Dongchun Xie

This study proposes a mixed-integer linear programming(MILP) framework for the joint capacity sizing and hourly dispatch of a grid-connected industrial park electricity–hydrogen system with fixed daily hydrogen demand. Unlike conventional formulations that prescribe an hourly hydrogen-load profile, the proposed model adopts a daily cumulative delivery constraint, allowing the electrolyzer to flexibly schedule hydrogen production according to renewable generation, electricity prices, and storage states. Three configurations are compared under consistent technical and economic assumptions: external hydrogen procurement (EHP), local renewable hydrogen production(LRH), and a bidirectional electricity–hydrogen system (BEHS) with hydrogen-to-power reconversion. The model jointly optimizes photovoltaic generation, wind turbines, battery energy storage, electrolyzers, hydrogen storage tanks, and fuel cells over a full 8760-hour horizon. Under the baseline conditions, the optimized BEHS achieves a total annualized cost of 5.865 million CNY/year and a renewable energy utilization rate of 99.49%, while satisfying both electricity and hydrogen demands without shortages. Compared with EHP, LRH reduces the total annualized cost by approximately 8.5%, whereas introducing the fuel cell provides an additional reduction of approximately 0.56%. The results indicate that local renewable hydrogen production is the main source of economic benefit, while hydrogen-to-power reconversion primarily provides supplementary flexibility. Sensitivity analyses further show that daily hydrogen demand determines the scale of the local hydrogen-production system, grid purchase capacity strongly affects the value of bidirectional coupling, and higher carbon price promotes renewable generation and battery deployment while reducing grid electricity use and indirect emissions.