{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:24:25Z","timestamp":1751091865891},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>The traditional optimal dispatching of distribution network usually takes the demand load measurement as the rigid load. However, as the demand for electricity continues to rise, a large amount of distributed renewable energy generation is incorporated into the power grid. It is difficult to ensure the safe and reliable operation of the electrical system if only the optimal dispatching of the generating side is carried out. This paper proposes a two-layer optimal distribution network scheduling strategy that considers the demand side load. The upper layer model chooses controllable load output as the objective and aims to achieve the lowest overall operation cost of the distribution network, as well as the lowest comprehensive load fluctuation to reflect the power supply reliability of the distribution network. The adaptive particle swarm optimization algorithm based on genetic algorithms is implemented to solve the model based on its characteristics. Simulation verification is performed on the IEEE33-node distribution system, and the results depict that the proposed two-layer optimization method reduces the operating cost of the distribution network, effectively stabilizes the peak valley difference of load, and improves the overall stability of the distribution network.<\/jats:p>","DOI":"10.3233\/faia231208","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:02Z","timestamp":1705064162000},"source":"Crossref","is-referenced-by-count":1,"title":["Two-Layer Optimal Dispatching Strategy of Distribution Network Considering Demand Side Load"],"prefix":"10.3233","author":[{"given":"Qing","family":"Wang","sequence":"first","affiliation":[{"name":"State Grid Electric Power Research Institute, Wuhan Nari Limited Liability Company, Wuhan 430074, China"}]},{"given":"Xiaohu","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Grid Electric Power Research Institute, Wuhan Nari Limited Liability Company, Wuhan 430074, China"}]},{"given":"Xiaozhuang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Wuhan Huagong Laser Engineering Co., Ltd., Wuhan 430078, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231208","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:04Z","timestamp":1705064164000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231208","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}