Optimal control strategy for bidirectional EV charging hubs contributing to energy market

This paper proposes two algorithmic approaches for trading the power and energy flexibility offered by electric vehicle (EV) charging hubs equipped with vehicle-to-grid (V2G) technology. Thereby, it targets mutual benefits in terms of economic profitability of the charging hub and ancillary services provision. The proposed approaches integrate portfolio optimization principles with model predictive control (MPC) to address two key objectives: energy transaction optimization and participation in ancillary services. The first algorithm employs a single-layer MPC with a mixed-integer linear program (MILP) to optimize energy transactions within an ecosystem comprising EVs, stationary energy buffers, and grid supply. The second algorithm is a double-layer MPC designed to operate at a high time resolution and focuses on optimal power flow to meet the demands of the grid. In this algorithm, all the EVs are considered as one big battery with accumulated capacity at the higher layer to find optimal energy packets and a static MILP at the lower layer to compute power distribution across the fleet of EVs. Both algorithms employ a set of relaxation inequalities to transform the problem from a mixed-integer bilinear programming (MIBP) to an MILP, ensuring computational tractability and enabling real-time performance. The proposed control strategies can be applied to facilitate demand response (DR) programs at different time scales by imposing constraints on energy and power consumption, respectively. They provide a generic platform for the integration of a variety of ancillary services in the primary and secondary control energy markets. The effectiveness of the proposed control structures is demonstrated through simulation scenarios. • Portfolio optimization for managing energy transactions in electric charging hubs. • Electric vehicles and energy buffers flexibility for supporting the power grid. • Enabling electric charging hubs to trade energy in the energy market.