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However, the SoC balance scheme induced by the power allocation through existing droop controllers can cause the capacity parameters of battery cells to be unequal to the droop coefficient, which is the result of battery capacity degradation. Under this limitation, previous capacity-based droop controllers and the secondary controllers are no longer suitable to address the imprecise power sharing and frequency restoration caused by this problem. Therefore, a power allocation scheme based on the current SoC level ratio is designed to induce a new droop controller and ensure that the SoC simultaneously drops to 0. In order to restore frequency in a distributed manner and obtain the SoC level ratio, a distributed nominal frequency controller, SoC average estimator, and power-sharing controller are designed based on multi-agent systems in both asymptotic and finite-time manners. In the asymptotic scheme, the steady-state performance of the SoC estimator is adjustable, and power sharing and frequency restoration are zero errors. In the finite-time scheme, the influence of parameters on convergence time is well analyzed, and some conservative calculation methods are provided. Several time-domain simulation examples are designed on an improved IEEE-57 bus system to verify the distribution of asymptotic and finite-time schemes.<\/jats:p>","DOI":"10.1177\/01423312241287248","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T02:16:22Z","timestamp":1733192182000},"page":"515-527","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Distributed secondary frequency control and state of charge (SoC) balance for droop-controlled battery energy storage systems (BESSs) with a unified SoC relative variation rate"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3788-700X","authenticated-orcid":false,"given":"Yalin","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"},{"name":"Tianjin Key Laboratory of Interventional Brain\u2013Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3565-4800","authenticated-orcid":false,"given":"Zhongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"},{"name":"Tianjin Key Laboratory of Interventional Brain\u2013Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2747-9635","authenticated-orcid":false,"given":"Fuyong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"},{"name":"Tianjin Key Laboratory of Interventional Brain\u2013Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1415-4073","authenticated-orcid":false,"given":"Zengqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"},{"name":"Tianjin Key Laboratory of Interventional Brain\u2013Computer Interface and Intelligent Rehabilitation, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"e_1_3_3_2_1","first-page":"1","article-title":"A review of modeling and applications of energy storage systems in power grids","volume":"111","author":"Calero F","year":"2022","unstructured":"Calero F, Ca\u00f1izares CA, Bhattacharya K, et al. 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