
6.1%
1.0%
4.2%
3.3%
2.6%
4.8%
24,434
728
810
3.0%
3.3%
Residential
Commercial
Industrial
Other Retail
Wholesale
6,502
6,207
5,713
82
6,488
6,495
6,265
5,799
83
6,602
6,301
6,431
6,103
5,661
81
6,117
Total Sales
24,992
25,244
24,264
2012 IRP Forecast
2012
2013
Normal
2012
2013
Difference
2012
2013
% Difference
2012
2013
135
266
187
2
361
4.5%
2.7%
0.9%
1.3%
5.2%
2.1%
4.4%
3.3%
2.6%
5.8%
952
3.4%
3.9%
Residential
Commercial
Industrial
Other Retail
Wholesale
6,502
6,207
5,713
82
6,488
6,495
6,265
5,799
83
6,602
6,221
6,360
6,044
5,661
81
6,168
5,999
5,612
81
6,241
280
163
52
1
321
Total Sales
24,992
25,244
24,175
24,292
817
2012 IRP Forecast
Winter Peak
Summer Peak
Actual
Difference
% Difference
2012
2013
2012
2013
2012
2013
2012
2013
4,370
5,286
4,452
5,358
4,021
5,205
4,178
5,048
349
81
274
310
8.7%
1.6%
6.6%
6.1%
2012 IRP Forecast
2012
2013
Winter Peak
Summer Peak
% Difference
2012
2013
4,370
5,286
4,452
5,358
Normal
2012
2013
Difference
2012
2013
% Difference
2012
2013
4,305
5,034
65
252
1.5%
5.0%
4,326
5,054
159
126
304
2.9%
6.0%
DRAFT 2015 Integrated Resource Plan
Table 2-7
Southwestern Electric Power Compand and Louisiana Jurisdiction
DSM/Energy Efficiency Included in 2012 IRP Load Forecast
Energy (GWh) and Coincident Peak Demand (MW)
Year
SWEPCO DSM/EE
Summer* Winter*
Energy Demand Demand
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
98.7
151.4
222.8
288.4
348.3
403.6
453.8
500.5
544.3
556.8
567.1
575.4
582.1
587.5
586.2
586.2
586.2
586.2
586.2
586.2
15.7
24.2
37.8
50.6
62.2
73.5
83.6
93.1
101.9
104.2
105.9
107.2
107.9
108.7
108.6
108.5
108.3
108.4
108.3
108.3
13.7
21.2
32.9
43.7
53.6
63.2
72.0
80.4
88.0
90.6
92.5
93.9
94.9
96.4
96.4
96.4
96.1
96.3
96.3
96.4
SWEPCO ‐ Louisana DSM/EE
Summer* Winter*
Energy Demand Demand
0.0
9.2
32.4
55.5
78.1
95.6
111.8
127.9
143.8
151.6
159.3
166.8
174.2
181.5
181.5
181.5
181.5
181.5
181.5
181.5
*Demand coincident with Company's seasonal peak demand.
160
0.0
1.0
4.6
8.3
11.8
14.3
16.7
19.0
21.2
22.0
22.7
23.5
24.2
24.9
24.9
24.8
24.8
24.8
24.7
24.7
0.0
1.5
6.0
10.2
14.5
17.7
20.7
23.7
26.6
28.1
29.4
30.6
31.8
33.1
33.1
33.2
33.1
33.1
33.2
33.2
DRAFT 2015 Integrated Resource Plan
Table 2-8 (1 of 2)
Southwestern Electric Power Company
Significant Economic and Demographic Variables
Utilized in Jurisdictional Residential Customer and Energy Usage Models
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Units
SWEPCO
Arkansas
SWEPCO
Real
Arkansas Personal
Population Income
566.0
582.1
593.8
602.5
613.6
627.3
636.3
647.0
659.7
672.9
690.0
708.5
722.3
733.4
743.7
756.0
765.3
773.3
780.1
788.2
796.2
804.3
812.8
821.2
SWEPCO
SWEPCO
SWEPCO
Arkansas
Louisiana
Texas
Gross
SWEPCO
SWEPCO
Real
SWEPCO
SWEPCO
SWEPCO
Real
SWEPCO
Regional Arkansas
Louisiana Personal Louisiana
Louisana
Texas
Personal
Texas
Product Employment Population Income Households Employment Population Income Employment
