Post on 23-Feb-2016
description
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MINIMIZING THE INTEGRATION COSTS OF WIND USING CURTAILMENT AND
ELECTRIC VEHICLE CHARGING
October 11, 2011
Allison WeisAdvisors: Paulina Jaramillo and Jeremy Michalek
Department of Engineering and Public PolicyCarnegie Mellon University
The integration of large amounts of wind power is an increasingly important issue in the United States.
Required by Renewable Portfolio Standards (RPS) 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
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
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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
Model Components
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Optimization model with: Capacity Expansion
– what new plants to build, including wind plants
Unit commitment – plant operation
Choosing the number of charging-controlled electric vehicles and how they should be charged
Model includes existing and new power plants, electric vehicles, and non-vehicle load
6Electric Vehicles
Conventional Power Plants
Wind Plants
Non-vehicle Load
Grid Energy Balance
Wind Plants
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Eastern Wind Integration and Transmission Study dataset 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
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
Power Plant Fleet
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Power Plant Fleet Composition:
Plant size and heat rate distributions for conventional plants were matched to NYISO
Total capacity of system
0%
20%
40%
60%
80%
100%Required WindGas Combustion TurbineGas Combined CycleOil/Gas Steam TurbineCoalNuclear
Electric Vehicle Type
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Characteristic
Value
Battery Size 16 kWh
Charging Power 9.6 kW (Level 2)
Charging Efficiency
88%
Vehicle Premium
$8000*
Vehicles modeled as plug-in hybrid vehicles with the following characteristics:
*Argonne National Lab “Multi-Path Transportation Futures Study : Vehicle Characterization and Scenario Analyses” (2009) , estimate for 2015 PHEV-40
Sample vehicle driving profiles chosen to match aggregate characteristics of all NHTS data.
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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
Optimize the Mixed-Integer Linear Problem Using the Cplex Solver
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Objective:
Choice Variables: 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
Constraints: Load = Generation Meet RPS Standard Power plant operating
constraints Ramp rate Minimum on and off
times Minimum generation
levels Electric vehicle constraints
Charging rate Battery capacity
premium gas savings
Preliminary Model Output
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5 Day Sample Schedule
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 Without curtailment: $28 million With curtailment: $20 million
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
No transmission constraints No emissions costs
Sensitivities to be investigated
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Vehicle characteristics Charging Scenario
Home only Work and home
Other regions See the effect of different correlations
between wind and load Fleet composition RPS Level
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Questions and Feedback
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
Policy Implications 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