{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,15]],"date-time":"2026-11-15T00:58:32Z","timestamp":1794704312691,"version":"4.9.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-14640-6","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:22:44Z","timestamp":1755692564000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RETRACTED ARTICLE: Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets"],"prefix":"10.1038","volume":"15","author":[{"given":"G. Madhu","family":"Mohan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T. Anil","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Srujana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasser","family":"Fouad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexey","family":"Mikhaylov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Kitmo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ch. Rami","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"issue":"5","key":"14640_CR1","doi-asserted-by":"publisher","first-page":"e12331","DOI":"10.1002\/eng2.12331","volume":"3","author":"M Sarwar","year":"2021","unstructured":"Sarwar, M., Siddiqui, A. S., Jaffery, Z. A. & Kothari, D. P. Bid responsive generation rescheduling for congestion management in electricity market. Eng. Rep. 3 (5), e12331 (2021).","journal-title":"Eng. Rep."},{"issue":"3","key":"14640_CR2","doi-asserted-by":"publisher","first-page":"106715","DOI":"10.1016\/j.tej.2020.106715","volume":"33","author":"A Narain","year":"2020","unstructured":"Narain, A., Srivastava, S. K. & Singh, S. N. Congestion management approaches in restructured power system: Key issues and challenges. The Electricity J. 33(3), 106715 (2020).","journal-title":"The Electricity J."},{"issue":"5","key":"14640_CR3","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1016\/j.enconman.2009.01.029","volume":"50","author":"BK Panigrahi","year":"2009","unstructured":"Panigrahi, B. K. & Pandi, V. R. Congestion management using an adaptive bacterial foraging algorithm. Energy Convers. Manag. 50(5), 1202\u20131209 (2009).","journal-title":"Energy Convers. Manag."},{"key":"14640_CR4","doi-asserted-by":"publisher","first-page":"110700","DOI":"10.1016\/j.epsr.2024.110700","volume":"234","author":"A Ewerszumrode","year":"2024","unstructured":"Ewerszumrode, A., Erle, N., Krahl, S. & Moser, A. An iterative approach to grid topology and redispatch optimization in congestion management. Electr. Power Syst. Res. 234, 110700 (2024).","journal-title":"Electr. Power Syst. Res."},{"key":"14640_CR5","doi-asserted-by":"publisher","first-page":"5118","DOI":"10.1016\/j.egyr.2025.04.035","volume":"13","author":"T Yamazaki","year":"2025","unstructured":"Yamazaki, T. et al. Method for optimizing reserve in congestion management using weather forecast information with uncertainty. Energy Rep. 13, 5118\u20135132 (2025).","journal-title":"Energy Rep."},{"key":"14640_CR6","doi-asserted-by":"publisher","first-page":"122892","DOI":"10.1016\/j.apenergy.2024.122892","volume":"361","author":"M Girod","year":"2024","unstructured":"Girod, M. et al. Bid filtering for congestion management in European balancing markets\u2013A reinforcement learning approach. Appl. Energy. 361, 122892 (2024).","journal-title":"Appl. Energy"},{"issue":"8","key":"14640_CR7","doi-asserted-by":"publisher","first-page":"11456","DOI":"10.1016\/j.egyr.2022.08.267","volume":"1","author":"K Paul","year":"2022","unstructured":"Paul, K., Sinha,  P., Mobayen, S., El-Sousy, F. F. & Fekih, A. A novel improved crow search algorithm to alleviate congestion in power system transmission lines. Energy Rep. 1 (8), 11456\u201311465 (2022).","journal-title":"Energy Rep."},{"issue":"10","key":"14640_CR8","doi-asserted-by":"publisher","first-page":"2548","DOI":"10.1049\/iet-gtd.2015.1555","volume":"10","author":"S Verma","year":"2016","unstructured":"Verma, S. & Mukherjee, V. Optimal real power rescheduling of generators for congestion management using a novel ant Lion optimiser. IET Gener. Transmiss. Distribut. 10 (10), 2548\u20132561 (2016).","journal-title":"IET Gener. Transmiss. Distribut."},{"issue":"1","key":"14640_CR9","doi-asserted-by":"publisher","first-page":"183","DOI":"10.3390\/en11010183","volume":"12","author":"JR Chintam","year":"2018","unstructured":"Chintam, J. R. & Daniel, M. Real-power rescheduling of generators for congestion management using a novel satin Bowerbird optimization algorithm. Energies 12 (1), 183 (2018).","journal-title":"Energies"},{"issue":"48","key":"14640_CR10","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.asoc.2016.07.011","volume":"1","author":"J Wang","year":"2016","unstructured":"Wang, J., Liu, F., Song, Y. & Zhao, J. A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl. Soft Comput. 