14,085.2
14,745.1
15,289.9
16,156.4
16,942.4
17,655.7
18,639.4
18,881.5
19,573.9
20,839.8
21,605.4
22,618.3
23,541.4
24,038.8
23,360.5
23,805.8
25,184.0
26,047.7
26,745.8
27,650.0
28,556.3
29,565.5
30,464.8
31,383.1
16,034.9
16,885.3
17,627.1
17,831.0
19,207.1
19,684.2
20,562.2
21,646.5
22,940.4
24,100.1
25,177.1
25,926.8
25,595.1
25,467.5
24,695.5
25,607.1
25,506.8
26,189.1
27,322.8
28,261.9
29,382.7
30,271.6
30,991.8
31,639.2
273.4
278.7
283.3
288.2
296.8
304.0
309.8
313.3
315.5
321.6
332.2
340.5
342.7
340.7
326.9
327.3
329.3
335.4
344.5
356.0
365.8
375.6
383.0
388.1
Thousands Millions
(2009 $)
Millions
(2009 $)
Thousands
572.4
573.6
574.1
573.0
575.5
577.2
576.6
576.7
575.9
579.9
583.4
589.7
589.7
590.3
596.1
603.5
606.4
610.0
612.0
613.2
614.4
615.3
616.4
617.6
14,275.0
14,458.4
14,767.6
15,206.7
15,461.3
15,892.0
16,780.7
16,998.3
17,421.1
18,115.8
18,887.8
19,768.8
19,846.2
21,936.9
20,931.1
21,749.9
22,768.4
23,049.8
23,122.7
23,999.8
24,744.5
25,421.2
26,071.6
26,706.1
Thousands Millions
(2009 $)
161
212.1
213.6
214.9
215.5
217.5
219.2
219.5
220.1
220.4
222.5
224.4
227.6
228.3
229.2
232.1
235.7
236.4
237.8
238.9
239.7
241.3
243.2
244.8
246.2
210.1
213.6
215.3
221.1
224.0
226.8
225.0
221.0
221.6
227.2
233.9
237.0
239.1
239.2
234.1
235.3
237.5
235.7
235.4
239.2
241.7
244.5
246.7
248.1
784.8
796.2
804.8
813.4
819.5
825.4
830.1
837.4
845.2
853.1
861.1
873.9
882.2
890.2
900.5
907.7
914.0
917.0
923.8
932.3
941.2
950.9
960.8
970.6
19,192.0
20,074.6
21,263.6
22,228.4
22,820.5
24,070.7
24,630.9
24,672.4
25,323.0
25,915.6
26,934.6
27,976.1
29,154.7
32,208.4
30,238.5
31,370.7
33,091.3
33,946.6
33,996.6
34,310.8
35,877.2
37,309.5
38,413.6
39,563.9
291.5
299.2
310.9
315.6
319.8
326.0
328.6
328.5
331.0
341.3
348.9
356.9
368.4
376.4
362.0
364.4
368.6
373.2
378.9
389.3
401.4
413.0
421.2
426.6
Thousands
Thousands
Thousands
Millions
(2009 $)
Thousands
DRAFT 2015 Integrated Resource Plan
Table 2-8 (2 of 2)
Southwestern Electric Power Company
Significant Economic and Demographic Variables
Utilized in Jurisdictional Residential Customer and Energy Usage Models
Year
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
Units
SWEPCO
Arkansas
SWEPCO
Real
Arkansas Personal
Population Income
829.5
837.8
846.0
854.0
861.9
869.5
877.0
884.3
891.6
898.4
904.7
910.5
915.9
920.7
925.2
929.3
932.9
936.4
940.0
943.7
947.3
950.5
953.7
956.9
960.0
SWEPCO
SWEPCO
SWEPCO
Arkansas
Louisiana
Texas
Gross
SWEPCO
SWEPCO
Real
SWEPCO
SWEPCO
SWEPCO
Real
SWEPCO
Regional Arkansas
Louisiana Personal Louisiana
Louisana
Texas
Personal
Texas
Product Employment Population Income Households Employment Population Income Employment
32,371.6
33,479.2
34,668.4
35,818.7
36,946.9
38,091.2
39,217.6
40,340.4
41,478.0
42,622.2
43,740.1
44,878.5
46,091.7
47,377.1
48,679.2
49,991.0
51,255.9
52,559.7
53,956.2
