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MINIMIZING THE INTEGRATION COSTS
OF WIND USING CURTAILMENT AND
ELECTRIC VEHICLE CHARGING
Allison Weis
Advisors: Paulina Jaramillo and Jeremy Michalek
Department of Engineering and Public Policy
Carnegie Mellon University
October 11, 2011
The integration of large amounts of wind power is an
increasingly important issue in the United States.
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Required by Renewable Portfolio Standards (RPS)
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29 States
Up to 40% of produced electricity must come from renewable sources
Complicated by the variable and intermittent nature of wind
power
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Grid flexibility must increase to cope with the
fluctuations in wind output
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Ramp existing plants
Build additional ramping plants, such as gas
turbines
Build extra wind plants and allow for curtailment
Variably charge electric vehicles
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Model Goal
Find the optimal combination of new plants, plant
operation, and controlled electric vehicle charging
in a high wind penetration scenario to minimize
systems costs (grid and vehicle)
Current Focus: 20% RPS standard for a given
mix of fuel types
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Model Components
Optimization model with:
 Capacity Expansion
– what new plants to build, including wind plants
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Unit commitment
– plant operation
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Choosing the number of charging-controlled electric
vehicles and how they should be charged
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Model includes existing and new power plants,
electric vehicles, and non-vehicle load
Wind Plants
Conventional
Power Plants
Grid Energy
Balance
Non-vehicle Load
Electric Vehicles
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Wind Plants
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Eastern Wind Integration and Transmission Study
dataset
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EWITS identified sites necessary to meet a 30% RPS in
the Eastern Interconnect
On-shore and off-shore wind
Capacity factors and 10 min. modeled production data
from 2004-2006
Added by capacity factor (high to low) until plants
capable of meeting RPS
All remaining EWITS plants can be built if cost
effective
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Load data spatially and temporally matched to
wind data
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5 minute load data for NYISO from 2006
Wind and load data averaged to create hourly
time series
Continuous 5 day sample used for computational
feasibility
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Power Plant Fleet
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Power Plant Fleet Composition:
100%
80%
60%
Gas Combustion
Turbine
Gas Combined Cycle
40%
Oil/Gas Steam Turbine
20%
0%
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Required Wind
Coal
Nuclear
Plant size and heat rate distributions for conventional
plants were matched to NYISO
Total capacity of system
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Electric Vehicle Type
Vehicles modeled as plug-in hybrid vehicles with the
following characteristics:
Characteristic
Value
Battery Size
16 kWh
Charging Power
9.6 kW (Level 2)
Charging Efficiency
88%
Vehicle Premium
$8000*
*Argonne National Lab “Multi-Path Transportation Futures Study : Vehicle
Characterization and Scenario Analyses” (2009) , estimate for 2015 PHEV-40
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Sample vehicle driving profiles chosen to match
aggregate characteristics of all NHTS data.
Weighted sample taken from the National Household
Travel Survey to match aggregate characteristics:
 Percent of vehicles of vehicles at home, work,
driving, or elsewhere at every time step
 Average number of miles driven in every time step
 Average number of cumulative miles driven in every
previous time step
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Optimize the Mixed-Integer Linear Problem
Using the Cplex Solver
Objective:
min 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑜𝑠𝑡𝑠 + 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑐𝑜𝑠𝑡𝑠 +
premium
gas savings
𝑝𝑙𝑎𝑛𝑡 𝑜𝑝𝑒𝑟𝑡𝑎𝑡𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠
𝑡
Choice Variables:
Constraints:
 Number of new wind and
conventional plants to build
 Operation of every plant in
every time step
 Number of electric vehicles
 Vehicle charging in every
time step
 Load = Generation
 Meet RPS Standard
 Power plant operating constraints
 Ramp rate
 Minimum on and off times
 Minimum generation levels
 Electric vehicle constraints
 Charging rate
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 Battery capacity
Preliminary Model Output
5 Day Sample Schedule
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Preliminary Results
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Very few controlled charging vehicles can help
reduce system costs in the current model (0-25)
Build extra wind capacity is reducing system costs
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Without curtailment: $28 million
With curtailment: $20 million
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Current Model Limitations
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Hourly time step
Perfect knowledge of wind and load (no forecasting)
 Both reduce the need for grid flexibility
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No transmission constraints
No emissions costs
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Sensitivities to be investigated
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Vehicle characteristics
Charging Scenario
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Other regions
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Home only
Work and home
See the effect of different correlations between wind
and load
Fleet composition
RPS Level
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Questions and Feedback
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Related work has value in incorporating electric
vehicle charging
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Sioshansi and Denholm calculated the value of
controlled charging and vehicle-to-grid services
with a unit commitment model of ERCOT (Texas)
Wang et. al. calculated the benefit of a set number
of electric vehicles with 20% wind power in Illinois
with a set number of power plants
Pacific Northwest National Lab calculated the
number of electric vehicles necessary to provide
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Policy Implications
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How critical is it to include a grid-to-vehicle
communications protocol in the standards for electric
vehicle chargers?
Will the shift of DOE funding to electric vehicle
research away from stationary technology still
improve grid management?
Consequences of different cost structures under
different RPS standards
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EWITS Data Set
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Model
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Explanation of Vehicle Profile choosing
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