Self-Consistent Multi-Energy Flow Coordination Optimization for Hydrogen Energy Railway with Tank Car in Hydrogen Energy Parks

The multi-energy flow coordination optimization of the self-sufficient hydrogen energy park is becoming a research focus. However, without explicit consideration of tank car, the optimization remains incomplete, thereby undermining practical applicability. In this paper, a Dynamic Adaptive Grey Wolf Optimization (DA-GWO) algorithm is proposed for self-consistent multi-energy flow coordination optimization, considering hydrogen energy-based tank cars in hydrogen railway energy parks. First, a foundational model of the hydrogen-based railway energy system was constructed that integrates green non-dispatchable units such as wind power and photovoltaics, as well as dispatchable units such as fuel cells, gas boilers, and cogeneration units. Given the diversity and complexity of in-service hydrogen railway tank cars, a probabilistic model of daily charging behaviour was constructed using a Monte Carlo method to simulate real-world operating conditions of tank cars, thereby enhancing the reliability of the hydrogen-powered railway model. Considering the diverse and complex units in the self-consistent hydrogen energy park for hydrogen-powered railways, a DA-GWO algorithm was constructed for the multi-energy flow optimization. Through a self-adaptive parameter adjustment, the algorithm’s global optimization performance is improved. Finally, the model parameters were further adjusted with data from a coastal Chinese city, and the optimization experimental tests were conducted to validate the proposed method. From the results, the proposed method can save at least 6.7% cost compared with the grey wolf optimization method and the PSO (Particle Swarm Optimization) optimization method.