55,364.8
56,791.1
58,303.5
59,803.2
61,159.8
62,427.6
32,281.9
32,962.2
33,669.7
34,392.9
35,107.5
35,812.5
36,524.0
37,220.8
37,948.8
38,694.6
39,444.0
40,186.9
40,938.3
41,713.8
42,513.5
43,316.2
44,109.1
44,839.4
45,557.4
46,256.2
46,992.0
47,772.3
48,512.3
49,180.1
49,794.2
392.8
397.5
402.1
406.2
410.4
414.9
419.3
423.9
428.6
433.1
437.2
441.2
445.2
449.4
453.6
457.8
461.8
465.8
469.8
473.8
477.8
481.6
485.7
489.8
494.0
Thousands Millions
(2009 $)
Millions
(2009 $)
Thousands
619.1
620.7
622.1
623.5
625.1
626.6
628.1
629.2
630.2
631.2
632.2
633.2
634.0
634.7
635.6
636.7
638.0
639.3
640.8
642.3
643.7
645.2
646.6
648.0
649.4
27,508.6
28,335.8
29,187.0
29,991.9
30,745.8
31,498.9
32,223.6
32,933.7
33,647.2
34,322.4
34,954.0
35,595.7
36,238.3
36,897.7
37,538.7
38,171.7
38,750.6
39,346.9
40,003.0
40,647.5
41,302.1
42,023.5
42,685.5
43,203.2
43,598.6
Thousands Millions
(2009 $)
162
247.5
248.8
249.9
250.9
251.8
252.6
253.3
253.9
254.3
254.8
255.2
255.7
256.1
256.5
257.0
257.4
258.0
258.6
259.3
259.9
260.4
260.9
261.2
261.5
261.7
249.1
249.6
249.8
249.5
249.0
248.4
247.7
247.1
246.8
246.4
246.0
245.6
245.2
244.8
244.4
244.1
244.0
244.6
245.3
246.1
247.0
248.1
249.2
250.2
250.9
980.2
989.7
999.2
1,008.6
1,018.1
1,027.6
1,036.8
1,046.4
1,056.2
1,066.1
1,076.1
1,086.0
1,096.3
1,106.5
1,116.9
1,127.4
1,138.1
1,149.0
1,160.0
1,171.0
1,182.1
1,193.3
1,204.7
1,216.2
1,227.8
40,736.4
42,021.5
43,388.8
44,661.5
45,864.7
47,048.0
48,176.4
49,296.1
50,471.4
51,657.3
52,788.6
53,956.9
55,254.6
56,661.7
58,033.9
59,365.8
60,684.0
62,042.0
63,530.2
65,046.5
66,675.2
68,523.6
70,218.8
71,604.4
72,829.0
431.9
437.4
443.3
448.4
453.4
458.2
461.7
464.5
467.9
471.6
474.5
477.8
482.5
488.3
493.4
498.0
503.0
507.6
512.4
517.2
523.1
530.3
535.9
539.9
543.3
Thousands
Thousands
Thousands
Millions
(2009 $)
Thousands
DRAFT 2015 Integrated Resource Plan
Table 2-9
Southwestern Electric Power Company
Significant Economic and Demographic Variables
Utilized in Jurisdictional Commercial Energy Sales Models
SWEPCO Arkansas
SWEPCO
SWEPCO
SWEPCO
Real
Louisana
Texas
Texas Gross
Personal
Gross Regional Gross Regional Reg'l Product ‐
Income
Product
Product
Commercial
Year
SWEPCO
Arkansas
Population
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
566.0
582.1
593.8
602.5
613.6
627.3
636.3
647.0
659.7
672.9
690.0
708.5
722.3
733.4
743.7
756.0
765.3
773.3
780.1
788.2
796.2
804.3
812.8
821.2
829.5
837.8
846.0
854.0
861.9
869.5
877.0
884.3
891.6
898.4
904.7
910.5
915.9
920.7
925.2
929.3
932.9
936.4
940.0
943.7
947.3
950.5
953.7
956.9
960.0
14,085.2
14,745.1
15,289.9
16,156.4
16,942.4
17,655.7
18,639.4
18,881.5
19,573.9
20,839.8
21,605.4
22,618.3
23,541.4
24,038.8
23,360.5
23,805.8
25,184.0
26,047.7
26,745.8
27,650.0
28,556.3
29,565.5
30,464.8
31,383.1
32,371.6
33,479.2
34,668.4
35,818.7
36,946.9
38,091.2
39,217.6
40,340.4
41,478.0
