The integration of renewable energy sources into railway traction substations has become a strategic priority to reduce operational costs and CO 2 emissions. This paper presents a novel optimization framework specifically designed for Hybrid Traction Substations (HTS), which differ from conventional microgrids due to their traction-specific load dynamics, bidirectional energy flows, and grid-integration constraints. A stochastic multi-objective optimization model is developed to jointly minimize total system cost and maximize renewable energy utilization, while accounting for Moroccan grid code restrictions, including the prohibition of reverse power injection and the absence of braking energy recovery. Solar irradiance uncertainty is captured via historical data clustering with k-means, enabling efficient computation and robust sizing. The optimization is solved with a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, demonstrating reliable performance. Seasonal load and irradiance profiles are incorporated to ensure representative results, and a sensitivity analysis on key economic and operational parameters confirms the robustness of the optimized solutions. A case study of the Asilah substation highlights Pareto trade-offs between investment cost, operational cost, and renewable penetration, confirming the framework’s effectiveness, scalability, and practical relevance for decarbonizing railway electrification.
Ouaid et al. (Sun,) studied this question.