Jingtao Lu

Electrified railways are major industrial electricity consumers. The Grid-Source-Storage-Vehicle (GSSV) system supports a more sustainable railway power supply by improving local renewable energy utilization, strengthening multi-source energy coordination, and promoting low-carbon development. However, existing rule-based energy management strategies (EMS) remain limited in their ability to support the efficient coordinated operation of the GSSV system. Moreover, under strong source-load fluctuations, conventional optimization-based EMS often fail to provide sufficiently reliable and responsive decision-making for real-time operation of GSSV systems. To address these issues, this paper proposes a real-time EMS based on a rule-guided enhanced non-dominated sorting genetic algorithm (RG-NSGA-II). First, based on the GSSV architecture, the operating modes of the system under different working conditions are systematically analyzed, and a corresponding rule-based EMS is designed. Then, a multi-objective optimization model considering system economic performance and grid power-intake fluctuation is formulated. Furthermore, a coordination mechanism between the rule-based EMS and the optimization EMS is developed. By embedding power commands generated by the rule-based EMS into the optimization EMS and regulating their activation through a time threshold, the proposed method improves the reliability, economic efficiency, and real-time performance of the EMS. Finally, the proposed method is validated, and the results show that the proposed real-time EMS ensures effective utilization of RE, improves power coordination efficiency and operational adaptability under fluctuating operating conditions, and delivers tangible environmental and economic sustainability benefits for electrified railway power supply systems.