42,622.2
43,740.1
44,878.5
46,091.7
47,377.1
48,679.2
49,991.0
51,255.9
52,559.7
53,956.2
55,364.8
56,791.1
58,303.5
59,803.2
61,159.8
62,427.6
Thousands
2009 $ Millions
Units
16,824.4
17,028.5
17,338.1
17,286.0
17,870.4
18,007.5
18,840.1
19,226.2
20,179.8
20,742.1
21,371.7
21,419.1
21,053.3
20,950.2
22,022.8
23,505.1
23,466.1
22,863.1
22,837.0
23,445.8
23,974.2
24,405.7
24,757.4
25,114.9
25,477.9
25,845.7
26,214.9
26,577.3
26,945.9
27,312.3
27,664.2
27,977.8
28,294.0
28,592.2
28,883.5
29,182.6
29,430.6
29,667.2
29,875.2
30,004.4
30,069.4
30,211.8
30,392.1
30,604.9
30,898.7
31,251.3
31,578.1
31,933.6
32,265.6
23,412.3
24,697.5
26,757.8
27,560.8
28,403.4
29,455.4
29,656.3
30,160.2
30,607.9
32,970.6
33,306.6
34,493.2
36,407.9
36,405.5
35,115.5
37,042.0
38,018.3
39,370.7
39,975.2
41,479.1
43,211.3
44,858.9
46,165.9
47,308.3
48,557.1
49,910.0
51,325.0
52,707.1
54,074.1
55,418.1
56,590.1
57,651.9
58,775.3
59,833.3
60,795.0
61,752.4
62,727.9
63,852.7
64,850.6
65,713.4
66,689.3
67,563.4
68,442.7
69,269.0
70,344.4
71,725.5
72,539.7
72,878.0
73,040.9
13,970.6
15,232.1
17,046.9
17,497.6
18,324.9
19,022.4
19,032.8
19,549.3
19,900.8
20,738.8
21,557.3
22,248.1
23,399.3
24,336.1
23,780.8
24,914.6
25,596.5
26,203.0
26,986.0
28,070.2
29,281.4
30,326.2
31,251.6
32,071.5
32,937.4
33,871.8
34,866.5
35,849.8
36,845.1
37,812.6
38,671.3
39,450.5
40,264.0
41,064.0
41,788.0
42,480.2
43,200.5
43,972.6
44,663.3
45,263.7
45,950.1
46,575.7
47,213.8
47,828.5
48,664.3
49,757.3
50,325.7
50,497.8
50,556.1
2009 $ Millions 2009 $ Millions 2009 $ Millions
163
SWEPCO
Texas
Population
784.8
796.2
804.8
813.4
819.5
825.4
830.1
837.4
845.2
853.1
861.1
873.9
882.2
890.2
900.5
907.7
914.0
917.0
923.8
932.3
941.2
950.9
960.8
970.6
980.2
989.7
999.2
1,008.6
1,018.1
1,027.6
1,036.8
1,046.4
1,056.2
1,066.1
1,076.1
1,086.0
1,096.3
1,106.5
1,116.9
1,127.4
1,138.1
1,149.0
1,160.0
1,171.0
1,182.1
1,193.3
1,204.7
1,216.2
1,227.8
Thousands
DRAFT 2015 Integrated Resource Plan
Table 2-10
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
Units
Southwestern Electric Power Company
Significant Economic and Demographic Variables
Utilized in Jurisdictional Manufacturing Energy Sales Models
SWEPCO ‐ AR
SWEPCO ‐ LA
SWEPCO
SWEPCO
Gross Regional
Gross Regional
Louisana
Texas
Product ‐
Product ‐
Manufacturing Manufacturing
Manufacturing
Manufacturing
Employment
Employment
FRB
Industrial
Production Index
Primary Metals
3,956.6
4,104.4
4,448.3
4,339.8
4,821.0
4,763.8
4,633.7
5,046.4
5,378.3
5,626.8
5,845.7
5,825.6
5,026.8
4,299.9
3,926.5
4,382.2
4,232.9
4,239.7
4,309.4
4,497.7
4,740.3
4,910.5
5,062.6
5,210.0
5,359.3
5,517.2
5,682.1
5,852.5
6,018.2
6,170.7
6,320.7
6,479.6
6,654.3
6,834.9
7,019.4
7,199.6
7,379.2
7,567.4
7,766.2
7,969.8
8,179.9
8,388.4
8,603.3
8,817.2
9,029.5
9,249.5
9,498.8
9,746.7
9,984.3
2,663.0
2,769.3
2,597.6
2,631.0
2,772.9
