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The objective of ETPD is optimizing operating cost for specified power demand meet and to satisfy the generation capacity limits of each unit. In the presented work, We cast the ETPD as a multi agent FRL (MAFRL) problem wherein individual thermal generators act as players for minimizing operational cost and also satisfying the generation limits of each units to obtain a specified power demand. To prove supremacy and validity of proposed multi agent fuzzy reinforcement learning technique, two benchmark test systems involving 10 and 40 units integrated using numerous fuel systems with valve point loading effect have been simulated. Simulation results and comparison against several other existing solution approaches showcases the efficacy of MAFRL technique in solving the ETPD problem.<\/jats:p>","DOI":"10.3233\/jifs-169776","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T17:08:02Z","timestamp":1532452082000},"page":"4921-4931","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":24,"title":["Solving nonconvex economic thermal power dispatch problem with multiple fuel system and valve point loading effect using fuzzy reinforcement learning"],"prefix":"10.1177","volume":"35","author":[{"given":"Nandan Kumar","family":"Navin","sequence":"first","affiliation":[{"name":"Division of Instrumentation and control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India"}]},{"given":"Rajneesh","family":"Sharma","sequence":"additional","affiliation":[{"name":"Division of Instrumentation and control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India"}]},{"given":"H.","family":"Malik","sequence":"additional","affiliation":[{"name":"Division of Instrumentation and control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India"}]}],"member":"179","published-online":{"date-parts":[[2018,7,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1972.8557"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.141757"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.485985"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.667345"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.331423"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2007.06.023"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.12.084"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2006.12.001"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2004.12.006"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2009.09.016"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/15325000903273379"},{"key":"e_1_3_1_13_2","first-page":"324","article-title":"Solving multi-objective optimal power flow using differential evolution","volume":"1","author":"Varadarajan M.","year":"2007","unstructured":"VaradarajanM., SwarupK.S.Solving multi-objective optimal power flow using differential evolution, Gener Transm Distrib IET1 (2007), 324doi: 10.1049\/iet-gtd.","journal-title":"Gener Transm Distrib IET"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2010.2043270"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2008.918131"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2010.01.016"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2012.07.009"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2008.11.012"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2010.0405"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2009.09.034"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2013.11.016"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2016.04.034"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2012.0142"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.09.010"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2005.857924"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.05.002"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2016.07.138"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2012.08.049"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.07.037"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"WieringM. van OtterloM.Reinforcement learning: State-of-the-art Springer2012.","DOI":"10.1007\/978-3-642-27645-3"},{"key":"e_1_3_1_31_2","unstructured":"BuoniuL. 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