{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:37:11Z","timestamp":1705106231499},"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 increasing use of renewable energy in the distribution network has led to several issues such as large load flow calculation and frequent overvoltage, making it challenging to ensure the quality of power supply. This paper presents a two-level optimization strategy that leverages the cooperation of distributed power supply and active distribution network to address these challenges. The upper-level model aims to minimize distribution network loss, improve voltage stability, and reduce the total cost of network operation. The lower-level model focuses on coordinated control of distributed power supply and other devices to enhance user demand response and ensure real-time dispatching reliability. To solve the model, this paper utilizes an improved binary particle swarm optimization (BPSO) and genetic algorithm (GA) based on the model\u2019s characteristics. The results demonstrate that the proposed bi-level optimization method effectively enhances voltage distribution and smooths the load peak-valley difference.<\/jats:p>","DOI":"10.3233\/faia231207","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:02Z","timestamp":1705064162000},"source":"Crossref","is-referenced-by-count":0,"title":["Distribution Network Double-Layer Optimization Strategy Based on Distributed Generation"],"prefix":"10.3233","author":[{"given":"Xiaohu","family":"Zhu","sequence":"first","affiliation":[{"name":"State Grid Electric Power Research Institute, Wuhan Nari Limited Liability Company, Wuhan 430074, China"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Electric Power Research Institute, Wuhan Nari Limited Liability Company, Wuhan 430074, China"}]},{"given":"Lirong","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Electric Power Research Institute, Wuhan Nari Limited Liability Company, Wuhan 430074, 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\/FAIA231207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:03Z","timestamp":1705064163000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231207","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]]}}}