1 (48), 281\u2013297 (2016).","journal-title":"Appl. Soft Comput."},{"issue":"13(3)","key":"14640_CR11","first-page":"3266","volume":"5","author":"R Peesapati","year":"2018","unstructured":"Peesapati, R., Yadav, A., Yadav, V. K. & Kumar, N. GSA\u2013FAPSO-based generators active power rescheduling for transmission congestion management. IEEE Syst. J. 5 (13(3)), 3266\u20133273 (2018).","journal-title":"IEEE Syst. J."},{"issue":"3","key":"14640_CR12","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1504\/IJBIC.2019.099172","volume":"13","author":"SS Reddy","year":"2019","unstructured":"Reddy, S. S. & Wajid, S. A. Swarm intelligent-based congestion management using optimal rescheduling of generators. Int. J. Bio-Inspired Comput. 13 (3), 159\u2013168 (2019).","journal-title":"Int. J. Bio-Inspired Comput."},{"issue":"1","key":"14640_CR13","first-page":"196","volume":"14","author":"SS Aote","year":"2013","unstructured":"Aote, S. S., Raghuwanshi, M. M. & Malik, L. A brief review on particle swarm optimization: Limitations & future directions. Int. J. Comput. Sci. Eng. (IJCSE) 14(1), 196\u2013200 (2013).","journal-title":"Int. J. Comput. Sci. Eng. (IJCSE)"},{"key":"14640_CR14","doi-asserted-by":"publisher","first-page":"115479","DOI":"10.1016\/j.est.2025.115479","volume":"112","author":"Seyyedi","year":"2025","unstructured":"Seyyedi, Ghaleh, A. Z., Gitizadeh, M., Fakhrooeian, M., & Nasero, M. J. Achieving the goals of energy arbitrage, peak-shaving, and PV self-consumption using PV-BTM BESS microgrids coupled with a distribution network. J. Energy Storage. 112, 115479 (2025).","journal-title":"J. Energy Storage"},{"key":"14640_CR15","doi-asserted-by":"publisher","first-page":"114695","DOI":"10.1016\/j.est.2024.114695","volume":"105","author":"E Aghdam","year":"2025","unstructured":"Aghdam, E., Asadzadeh, S., Moslemi, M. S., Nakisaee, M., Fakhrooeian, A. J.,  Al-Hassanawy, K., Masali, M. H. & Seyyedi, A. Z. G. A new IGDT-based robust model for day-ahead scheduling of smart power system integrated with compressed air energy storage and dynamic rating of Transformers and lines. J. Energy Storage. 105, 114695 (2025).","journal-title":"J. Energy Storage"},{"key":"14640_CR16","doi-asserted-by":"publisher","first-page":"114793","DOI":"10.1016\/j.rser.2024.114793","volume":"204","author":"AZ Seyyedi","year":"2024","unstructured":"Seyyedi, A. Z. et al. Application of robust optimized Spatiotemporal load management of data centers for renewable curtailment mitigation. Renew. Sustain. Energy Rev. 204, 114793 (2024).","journal-title":"Renew. Sustain. Energy Rev."},{"key":"14640_CR17","doi-asserted-by":"publisher","first-page":"107800","DOI":"10.1016\/j.ijepes.2021.107800","volume":"137","author":"AZ Seyyedi","year":"2022","unstructured":"Seyyedi, A. Z. et al. Bi-level sitting and sizing of flexi-renewable virtual power plants in the active distribution networks. Int. J. Electr. Power Energy Syst. 137, 107800 (2022).","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"1","key":"14640_CR18","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.asoc.2009.07.008","volume":"10","author":"SN Pandey","year":"2010","unstructured":"Pandey, S. N., Tapaswi, S. & Srivastava, L. Integrated evolutionary neural network approach with distributed computing for congestion management. Appl. Soft Comput. 10(1), 251\u2013260 (2010).","journal-title":"Appl. Soft Comput."},{"key":"14640_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","volume":"87","author":"J Kim","year":"2017","unstructured":"Kim, J., Kim, J., Jang, G. J. & Lee, M. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Netw. 87, 109\u2013121 (2017).","journal-title":"Neural Netw."},{"key":"14640_CR20","doi-asserted-by":"publisher","first-page":"74822","DOI":"10.1109\/ACCESS.2019.2921238","volume":"7","author":"CJ Huang","year":"2019","unstructured":"Huang, C. J. & Kuo, P. H. Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access 7, 74822\u201374834 (2019).","journal-title":"IEEE Access"},{"key":"14640_CR21","doi-asserted-by":"publisher","first-page":"105790","DOI":"10.1016\/j.ijepes.2019.105790","volume":"118","author":"H Zang","year":"2020","unstructured":"Zang, H. et al. Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. Int. J. Electr. Power Energy Syst.\n118, 105790 (2020).","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"14","key":"14640_CR22","doi-asserted-by":"publisher","first-page":"78063","DOI":"10.1109\/ACCESS.2019.2923006","volume":"7","author":"H Zhou","year":"2019","unstructured":"Zhou, H. et al. Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism. IEEE Access 7 (14), 78063\u201378074 (2019).","journal-title":"IEEE Access"},{"issue":"244","key":"14640_CR23","doi-asserted-by":"publisher","first-page":"114451","DOI":"10.1016\/j.enconman.2021.114451","volume":"15","author":"Y Chen","year":"2021","unstructured":"Chen, Y. et al. 2-D regional short-term wind speed forecast based on CNN-LSTM deep learning model. Energy. Conv. Manag. 15 (244), 114451 (2021).","journal-title":"Energy. Conv. Manag."},{"key":"14640_CR24","doi-asserted-by":"publisher","first-page":"28309","DOI":"10.1109\/ACCESS.2019.2901920","volume":"27","author":"Y Ju","year":"2019","unstructured":"Ju, Y. et al. A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access 27, 28309\u201328318 (2019).","journal-title":"IEEE Access"},{"key":"14640_CR25","doi-asserted-by":"publisher","first-page":"117561","DOI":"10.1109\/ACCESS.2022.3218327","volume":"10","author":"L Wang","year":"2022","unstructured":"Wang, L. et al. Critical propagation path identification for cascading overload failures with multi-stage Milp. IEEE Access 10, 117561\u2013117571 (2022).","journal-title":"IEEE Access"},{"issue":"1","key":"14640_CR26","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s42452-020-04084-0","volume":"3","author":"A Gautam","year":"2021","unstructured":"Gautam, A., Sharma, P. & Kumar, Y. Mitigating congestion by optimal rescheduling of generators applying hybrid PSO\u2013GWO in deregulated environment. SN Appl. Sci. 3 (1), 69 (2021).","journal-title":"SN Appl. Sci."},{"key":"14640_CR27","doi-asserted-by":"crossref","unstructured":"Yildirim, B., Ersoz, S., Yildiz, A., Ozan Erdinc, S. T. & Boynuegri, A. R. Evaluating LMP forecasting with LSTM networks: A deep learning approach to analyzing electricity prices during unpredictable events. In 2023 5th Global Power, Energy and Communication Conference (GPECOM) 477\u2013482 (IEEE, 2023).","DOI":"10.1109\/GPECOM58364.2023.10175743"},{"issue":"7","key":"14640_CR28","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1049\/rpg2.12310","volume":"16","author":"J Li","year":"2022","unstructured":"Li, J., Yao, J., Zhang, X. & Tao Yu, and Distributed deep reinforcement learning for integrated generation-control and power\u2010dispatch of interconnected power grid with various renewable units. IET Renew. Power Gener. 16 (7), 1316\u20131335 (2022).","journal-title":"IET Renew. Power Gener."},{"key":"14640_CR29","doi-asserted-by":"publisher","first-page":"64049","DOI":"10.1109\/ACCESS.2023.3284470","volume":"11","author":"A Abdelaziz","year":"2023","unstructured":"Abdelaziz, A. Santos, V. & Dias, M. S. Convolutional neural network with genetic algorithm for predicting energy consumption in public buildings. IEEE Access 11, 64049\u201364069 (2023).","journal-title":"IEEE Access"},{"key":"14640_CR30","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"1","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey Wolf optimizer. Adv. Eng. Softw. 1, 69:46\u201361 (2014).","journal-title":"Adv. Eng. Softw."},{"issue":"6","key":"14640_CR31","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1080\/15325008.2023.2237515","volume":"52","author":"MM Gajjala","year":"2024","unstructured":"Gajjala, M. M. & Ahmad, A. A novel adaptive restarting genetic algorithm for transmission congestion alleviation in the deregulated power market. Electr. Power Compon. Syst. 52 (6), 971\u2013984 (2024).","journal-title":"Electr. Power Compon. Syst."},{"key":"14640_CR32","unstructured":"Balaraman, S. Applications of Evolutionary Algorithms and Neural Network for Congestion Management in Power Systems (Anna University, 2011)."}],"updated-by":[{"DOI":"10.1038\/s41598-025-25358-w","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"record-id":"68773"},{"DOI":"10.1038\/s41598-025-25358-w","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000}}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14640-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14640-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14640-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T13:32:47Z","timestamp":1761831167000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14640-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["14640"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-14640-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"22 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This article has been retracted. Please see the Retraction Notice for more detail:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1038\/s41598-025-25358-w","URL":"https:\/\/doi.org\/10.1038\/s41598-025-25358-w","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"30581"}}