{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:17:46Z","timestamp":1753881466247,"version":"3.41.2"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"content-version":"vor","delay-in-days":301,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFB0905101"],"award-info":[{"award-number":["2018YFB0905101"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Aiming at the problems of complex structures, variable loads, and fluctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double\u2010layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second\u2010layer optimization is based on the optimization result of the first layer. The first layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage offset, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the first layer and the characteristics of different DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double\u2010layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.<\/jats:p>","DOI":"10.1155\/2019\/5367403","type":"journal-article","created":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T23:30:43Z","timestamp":1572391843000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Optimal Allocation Strategy for Multienergy Networks Based on Double\u2010Layer Nondominated Sorting Genetic Algorithms"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5001-8853","authenticated-orcid":false,"given":"Min","family":"Mou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8478-5279","authenticated-orcid":false,"given":"Da","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yuhao","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1428-7009","authenticated-orcid":false,"given":"Wenguang","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9984-4526","authenticated-orcid":false,"given":"Jiongming","family":"Ruan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0117-1736","authenticated-orcid":false,"given":"Dongdong","family":"Ke","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tec.2008.2005280"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2015.05.006"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-015-1685-6"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrd.2011.2177102"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2003.818693"},{"key":"e_1_2_11_6_2","first-page":"711","article-title":"A hybrid evolutionary immune algorithm for multiobjective optimization problems","volume":"20","author":"Lin Q.","year":"2016","journal-title":"IEEE Transaction on Evolutionary Computation"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0378-7796(96)01096-6"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/15325000590964254"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2006.02.003"},{"key":"e_1_2_11_10_2","doi-asserted-by":"crossref","unstructured":"KazemiA.andSadeghiM. 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