Deep learning-aided stochastic integrated optimization of highway service area renewable energy systems adopting a novel topology

Utilizing renewables locally for EV charging in highway service areas (HSAs) represents an optimal synergy between EVs and renewable energy. However, uncertainties in collective EV charging load and renewable generation make it challenging for HSA renewable systems to maintain real-time power balance and long-term sustainability with limited grid support. To address this, this work develops a deep learning-aided integrated optimization framework for system control and design. The method introduces a data-driven representation of complex design constraints under stochastic operation environments and evaluates the expected optimal system performance. In parallel, a novel system topology is engineered to enhance the matching between EV charging load and photovoltaic generation. Compared with the traditional isolated design, conditioned on the same capital investment, the mutual-aid system demonstrates a 20.2 % average improvement in comprehensive performance and a 6.4 % increase in self-sustain rate. The proposed method also regulates the interaction between the HSA renewable system and the grid by conducting at most 99.2 % of requests for grid supply through regular and economy day-ahead electricity purchases, which leads to a friendly and economical interaction among EVs, renewable systems, and the grid.