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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\u2013electric 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\u2019s 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\u2019s 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\u2013electric hybrid microgrids.<\/jats:p>","DOI":"10.3390\/sym15040919","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T03:14:09Z","timestamp":1681701249000},"page":"919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Improving Sparrow Search Algorithm for Optimal Operation Planning of Hydrogen\u2013Electric Hybrid Microgrids Considering Demand Response"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3446-1063","authenticated-orcid":false,"given":"Yuhao","family":"Zhao","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Institute of Intelligent Manufacturing, GDAS, Guangzhou 510070, China"},{"name":"Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7539-7060","authenticated-orcid":false,"given":"Yixing","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}]},{"given":"Zhiheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Manufacturing, GDAS, Guangzhou 510070, China"},{"name":"Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China"}]},{"given":"Shouming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2436-037X","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Manufacturing, GDAS, Guangzhou 510070, China"},{"name":"Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"key":"ref_1","unstructured":"Li, X., and Fang, L. 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