An Optimization Model for Multi-energy Supply and Demand Network Scheduling of Public Transportation Based on GNSS Trajectory Data

This study addresses the supply-demand matching optimization problem for energy replenishment in hybrid energy networks of public transit systems, encompassing multiple energy types including oil, electricity, gas, and hydrogen etc. To bridge the limitations of existing theoretical research, which predominantly focuses on single energy types, the study incorporates practical operational experiences and habits of public transit systems. Using real-world GNSS data of buses, the spatial characteristics of energy replenishment behavior are analyzed, and the concept of "potential energy demand points" is proposed. Integrating potential energy demand points with energy supply and demand nodes for different energy types, a multi-energy hybrid scheduling optimization model is developed. The model incorporates constraints such as energy type limitations and supply node capacities, ensuring alignment with real-world operational conditions. An improved genetic algorithm based on the elite strategy is proposed to solve the model, inspired by the principle of base pairing in DNA, to characterize the coexistence of multiple energy demands along a single bus line. Multiple indicators are combined to derive solutions that minimize additional deadhead costs under energy replenishment constraints, optimize the matching scheme of the supply-demand network, and evaluate the efficiency of the transit network. Taking long-term GNSS trajectory data from Beijing's public buses as a case study, a two-stage clustering algorithm is employed to identify potential energy demand points. A multi-energy supply-demand matching optimization strategy for public buses is proposed, alongside robustness tests for the network under scenarios involving random energy type configurations and the removal of critical nodes. The results demonstrate that the proposed model reduces the energy supply-demand matching costs for fuel, hydrogen, and electric bus routes by 7.12%, 9.07%, and 9.82%, respectively, compared to baseline models. Furthermore, the fitness function of the optimization algorithm improves by 5.18%. These findings contribute to the optimization of energy supply-side configurations and the intelligent management of energy demand. Additionally, the study emphasizes the need for coordinated adjustments of energy demand and replenishment in mixed-energy public transit operations to achieve supply-demand balance. The construction and operation of critical energy replenishment nodes are highlighted as essential for enhancing network stability and resource utilization efficiency.