Two-Layer Robust Optimal Configuration of Photovoltaic–Storage–Charging Systems for Highway Microgrids Integrating Charging Load Prediction

With the increasing number of electric vehicles, charging demand in highway service areas has surged, posing significant challenges to the planning of energy facilities. This paper proposes a two-level robust optimization configuration method for highway Photovoltaic-Storage-Charging systems based on charging load forecasting. The upper-level model aims to maximize return on investment, employing a genetic algorithm for capacity optimization and constructing uncertainty sets for photovoltaic output to enhance robustness. The lower-level model utilizes the cell transmission model to simulate traffic flow and introduces cumulative prospect theory to capture the uncertainty of user charging behavior, enabling accurate charging load forecasting. Meanwhile, mixed-integer linear programming is adopted to optimize the intraday scheduling strategy of the energy storage system. The return on investment is fed back to the upper level via weighted robust aggregation, driving iterative evolution to obtain a robust optimal configuration that balances economic efficiency and service level. The proposed method is validated through a case study on the Wushen-Liangguang highways chain section. Simulation results show that the overall return on investment and photovoltaic consumption rate of the selected road section reach 20.85% and 98.53%, and the charging waiting time in most service areas is less than 5 minutes.