Information
• Poster Title:
– “Integrating Remote Wind Resources: The Role of Energy Storage”
• Authors:
– Julian Lamy, Doctoral Student, Department of Engineering and Public Policy, Carnegie
Mellon University
– Granger Morgan, Department Head and Lord Chair Professor, Department of
Engineering and Public Policy, Carnegie Mellon University
– Ines Azevedo, Assistant Research Professor, Department of Engineering and Public
Policy, Carnegie Mellon University
• Complete contact details for the lead author/student
– Name: Julian Lamy
– Title: Doctoral Student
– Organization: Carnegie Mellon University, Department of Engineering and Public
Policy
– Address: 5744 Holden St, apt 24F Pittsburgh PA, 15232
– Phone/Fax/Email: [email protected]
Integrating Remote Wind Resources: The Role of Energy Storage
Julian Lamy, Granger Morgan, Ines Azevedo
Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213
Background Information
Using Storage for Integrating Remote Wind
- 29 U.S. states have renewable portfolio standards (RPS) that require some
amount of low-carbon generation. Wind power is likely to play a large role
in meeting these goals. For example, Illinois set a 25% RPS target for
2025 of which 60-75% of this target must be met using wind resources.
Previous work [1] showed that in meeting this target, building local wind
resource in Illinois is less expensive than building wind in and
transmission to remote locations with higher capacity factors (CF).
- However, building local resources may be not possible due to public
disapproval, to maxed out wind potential, or to the onset of even stricter
targets requiring more wind resources than are available locally. In this
case, massive transmission investment would likely be needed to access
these remote resources.
- Past research suggests that energy storage technology could replace
some transmission capacity [2], however at what costs? And how do they
compare with transmission costs?
- Building storage at a remote wind farm can add value in two ways: 1) price
arbitrage and 2) transmission savings.
- The first adds value by allowing the operator to store power when
transmission is constrained and then deliver it once the value of electricity
is higher and transmission is unconstrained. See the graph below.
Research Objectives
1) Instead of making cost assumptions for transmission and storage, this
research finds the costs at which building remote versus local wind is
most economic in terms of minimizing total levelized cost of electricity
(LCOE). This decision is made from a system planner’s perspective.
2) In making this decision, it adds the option to build energy storage
capacity as well as transmission and finds the optimal level of both for
different costs. The focus is on remote resources in North Dakota versus
local resources in Illinois but the results and tools developed are
generalizable to other locations.
3) It also considers the problem from an individual agent’s perspective in
which an investor would bear the cost of a remote wind farm and all or
most of the storage and transmission costs. In this case, the optimal
investment decision is based on maximizing yearly return on investment
(ROI) from selling electricity. Work by [2, 3, 4] suggest that in addition to
transmission savings, an individual investor could also gain value from
storage capacity through price arbitrage. It is assumed that the investor
will enter a fixed price purchasing power agreement (PPA) with a load
serving entity (LSE) and that this contract will be based on historical
location marginal prices (LMP). For this, 2010 hourly LMP data from the
Illinois Hub are used . 2006 simulated data from the Eastern Wind
Interconnection Study (EWITS) are used for hourly wind output.
4) To the author’s knowledge, this is the first attempt at analyzing this
decision from both an investor’s and system planner’s perspective.
- This ability could help the investor
negotiate a higher rate for a bundled PPA
since they could deliver renewable power
to LSE’s seeking RECs at times when
power is most valuable to the LSE. It is
therefore assumed that the investor
receives the annual revenue from
matching hourly wind output to highest
hourly prices (LMP).
ND wind farm hourly CF with TRNS constraint
equal to 70% of wind’s nameplate capacity.
- Second, storage could allow the investor to replace some transmission
capacity. Referring to the figure above, so long as the storage was large
enough, the 70% transmission constraint might be able to send the same
amount of power as a 100% transmission line. This might be optimal
depending on the price of storage and transmission.
Modeling Steps and Objective Function
Investor Perspective:
1) Parameterize transmission (TC) and storage (SC) capacity
2) Optimize the wind farm’s operation to maximize net revenue assuming
certain hourly prices and wind output
Max ∑ ptqt , Vt
St. qt = wt + st – st+1 – zt
qt, st+1
0<st<SC, 0<qt<TC
t = hour
qt = delivered power Zt = curtailed power
Wt = power produced TC = TRNS const. as a % of wind nameplate capacity
St = power stored
SC = storage const. as a % of wind nameplate capacity
3) Restart at 1) by changing TC and SC
4) Calculate ROI for the year for each scenario and find the maximum
System Planner ‘s Perspective:
1) Set TC, fix power required per year (Q), let SC = inf (unconstrained)
2) Optimize the wind farm’s operation to minimize total curtailed power
assuming certain wind output
Min ∑ zt , Vt
qt, st+1
St. qt = wt + st – st+1 – zt
0<st<inf, 0<qt<TC
∑ qt = Q
Q = power required per year
3) Restart at 1) by changing TC
4) Calculate LCOE for each scenario and find the minimum
A) 48% CF
~1000km
Assumptions
B) 33% CF
~0 km
- A 200 MW wind farm is to be built either in location A or B in the figure
above. A and B have average capacity factors of 48% and 33% respectively.
