Development of Optimal Bidding Strategy for an Electric Vehicle Aggregator in a Real-Time Electricity Market

Abstract

An electric vehicle aggregator (EVA) can participate strategically in an electricity market, on behalf of a large number of electric vehicle (EV) owners. Strategic bidding optimization and charging management for each EVA can benefit both the power system and EVA. In this work, a hierarchical optimization model for developing optimal bidding strategy and charging management in a real-time (RT) electricity market is proposed for each EVA to minimize its costs. The developed model is formulated as a two-level optimization problem in the framework of the well-established model predictive control (MPC). The first level optimization is to determine the optimal hourly RT bidding strategy of the EVA by a two-stage stochastic predictive optimization, with the charging scheduling of EVs formulated as a linear recourse problem. The second level optimization is to manage the RT charging of EVs in each time interval according to the cleared bidding quantity in the RT market, which can be formulated as a predictive linear programming problem. Both the first level and second level problems are optimized using the commercial solver Gurobi interfaced with Python language. Case studies demonstrate that the EVA can strategically participate in the RT electricity market and attain more benefits by utilizing the proposed strategy.