Minimizing the Integration Costs of Wind Using Curtailment and Electric Vehicle Charging

1. Allison Weis Advisors:Paulina Jaramillo and Jeremy Michalek Department of Engineering and Public Policy Carnegie Mellon University Minimizing the Integration Costs of Wind Using Curtailment and Electric Vehicle Charging October 11, 2011

2. 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

3. 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

4. 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

5. Model Components 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

6. Model includes existing and new power plants, electric vehicles, and non-vehicle load Wind Plants Conventional Power Plants Transmission Grid Energy Balance Non-vehicle Load Electric Vehicles

7. Wind Plants • 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

8. Load data spatially and temporally matched to wind data • 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

9. Power Plant Fleet • Power Plant Fleet Composition: • Plant size and heat rate distributions for conventional plants were matched to NYISO • Total capacity of system

10. Electric Vehicle Type 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

11. 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

12. Optimize the Mixed-Integer Linear Problem Using the Cplex Solver Objective: premium gas savings • 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

13. Preliminary Model Output 5 Day Sample Schedule

14. Preliminary Results w/ curtailment Total cost = 4.6715e+009 # Vehicles = 26.8091 • 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

15. Current Model Limitations • Hourly time step • Perfect knowledge of wind and load (no forecasting)  Both reduce the need for grid flexibility • No transmission constraints • No emissions costs

16. Sensitivities to be investigated • 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

17. Questions and Feedback

18. Related work has value in incorporating electric vehicle charging • 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

19. 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

20. EWITS Data Set

21. Model

22. Explanation of Vehicle Profile choosing