2,335.4
2,029.1
2,381.3
3,402.6
3,737.4
4,033.1
3,655.8
3,268.8
2,752.1
2,499.1
3,054.1
2,969.0
2,939.3
2,917.6
3,223.9
3,442.2
3,594.8
3,748.5
3,943.4
4,158.2
4,381.2
4,593.6
4,785.8
4,983.5
5,191.8
5,409.8
5,628.4
5,863.0
6,100.1
6,340.7
6,594.0
6,838.0
7,087.1
7,333.2
7,596.8
7,869.8
8,155.8
8,454.5
8,768.4
9,117.8
9,495.9
9,889.7
10,358.3
10,845.8
28.1
27.3
25.8
25.5
25.7
25.5
23.7
21.5
21.5
22.0
22.4
22.3
22.0
19.5
17.0
17.0
17.1
16.7
15.9
16.7
16.9
17.1
17.2
17.2
17.3
17.3
17.2
17.1
17.0
17.0
16.9
16.8
16.8
16.8
16.8
16.8
16.8
16.8
16.8
16.9
16.9
17.0
17.1
17.2
17.4
17.6
17.8
18.0
18.3
49.4
50.2
51.0
52.1
52.1
52.2
50.7
49.0
48.3
49.6
50.0
50.7
51.2
49.6
42.3
39.8
39.9
39.0
37.4
38.0
38.5
38.5
38.3
37.8
37.4
37.1
36.9
36.6
36.5
36.3
36.0
35.6
35.2
34.9
34.6
34.3
34.2
34.1
34.0
33.9
33.8
33.7
33.6
33.5
33.5
33.7
33.7
33.6
33.5
95.6
97.9
102.0
103.8
103.7
100.3
91.3
91.3
89.8
97.7
95.2
98.0
100.0
100.0
74.0
91.1
97.4
99.4
98.7
102.1
105.4
109.5
113.9
118.5
123.0
127.5
132.0
136.4
140.8
145.0
149.1
153.2
157.1
160.8
164.3
167.7
170.9
173.8
176.5
179.0
181.2
183.2
184.9
186.2
187.4
188.2
188.7
188.9
188.8
2009 $ Millions
2009 $ Millions
Thousands
Thousands
Index (2007 = 100)
164
DRAFT 2015 Integrated Resource Plan
Table 2-11
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
Units
Southwestern Electric Power Company
Significant Economic and Demographic Variables
Utilized in Jurisdictional Other Retail and Wholesale Energy Sales Models
SWEPCO
SWEPCO
SWEPCO
SWEPCO ‐ AR
SWEPCO ‐ LA
SWEPCO
SWEPCO ‐ TX
Arkansas Louisiana
Texas
Gross Regional Gross Regional
Texas
Gross Reg'l Product ‐
Population Population Employment
Product
Product
Population
Commercial
566.0
582.1
593.8
602.5
613.6
627.3
636.3
647.0
659.7
672.9
690.0
708.5
722.3
733.4
743.7
756.0
765.3
773.3
780.1
788.2
796.2
804.3
812.8
821.2
829.5
837.8
846.0
854.0
861.9
869.5
877.0
884.3
891.6
898.4
904.7
910.5
915.9
920.7
925.2
929.3
932.9
936.4
940.0
943.7
947.3
950.5
953.7
956.9
960.0
572.4
573.6
574.1
573.0
575.5
577.2
576.6
576.7
575.9
579.9
583.4
589.7
589.7
590.3
596.1
603.5
606.4
610.0
612.0
613.2
614.4
615.3
616.4
617.6
619.1
620.7
622.1
623.5
625.1
626.6
628.1
629.2
630.2
631.2
632.2
633.2
634.0
634.7
635.6
636.7
638.0
639.3
640.8
642.3
643.7
645.2
646.6
648.0
649.4
Thousands Thousands
SWEPCO ‐ TX
Gross Regional
Product
291.5
299.2
310.9
315.6
319.8
326.0
328.6
328.5
331.0
341.3
348.9
356.9
368.4
376.4
362.0
364.4
368.6
373.2
378.9
389.3
401.4
413.0
421.2
426.6
431.9
437.4
443.3
448.4
453.4
458.2
461.7
464.5
467.9
471.6
474.5
477.8
482.5
488.3
493.4
498.0
503.0
507.6
512.4
517.2
523.1
530.3
535.9
539.9
543.3
16,034.9
16,885.3
17,627.1
17,831.0
19,207.1
19,684.2
20,562.2
21,646.5
22,940.4
24,100.1
25,177.1
25,926.8
25,595.1
25,467.5
24,695.5
25,607.1
25,506.8
26,189.1
27,322.8
28,261.9
29,382.7
30,271.6
30,991.8
31,639.2
