Optimization of multi-objective capacity allocation and performance analysis for integrated energy systems considering hydrogen storage

As the global energy mix transforms and the demand for clean energy increases, integrated energy systems (IES) are gaining attention for their ability to enable the complementary and optimal allocation of multiple energy sources. However, the volatility of renewable energy can affect the stability and reliability of energy supply, thereby limiting its penetration in IES. Hydrogen energy storage, as a novel energy storage solution, offers advantages such as a long regulation period and large storage capacity. These characteristics can promote the consumption of renewable energy, reduce system operating costs, and improve overall energy efficiency. This paper proposes a multi-objective capacity optimization allocation model based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. An operation strategy of “set electricity by cooling and heat by electricity” is also proposed. Three scenarios are established to optimize the scheduling of power generation, energy storage, and loads in the IES. The performance of the IES is analyzed on typical days to verify the feasibility of the proposed strategy and model. The results show that, compared with the system without energy storage, the system configured with hydrogen storage increases the renewable energy consumption rate by 6.54 %, and reduces carbon emissions and grid interaction power by 4.78 % and 45.01 %, respectively. Compared with the system configured with batteries and water storage tank only, the renewable energy consumption rate increases by 0.33 %, and carbon emissions and grid interaction power are reduced by 3.35 % and 19.08 %, respectively. These findings demonstrate the effectiveness of the proposed method and the research significance of optimizing device configurations.