Research on the Regulation and Optimization of the Integrated Energy System of the Park Taking into Account the Power Storage of Electric Vehicles

There are certain limitations to using historical data for optimizing the park's integrated energy system (PLIES). In this study, we first establish a prediction model for air conditioning load. Utilizing the dual regulation characteristics of electric vehicles (EV s), the Monte Carlo method is employed to predict EV charging load and develop an orderly charging and discharging model for EVs. Finally, we construct a collaborative optimization scheduling model for PLIES, which includes photovoltaic power generation systems, wind power generation systems, batteries, energy storage tanks, and EV s. Case analysis of four scenarios shows that orderly charging and discharging of EV s significantly reduces the proportion of electricity consumed by the air-conditioning system during peak periods, and can reduce operating costs by 10.2-11.3% under typical conditions. After incorporating the load prediction model, operating costs can be further reduced by 1.9-3.0%. Therefore, the proposed scheduling model effectively reduces the operating cost of the PLIES.