Capacity Optimization of Integrated Energy System for Hydrogen-Containing Parks Under Strong Perturbation Multi-Objective Control

To address the issue of significant perturbations caused by the limited flexibility of clean energy grid integration, along with the combined effects of electric vehicle charging demand and the uncertainty of high-penetration intermittent energy in the integrated energy system (IES), a capacity optimization method for the IES subsystem of a hydrogen-containing chemical park, accounting for strong perturbations, is proposed in the context of the park’s energy usage. Firstly, a typical scenario involving source-load disturbances is characterized using Latin hypercube sampling and Euclidean distance reduction techniques. An energy management strategy for subsystem coordination is then developed. Building on this, a capacity optimization model is established, with the objective of minimizing daily integrated costs, carbon emissions, and system load variance. The Pareto optimal solution set is derived using a non-dominated genetic algorithm, and the optimal allocation case is selected through a combination of ideal solution similarity ranking and a subjective–objective weighting method. The results demonstrate that the proposed approach effectively balances economic efficiency, carbon reduction, and system stability while managing strong perturbations. When compared to relying solely on external hydrogen procurement, the integration of hydrogen storage in chemical production can offset high investment costs and deliver substantial environmental benefits.