In the journey of electrified railways toward the "dual-carbon" goals, the integration of photovoltaic (PV) and energy storage systems into traction power supply systems has become a critical pathway for energy conservation and emission reduction. However, the uncertainty of source-load and the limitations of traditional scheduling strategies hinder the efficient operation of the system. This study draws in-depth inspiration from the low-carbon scheduling methods of power systems incorporating wind power, innovatively introduces fuzzy chance-constrained programming theory, and comprehensively optimizes the day-ahead coordinated scheduling model for PV-storage-based Railway Power Conditioners (RPC). By constructing a more practical uncertainty quantification model, refining the optimization objective function centered on comprehensive costs, expanding constraint conditions covering multiple operational limits, and employing advanced solving algorithms, the accuracy of the model is ensured. Comparative case analyses demonstrate that the improved scheduling strategy achieves remarkable results in enhancing the low-carbon economy and robustness of the system. It provides a more scientific and efficient solution for energy scheduling in electrified railways, strongly promoting the sustainable development of the industry.
Ma et al. (Sun,) studied this question.