{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:25:23Z","timestamp":1765355123757,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015330","name":"State Grid Jiangsu Electric Power Co., Ltd.","doi-asserted-by":"crossref","award":["SGJSJY00SJJS2500138"],"award-info":[{"award-number":["SGJSJY00SJJS2500138"]}],"id":[{"id":"10.13039\/501100015330","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed by incorporating chaotic features (maximum Lyapunov exponent) and sliding-window statistical features (mean, standard deviation), significantly improving short-term prediction accuracy. Based on these high-precision forecasts, a dynamic transformer switching optimization model is established to maximize the wind farm\u2019s net profit. This model finely balances power generation revenue, wind curtailment penalties, and transformer losses (no-load and load) at a 15 min timescale. Experimental results from a wind farm in Xinjiang demonstrate that the proposed method effectively enhances the economic efficiency of wind farm operations. The study provides a valuable framework for optimizing energy storage configuration and improving profitability by leveraging accurate forecasting.<\/jats:p>","DOI":"10.3390\/a18110698","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:21:46Z","timestamp":1762194106000},"page":"698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Energy Storage Configuration Optimization Method for Wind Farm Substations Based on Wind Power Fluctuation Prediction Integrating Chaotic Features and Bidirectional Gated Recurrent Units"],"prefix":"10.3390","volume":"18","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210036, China"}]},{"given":"Zikai","family":"Fan","sequence":"additional","affiliation":[{"name":"Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210036, China"}]},{"given":"Yifei","family":"Fan","sequence":"additional","affiliation":[{"name":"Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210036, China"}]},{"given":"Jiayi","family":"Ren","sequence":"additional","affiliation":[{"name":"Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210036, China"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210036, China"}]},{"given":"Leiming","family":"Suo","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5148-340X","authenticated-orcid":false,"given":"Jinrui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Wuhan University of Technology, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122860","DOI":"10.1016\/j.jclepro.2020.122860","article-title":"What influences windpower decisions? 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