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.

The integration of electric railways with renewable energy sources is crucial for advancing sustainable transportation and building clean, low-carbon, and efficient energy systems in alignment with global sustainable development goals. However, the application of photovoltaic (PV) integration into railway traction power supply systems may exacerbate resonance phenomena between electric locomotives and the traction network. It is therefore necessary to study the impedance frequency characteristics (IFCs) of traction networks to minimize harmonic resonance overvoltage. In this paper, a harmonic impedance model of the sustainable traction power supply system (STPSS) is established, and an impedance analysis method is adopted to reveal the influence law of grid-connected PV inverters on the IFCs of STPSSs. Additionally, to improve the stability of STPSSs, a multi-parameter co-tuning method based on an improved particle swarm optimization algorithm is proposed. This method constructs a multi-objective function that includes resonance frequency, impedance magnitude, and filtering cost, thereby realizing the automatic optimization of the control parameters and filtering parameters of PV inverters. The results demonstrate a 56% reduction in the maximum impedance magnitude within the 0–5 kHz frequency range and a 10.8% cost reduction in the LCL filter implementation, confirming the effectiveness of the proposed optimization model. Results show that the maximum impedance magnitude of the optimized system in the frequency range of 0–5 kHz can be reduced by 56%. Moreover, the cost of LCL filters can be reduced by 10.8% through component value optimization. These findings validate the effectiveness of the proposed method.