32,281.9
32,962.2
33,669.7
34,392.9
35,107.5
35,812.5
36,524.0
37,220.8
37,948.8
38,694.6
39,444.0
40,186.9
40,938.3
41,713.8
42,513.5
43,316.2
44,109.1
44,839.4
45,557.4
46,256.2
46,992.0
47,772.3
48,512.3
49,180.1
49,794.2
16,824.4
17,028.5
17,338.1
17,286.0
17,870.4
18,007.5
18,840.1
19,226.2
20,179.8
20,742.1
21,371.7
21,419.1
21,053.3
20,950.2
22,022.8
23,505.1
23,466.1
22,863.1
22,837.0
23,445.8
23,974.2
24,405.7
24,757.4
25,114.9
25,477.9
25,845.7
26,214.9
26,577.3
26,945.9
27,312.3
27,664.2
27,977.8
28,294.0
28,592.2
28,883.5
29,182.6
29,430.6
29,667.2
29,875.2
30,004.4
30,069.4
30,211.8
30,392.1
30,604.9
30,898.7
31,251.3
31,578.1
31,933.6
32,265.6
784.8
796.2
804.8
813.4
819.5
825.4
830.1
837.4
845.2
853.1
861.1
873.9
882.2
890.2
900.5
907.7
914.0
917.0
923.8
932.3
941.2
950.9
960.8
970.6
980.2
989.7
999.2
1,008.6
1,018.1
1,027.6
1,036.8
1,046.4
1,056.2
1,066.1
1,076.1
1,086.0
1,096.3
1,106.5
1,116.9
1,127.4
1,138.1
1,149.0
1,160.0
1,171.0
1,182.1
1,193.3
1,204.7
1,216.2
1,227.8
13,970.6
15,232.1
17,046.9
17,497.6
18,324.9
19,022.4
19,032.8
19,549.3
19,900.8
20,738.8
21,557.3
22,248.1
23,399.3
24,336.1
23,780.8
24,914.6
25,596.5
26,203.0
26,986.0
28,070.2
29,281.4
30,326.2
31,251.6
32,071.5
32,937.4
33,871.8
34,866.5
35,849.8
36,845.1
37,812.6
38,671.3
39,450.5
40,264.0
41,064.0
41,788.0
42,480.2
43,200.5
43,972.6
44,663.3
45,263.7
45,950.1
46,575.7
47,213.8
47,828.5
48,664.3
49,757.3
50,325.7
50,497.8
50,556.1
23,412.3
24,697.5
26,757.8
27,560.8
28,403.4
29,455.4
29,656.3
30,160.2
30,607.9
32,970.6
33,306.6
34,493.2
36,407.9
36,405.5
35,115.5
37,042.0
38,018.3
39,370.7
39,975.2
41,479.1
43,211.3
44,858.9
46,165.9
47,308.3
48,557.1
49,910.0
51,325.0
52,707.1
54,074.1
55,418.1
56,590.1
57,651.9
58,775.3
59,833.3
60,795.0
61,752.4
62,727.9
63,852.7
64,850.6
65,713.4
66,689.3
67,563.4
68,442.7
69,269.0
70,344.4
71,725.5
72,539.7
72,878.0
73,040.9
Thousands
2009 $ Millions
2009 $ Millions
Thousands
2009 $ Millions
2009 $ Millions
165
DRAFT 2015 Integrated Resource Plan
Table 2-12
Southwestern Electric Power Compand and Louisiana Jurisdiction
DSM/Energy Efficiency Included in Load Forecast
Energy (GWh) and Coincident Peak Demand (MW)
Year
SWEPCO DSM/EE
Summer* Winter*
Energy Demand Demand
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
56.4
79.5
98.6
113.8
125.8
135.1
142.0
146.8
150.0
150.0
150.0
150.0
150.0
150.0
150.0
150.0
150.0
150.0
150.0
150.0
150.0
11.9
17.2
21.7
25.4
28.5
30.9
32.9
34.3
35.4
35.3
35.4
35.4
35.4
35.4
35.4
35.4
35.4
35.3
35.4
35.4
35.4
8.9
12.8
16.1
18.9
21.1
22.7
24.2
25.2
25.9
25.8
26.0
25.9
26.0
25.8
25.9
26.0
26.0
25.8
26.0
25.9
25.9
SWEPCO ‐ Louisana DSM/EE
Summer* Winter*
Energy Demand Demand
3.9
11.3
17.9
23.8
29.0
33.4
37.0
40.1
42.5
42.5
42.5
42.5
42.5
42.5
42.5
42.5
42.5
42.5
42.5
42.5
42.5
*Demand coincident with Company's seasonal peak demand.
