Improving Sparrow Search Algorithm for Optimal Operation Planning of Hydrogen-Electric Hybrid Microgrids Considering Demand Response

Microgrid operation planning is crucial for ensuring the safe and efficient output of distributed energy resources (DERs) and stable operation of the microgrid power system. The integration of hydrogen fuel cells into microgrids can increase the absorption rate of renewable energy, while the incorporation of lithium batteries facilitates the adjustment of microgrid power supply voltage and frequency, ensuring the three-phase symmetry of the system. This paper proposes an economic scheduling method for a grid-connected microgrid that considers demand response and combines hydrogen and electricity. Based on the operating costs of renewable energy, maintenance and operation costs of nonrenewable energy, interaction costs between the microgrid and main grid, and pollution control costs, an optimization model for dispatching a hydrogen–electric hybrid microgrid under grid-connected mode is established. The primary objective is to minimize the operating cost, while the secondary objective is to minimize the impact on the user’s power consumption comfort. Therefore, an improved demand response strategy is introduced, and an enhanced sparrow search algorithm (ISSA) is proposed, which incorporates a nonlinear weighting factor and improves the global search capability based on the sparrow search algorithm (SSA). The ISSA is used to solve the optimal operation problem of the demand-response-integrated microgrid. After comparison with different algorithms, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), sooty tern optimization algorithm (STOA), and dingo optimization algorithm (DOA), the results show that the proposed method using demand response and ISSA achieves the lowest comprehensive operating cost for the microgrid, making the microgrid’s operation safer and with minimum impact on user satisfaction. Therefore, the feasibility of the demand response strategy is demonstrated, and ISSA is proved to have better performance in solving optimal operation planning problems for hydrogen–electric hybrid microgrids.