Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid demonstration. (15th January 2018)

• Record Type: Journal Article
• Authors: DeForest, Nicholas; MacDonald, Jason S.; Black, Douglas R.
• Is Part Of: Applied energy. Volume 210(2018)
• Page Start: 987
• Page End: 1001
• Publication Date: 2018-01-15
• Subjects: Electric vehicles -- Vehicle to grid -- Demonstration -- Ancillary services -- Frequency regulation -- Optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.07.069
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: We present an overview of an ongoing EV vehicle-to-grid demonstration project. We discuss practical issues of bidding EV capacity in frequency regulation markets. We formulate a MILP optimization model to plan EV charging and day-ahead bidding. We analyze the model sensitivity and bidding strategy to variation of key inputs. Model behavior is highly sensitive to predicted resource utilization and prices. Abstract: The Los Angeles Air Force Base Electric Vehicle Demonstration is a currently ongoing vehicle-to-grid demonstration project with the objective of minimizing the cost of operation of a fleet of approximately 30 electric vehicles (EVs) through participation in the California Independent System Operator (CAISO) frequency regulation market. To accomplish this, a hierarchical control system has been developed to optimize, plan, and control the charging, market bidding, and response to grid system operator control of the EVs. This paper presents an overview of the day-ahead optimization model component of the hierarchy. The model is a mixed integer linear program that optimizes daily EV charging and regulation capacity bids strategies in order to minimize operation costs and maximize ancillary service revenue. A deterministic approach is used due to several practical concerns of the demonstration project, including model complexity and the availability and uncertainty of input data in day-ahead decision making, and the limited size of the fleet. The model includes additional user-defined parameters to tune model behavior to better match real-world conditions and minimize the risks of uncertainty. The paper conducts scenario analysis to explore the impact of these parameters on high level model behavior and resulting bid strategy. The parameters explored include hourly regulation prices, local load conditions leading to retail demand charges, forced symmetry constraints for regulation bids, SOC penalty values to reserve higher states-of-charge in vehicles, and expected regulation resource utilization while providing reserves. These analyses show significant sensitivity in the frequency regulation bidding strategy to the regulation utilization, as well as large differences in the regulation prices between regulation up (discharging capacity) and regulation down (charging capacity). Results also suggest enforcing symmetry in regulation appears to have significant impacts in regulation revenue when there is large relative disparities between prices in the up and down direction. Finally, imposing a small cost on low SOC values significantly impacts the fleet-wide average SOC, making the system more resilient to uncertainty in the mobility demands gathered at the time of making day ahead decisions.