- For A, the goal is to solve for the optimal capacity of transmission, (1,000 km
from load) and storage capacity to 1) minimize LCOE for the system
planner’s perspective and 2) maximize profits for the investor’s perspective.
- For B, no transmission or storage is added.
CF
Avg.$/MWh i: Balance of power and cost of
Wind Battery TRNS
power electronics for batteries
25
Ints cost ($/kw)
2,000 “varies” “varies” ND 48% LMP
Ii: based on Li-ion battery but
30
length (km)
1,000
IL 33% REC
FOP Cost ($/kw-yr)
30
2.5i
CF
Avg.$/MWh also generalizable for CAES
Iii: Based on Li-ion battery or
Duration (hrs)
1ii
25
ND 48% LMP
high cost for CAES
VOP Cost ($/MWh) 5.5
7iii
0
30
IL 33% REC
Efficiency
80%
Perfect foresight
The author fully understands the limitation in assuming perfect foresight in LMP and wind output. He also
understands that LMP prices will necessarily change with more wind power supplied at one node. In making
these assumptions, this analysis represents a best case scenario for using storage in the applications discussed.
Further work is needed to verify these results with general treatment of the problem.
Results ($600/MW-km TRNS cost Base Value)
From the system planner’s
perspective, building local wind is
optimal for costs above
$700/MW-km. At lower TNRS
cost, remote wind is optimal but
without storage. Storage only
replaces TRNS when its cost is
$20/kWh or less (RIGHT). When
TRNS costs approach
$1,200/MW-km, storage is
optimal up to $40/kWh but at
this TRNS cost, local wind is best.
Capital Costs
Estimates
CAES
Sodium-ion
$/kWh
Source
25-100
~500
[2], [5]
[5]
Li-ion
300-1,000
[6], [5]
Used Li-ion
50-150
Preliminary
estimates
LEFT: From the investor’s
perspective, given that one
chooses to build remote wind
and must also build
transmission, some storage is
optimal (highest ROI) up to
$100/kWh. This result is
consistent for up to $1000/MWkm. Note however that storage
never replaces transmission,
instead the operator chooses to
build storage simply for price
arbitrage opportunities.
% TRNS
This work was supported by the center for
Climate and Energy Decision Making (SES0949710), through a cooperative
agreement between the National Science
Foundation and Carnegie Mellon
University.
100%
95%
90%
85%
80%
75%
70%
65%
60%
55%
50%
0 MWh
575
MWh
10 20 30 40 50 60 70 80 90 100
$/kWh
Transmission cost estimates range
from $400-1,200/km-MW. Results
are robust across this range.
Capital Costs
$/MW-km
Estimates
TRNS
$400-1,200
Source
[3]
The most optimistic estimates of storage cost are
$25/kWh for CAES and $50/kWh for batteries.
Discussion and Policy Implications
- Based on this case study, assuming that transmission costs are
$600/km-MW or lower, from a system planner’s perspective storage
does not replace transmission unless costs are $20/kWh or lower.
- From an individual investor’s perspective, the same result holds
since optimal transmission investment is the same for all storage
cost scenarios (90%). Storage does however add value to the project
with price arbitrage for costs up to $100/kWh.
- Results suggest that storage is not a viable replacement for
transmission capacity from either perspective unless storage costs
decrease significantly. Policymakers/ system planners interested in
accessing remote wind resource should simply build transmission.
Storage does however add value to individual projects with price
arbitrage. Further analysis of different locations and generalizations
of this study must be done to verify these results.
[1] David C. Hoppock and Dalia Patino-Echeverri, “Cost of Wind Energy: Comparing Distant Wind Resources to Local Resources
in the Midwestern United States,” Environ. Sci. Technol. 2010, 44, 8758–8765
[2] Paul Denholm and Ramteen Sioshansi, "The value of compressed air energy storage with wind in transmission-constrained
electri cpower systems," Energy Policy 37(2009)3149–3158
[3] Emily Fertig and Jay Apt, "Economics of compressed air energy storage to integrate wind power: A case study in ERCOT,"
Energy Policy 39 (2011) 2330–2342
[4] Eric Hittinger, J.F. Whitacre and Jay Apt, "Compensating for wind variability using co-located natural gas generation and
energy storage," Energy Syst. DOI 10.1007/s12667-010-0017-2, June 2010
[5] EPRI, "Electricity Energy Storage Technology Options: A White Paper Primer on Applications, Costs, and Benefits," EPRI
Project Manager D. Rastler, December 2010
[6] Argonne National Labs (ANL), "Modeling the Performance and Cost of Lithium-Ion Batteries for Electric-Drive Vehicles," Paul
A. Nelson, Kevin G. Gallagher, Ira Bloom, and Dennis W. Dees, August 2011