Multi-Objective Optimization and Discrete Action Set Based Rolling Optimal Lookahead Schedule for Coordinating Electric Vehicles

Addressing the arbitrage revenue of electric vehicles (EVs) and the mitigation of grid load fluctuations, this paper proposes an approach aiming at rolling optimal lookahead schedule to coordinate the EV charging (or discharging) by using a multi-objective optimization (MOO) algorithm equipped with a discrete action (or behavior) set based EV aggregation and the associated price-driven strategy. Consequently, a comprehensive mechanism is developed, in which, for more judicious trade-off decision-making based on the maximum EV arbitrage revenue and the minimum grid load fluctuation, a typical MOO algorithm, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D), is adopted on the evaluation over entire schedule period; for decreasing excessive computational and communication costs, a finite discrete set associated with charging or discharging behaviors is proposed, and for organizing such EV behaviors, a price-driven auction algorithm is developed to respond to time-of-use (TOU) tariffs in each time slice. The simulation section utilizes the data from state-of-the-art to validate the proposed approach, the results show that the performance is promising.