166
0.9
2.6
4.2
5.6
6.8
7.9
8.8
9.6
10.3
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.3
10.2
0.6
1.9
3.0
4.0
4.9
5.6
6.2
6.8
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
DRAFT 2015 Integrated Resource Plan
Table 2-13
Southwestern Electric Power Company
Actual and Forecast Losses (GWh)
Year
Losses
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014*
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
784.6
972.6
1,203.3
808.5
751.5
965.9
1,020.6
902.2
924.0
1,049.1
1,168.1
1,147.5
1,164.2
1,167.8
1,371.3
1,358.2
1,368.4
1,379.0
1,390.1
1,400.9
1,411.4
1,421.2
1,428.6
1,437.4
1,448.3
1,456.2
1,464.6
1,470.9
1,477.6
1,487.4
1,493.5
1,499.6
167
DRAFT 2015 Integrated Resource Plan
Table 2-14
Southwestern Electric Power Company
Short‐Term Load Forecast
Blended Forecast vs. Long‐Term Model Results
Class
Residential
Commercial
Industrial
Other Retail
Arkansas
Blend
Long‐Term
Long‐Term
Long‐Term
Louisiana
Long‐Term
Blend
Long‐Term
Blend
168
Texas
Long‐Term
Long‐Term
Long‐Term
Long‐Term
DRAFT 2015 Integrated Resource Plan
Exhibit H Plan Summary Charts
Base‐2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
Firm Cap (MW)
5500
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
0
High Band ‐ 2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
169
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
0
2014
Firm Cap (MW)
5500
DRAFT 2015 Integrated Resource Plan
Low Band ‐ 2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
Firm Cap (MW)
5500
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
0
High Carbon ‐ 2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
170
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
0
2014
Firm Cap (MW)
5500
DRAFT 2015 Integrated Resource Plan
No Carbon ‐ 2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
Firm Cap (MW)
5500
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
0
AccelGas‐2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
171
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
0
2014
Firm Cap (MW)
5500
DRAFT 2015 Integrated Resource Plan
HighLoad‐2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
Firm Cap (MW)
5500
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
0
LowLoad‐2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
172
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
0
2014
Firm Cap (MW)
5500
DRAFT 2015 Integrated Resource Plan
Early Coal‐2015 SWEPCO IRP PRELIMINARY Existing + Firm Capacity Additions (MW)
6500
6000
5000
Existing
4500
Wind
4000
VVO
Utility Solar
3500
Dist Solar
3000
EE
2500
50 Wartsila
2000
390 CC
1500
164 CT
1000
Peak Load+Res
500
173
2040
2039
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
0
2014
Firm Cap (MW)
5500
DRAFT 2015 Integrated Resource Plan
This page is intentionally blank
174
DRAFT 2015 Integrated Resource Plan
Exhibit I EE Annual Energy & Capacity Program Savings Tables
Datetime
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Datetime
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Units
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
Units
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
GWh
Commercial Cooling HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
3.9
5.9
7.9
9.9
11.8
13.8
15.8
17.7
19.7
Residential Shell‐
Thermal HAP
0.0
0.0
0.0
0.0
0.0
0.0
1.0
9.9
18.7
27.6
27.6
27.6
33.5
33.5
34.5
43.4
52.3
60.1
60.1
60.1
60.1
69.0
Commercial Commercial Office Commercial Indoor Cooling AP
Equipment HAP
Lighting HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
10.8
0.0
3.9
21.7
0.0
5.9
32.5
0.0
7.9
32.5
0.0
7.9
32.5
0.0
9.9
43.4
3.9
11.8
54.2
3.9
13.8
65.1
7.9
15.8
75.9
11.8
17.7
86.8
15.8
19.7
97.6
15.8
21.7
108.5
15.8
23.7
119.3
11.8
25.6
130.2
15.8
25.6
141.0
15.8
25.6
Residential Residential Shell‐
Cooling HAP
Thermal AP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
