Energy Scheduling of Electric Vehicles for Electricity Market Participation

University dissertation from Chalmers University of Technology

Abstract: Global policy targets to reduce greenhouse gas emissions has led to increased interest in electric vehicles (EV) and their integration into the electricity network. Batteries in EVs offer flexibility from the demand side that could potentially compete against generating resources for providing power system services. Existing power markets, however, are not well suited to encourage direct participation of flexible demand from small consumers such as EV owners. The introduction of an aggregator agent with the functions of gathering and representing the energy needs of EV owners in electricity markets could prove useful in this regard. In this thesis, mathematical models are developed for optimizing the EV aggregator agent's: a) energy schedule for day-ahead electricity market participation, b) energy schedule for regulating power market (RPM) participation and c) energy portfolio to determine the power contracts to be obtained from forward electricity market. The modeling is done by accumulating individual vehicle batteries and treating them as a single large battery. The centralized charging and discharging of this battery is then scheduled based on the traveling needs of the EV owners determined by an aggregated driving profile and the cumulative electrical energy needs of vehicles over the optimization horizon. Two methods for scheduling EV demand, named as joint scheduling method (JSM) and aggregator scheduling method (ASM), are presented. The developed methods are then applied on selected test systems to observe the effects of EV demand scheduling on prices in the day-ahead, regulating power and retail markets. The results from the day-ahead market case study indicate that the scheduling of EV energy using JSM at high EV penetration levels of 75-100% could lead to lowering of day-ahead market prices as compared to a simpler control method such as fixed period charging. Results from RPM case study indicate that EV aggregator could potentially perform arbitrage provided that they plan and bid competitively against other market players, while considering the additional costs associated with vehicle-to-grid discharge. The case study results from energy portfolio optimization of the aggregator point to the monetary benefits from demand flexibility of EV batteries to both the electricity retailer, in the form of increased profits, and to EV owners through higher cost savings. It was found that the savings by customers could be attained provided that the ratio of variable to fixed price retail contracts is greater than 30:70 for a 10% EV penetration level and exceeds 50:50 for a 30% EV penetration level.