Energy storage optimization strategy for photovoltaic-storage-charging microgrid at highway service areas

Abstract To enhance the utilization rate of photovoltaic (PV) systems in highway service areas and reduce energy costs, this paper proposes an optimization model for the configuration and scheduling of energy storage capacity in a PV-storage-charging microgrid for highway service areas. A novel solving algorithm is employed for the simulation analysis. First, a mathematical model of the highway service area microgrid is established, and the electric vehicle charging load in the service area is analyzed using Monte Carlo simulations. Based on this analysis, a microgrid load model for the service area is developed. Secondly, from the economic perspective of the highway microgrid, a bilevel optimization model is proposed to achieve comprehensive optimization of both the configuration and scheduling of the energy storage system. The model is solved using the EDO-MILP (Exponential Distribution Optimizer-Mixed-Integer Linear Programming) algorithm. Finally, the Pan-Da Highway (within Sichuan) distributed PV-storage demonstration project is taken as a case study for simulation and optimization. The simulation results underscore the significance of deploying 2856 kWh/714 kW of energy storage equipment and optimizing its scheduling. Compared to the no-storage scenario, this configuration leads to a substantial reduction of 36.25% in annual costs. Furthermore, when compared to an empirical scenario, the cost savings reach 30.74%. These findings serve as invaluable references for configuring photovoltaic storage systems in newly developed highway service areas, guiding the design and execution of strategies aimed at enhancing the self-sufficiency of green energy. This research not only establishes a theoretical foundation but also presents a practical solution framework for promoting broader energy integration across the entire highway network.