0.0
2.0
0.0
3.0
0.0
4.9
0.0
6.9
0.0
8.9
0.0
10.8
0.0
12.8
0.0
14.8
0.0
16.8
0.0
18.7
0.0
19.7
0.0
20.7
0.0
21.7
0.0
21.7
0.0
21.7
0.0
21.7
0.0
21.7
0.0
21.7
Residential Cooling AP
0.0
0.0
0.0
3.0
5.9
8.9
11.8
14.8
17.7
20.7
23.7
26.6
29.6
32.5
35.5
38.5
41.4
44.4
47.3
50.3
53.2
53.2
Commercial Outdoor Lighting 0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Residential Lighting HAP
0.0
0.0
0.0
4.9
9.9
14.8
16.8
18.7
20.7
22.7
24.7
26.6
28.6
30.6
32.5
34.5
36.5
38.5
40.4
42.4
44.4
46.3
175
Commercial Office Commercial Commercial Outdoor Equipment AP
Indoor Lighting AP
Lighting AP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
14.8
0.0
6.9
29.6
0.0
13.8
44.4
0.0
19.7
49.3
0.0
25.6
54.2
0.0
31.6
59.2
0.0
37.5
64.1
0.0
36.5
69.0
0.0
35.5
74.0
1.0
35.5
78.9
2.0
35.5
83.8
3.0
35.5
88.7
3.9
35.5
93.7
4.9
35.5
83.8
5.9
35.5
73.9
6.9
35.5
64.1
7.9
35.5
64.1
8.9
35.5
64.1
9.9
35.5
64.1
10.8
Residential Residential Water Residential Lighting AP
Heating HAP
Appliances HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4.9
0.0
0.0
9.9
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
0.0
0.0
14.8
16.8
0.0
14.8
33.5
0.0
14.8
50.3
0.0
14.8
67.0
0.0
14.8
83.8
0.0
14.8
100.6
0.0
14.8
117.3
0.0
14.8
134.1
0.0
Residential Water Residential Heating AP
Appliances AP
0.0
0.0
0.0
0.0
0.0
0.0
3.9
0.0
7.9
0.0
11.8
0.0
16.8
0.0
21.7
0.0
26.6
0.0
31.6
0.0
36.5
0.0
41.4
0.0
46.3
0.0
51.3
4.9
56.2
9.9
61.1
14.8
66.1
19.7
67.0
19.7
68.0
23.7
69.0
28.6
69.0
33.5
69.0
38.5
DRAFT 2015 Integrated Resource Plan
Datetime
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Datetime
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
Units
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
Units
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
Commercial Cooling HAP
Commercial Cooling AP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
2.4
3.5
4.7
5.9
7.1
8.3
9.5
10.6
11.8
Residential Shell‐
Thermal HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.6
6.2
11.8
17.4
17.4
17.4
21.1
21.1
21.8
27.4
32.9
37.9
37.9
37.9
37.9
43.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.5
13.0
19.5
19.5
19.5
26.0
32.5
39.0
45.5
52.0
58.5
65.0
71.5
78.0
84.5
Residential Cooling HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Commercial Commercial Office Commercial Indoor Outdoor Lighting Equipment HAP
Lighting HAP
HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.0
0.9
0.0
0.0
1.4
0.0
0.0
1.9
0.0
0.0
1.9
0.0
0.0
2.3
0.0
0.9
2.8
0.0
0.9
3.3
0.0
1.9
3.8
0.0
2.8
4.2
0.0
3.8
4.7
0.0
3.8
5.2
0.0
3.8
5.6
0.0
2.8
6.1
0.0
3.8
6.1
0.0
3.8
6.1
0.0
Residential Shell‐
Thermal AP
0.0
0.0
0.0
0.6
1.2
1.9
3.1
4.4
5.6
6.8
8.1
9.3
10.6
11.8
12.4
13.1
13.7
13.7
13.7
13.7
13.7
13.7
Residential Cooling AP
0.0
0.0
0.0
1.9
3.7
5.6
7.5
9.3
11.2
13.1
14.9
16.8
18.7
20.5
22.4
24.2
26.1
28.0
29.8
31.7
33.6
33.6
Residential Lighting HAP
0.0
0.0
0.0
0.3
0.7
1.0
1.1
1.3
1.4
1.5
1.7
1.8
1.9
2.1
2.2
2.3
2.5
2.6
2.7
2.9
3.0
3.1
176
Residential Lighting AP
0.0
0.0
0.0
0.3
0.7
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Commercial Office Equipment AP
Commercial Commercial Outdoor Indoor Lighting AP
Lighting AP
0.0
0.0
0.0
0.0
1.6
3.3
4.7
6.1
7.5
8.9
8.7
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
0.0
0.0
0.0
3.5
7.0
10.6
11.7
12.9
14.1
15.3
16.4
17.6
18.8
20.0
21.1
22.3
20.0
17.6
15.3
15.3
15.3
15.3
Residential Water Residential Heating HAP
Appliances HAP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.0
12.0
17.9
23.9
29.9
35.9
41.9
47.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.5
0.7
0.9
1.2
1.4
1.6
1.9
2.1
2.3
2.6
Residential Water Heating AP
0.0
0.0
0.0
1.4
2.8
4.2
6.0
7.7
9.5
11.3
13.0
14.8
16.5
18.3
20.1
21.8
23.6
23.9
24.3
24.6
24.6
24.6
Residential Appliances AP
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.8
3.5
5.3
7.0
7.0
8.4
10.2
12.0
13.7