Highway Service Area Multi-Timescale Optimization Scheduling Considering the Spatiotemporal Dynamic Evolution of Electric Vehicles

With the increasing demand for long-distance travel of electric vehicles (EVs), the uncertain fast charging behavior of EVs poses great pressure on the energy system of highway service areas (HSAs). The development of information and communication technology provides new insights for promoting advance perception and coordinated optimization among various entities. In this context, this paper proposes an HSA multi-timescale optimization scheduling strategy considering the spatiotemporal dynamic evolution of EVs. Firstly, the information exchange structure among various functional departments of the highway is formulated, and the highway topology model and spatiotemporal extended EV model are established as the basis for EV charging selection and EV load prediction. Then, a multi-timescale scheduling strategy suitable for multi-energy systems in HSAs is proposed to support the economic and self-sustained operation of the system. The chance-constrained method in the day-ahead stage and the two-layer model predictive control (MPC) method in the intraday and real-time stages are employed to mitigate fluctuations in power generation and demand. The effectiveness of the proposed solution is widely validated through simulations, the results indicate that the proposed EV evolution method can effectively predict the EV load, and the scheduling strategy can ensure the economy and reliability for the operation of HSA energy system.