{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:21:52Z","timestamp":1769707312752,"version":"3.49.0"},"reference-count":27,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>Congestion of the power system is the most common challenge an Independent System Operator (ISO) faces in restructured electricity markets. It affects the efficiency of the market when transmission lines are congested causing transmission costs to rise. To prevent transmission line congestion, ISO needs to take the necessary steps. To solve these issues, this paper introduces a new method namely the Adaptive Red Fox Optimization algorithm (ARFOA) to compute the congestion cost considering the power losses in the transmission line system. Initially, all the generators in the system are selected to reschedule real power outputs. Second, by establishing a proposed optimization issue, ARFOA is employed to control transmission line congestion. The implementation of the proposed method is evaluated on the IEEE 30 bus system. The algorithm\u2019s adaptability is tested using several case studies involving the base case and line outages, also compared with the other existing techniques such as PSO, ASO, and GSO approaches. The simulation outcomes indicate that the proposed strategy outperforms existing techniques in terms of congestion cost, power loss, generation rescheduled power, and computational time.<\/jats:p>","DOI":"10.3233\/jifs-224559","type":"journal-article","created":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T15:07:12Z","timestamp":1691939232000},"page":"8465-8477","source":"Crossref","is-referenced-by-count":0,"title":["Congestion cost estimation using adaptive red fox algorithm in restructured electricity markets"],"prefix":"10.1177","volume":"45","author":[{"given":"S.","family":"Chellam","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Velammal College of Engineering and Technology (Autonomous), Madurai, Tamil Nadu"}]},{"given":"S.","family":"Kuruseelan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu"}]},{"given":"T.","family":"Pravin Rose","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Vidya Academy of Science and Technology Technical Campus, Kilimanoor"}]},{"given":"A.","family":"Jasmine Gnana Malar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology, Tirunelveli"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224559_ref1","doi-asserted-by":"crossref","first-page":"162231","DOI":"10.1109\/ACCESS.2021.3133360","article-title":"Novel Neural Network-Based Load Frequency Control Scheme: A Case Study of Restructured Power System","volume":"9","author":"Kumar","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-224559_ref3","doi-asserted-by":"crossref","first-page":"118306","DOI":"10.1016\/j.apenergy.2021.118306","article-title":"A locational marginal price for frequency balancing operations in regulation 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