{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T14:48:37Z","timestamp":1776350917185,"version":"3.51.2"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2022A1515010699"],"award-info":[{"award-number":["2022A1515010699"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangzhou Education Bureau University Research Project - Graduate Research Project","award":["2024312278"],"award-info":[{"award-number":["2024312278"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s40747-025-01916-7","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T08:52:09Z","timestamp":1749113529000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Advancing sustainable electricity markets: evolutionary game theory as a framework for complex systems optimization and adaptive policy design"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7007-7535","authenticated-orcid":false,"given":"Lefeng","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Runbao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Pengrong","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Mengya","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"issue":"5","key":"1916_CR1","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1016\/j.energy.2011.03.050","volume":"36","author":"J Wang","year":"2011","unstructured":"Wang J, Zhou Z, Botterud A (2011) An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand. Energy 36(5):3459\u20133467","journal-title":"Energy"},{"key":"1916_CR2","doi-asserted-by":"crossref","unstructured":"Miorandi D, De Pellegrini F (2012) Demand-side management in smart grids: An evolutionary games perspective. In: Proceedings of the 6th international ICST conference on performance evaluation methodologies and tools. IEEE, pp 178\u2013187","DOI":"10.4108\/valuetools.2012.250351"},{"key":"1916_CR3","doi-asserted-by":"crossref","first-page":"106743","DOI":"10.1016\/j.ijepes.2020.106743","volume":"129","author":"L Cheng","year":"2021","unstructured":"Cheng L, Yin L, Wang J, Shen T, Chen Y, Liu G, Yu T (2021) Behavioral decision-making in power demand-side response management: a multi-population evolutionary game dynamics perspective. Int J Electr Power Energy Syst 129:106743","journal-title":"Int J Electr Power Energy Syst"},{"issue":"1","key":"1916_CR4","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TCC.2021.3117956","volume":"10","author":"O Han","year":"2021","unstructured":"Han O, Ding T, Bai L, He Y, Li F, Shahidehpour M (2021) Evolutionary game based demand response bidding strategy for end-users using Q-learning and compound differential evolution. IEEE Trans Cloud Comput 10(1):97\u2013110","journal-title":"IEEE Trans Cloud Comput"},{"key":"1916_CR5","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.energy.2018.07.209","volume":"162","author":"XG Zhao","year":"2018","unstructured":"Zhao XG, Ren LZ, Zhang YZ, Wan G (2018) Evolutionary game analysis on the behavior strategies of power producers in renewable portfolio standard. Energy 162:505\u2013516","journal-title":"Energy"},{"issue":"6","key":"1916_CR6","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.3390\/en16062876","volume":"16","author":"X Huang","year":"2023","unstructured":"Huang X, Li Z (2023) A comparative analysis of two pricing mechanisms, MCP and PAB, in the Chinese frequency regulation market. Energies 16(6):2876","journal-title":"Energies"},{"key":"1916_CR7","doi-asserted-by":"publisher","DOI":"10.1063\/5.0192019","author":"M Hou","year":"2024","unstructured":"Hou M, Luo Z, Qing S, Zhang X (2024) Evolutionary game and simulation analysis of three parties of electricity market regulation under government participation mechanism. J Renew Sustain Energy. https:\/\/doi.org\/10.1063\/5.0192019","journal-title":"J Renew Sustain Energy"},{"key":"1916_CR8","doi-asserted-by":"crossref","first-page":"28970","DOI":"10.1109\/ACCESS.2020.2972387","volume":"8","author":"L Yin","year":"2020","unstructured":"Yin L, Li S, Gao F (2020) Equilibrium stability of asymmetric evolutionary games of multi-agent systems with multiple groups in open electricity market. IEEE Access 8:28970\u201328978","journal-title":"IEEE Access"},{"issue":"2","key":"1916_CR9","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3390\/electronics13020248","volume":"13","author":"M Gautam","year":"2024","unstructured":"Gautam M (2024) Evolutionary game dynamics between distributed energy resources and microgrid operator: balancing act for power factor improvement. Electronics 13(2):248","journal-title":"Electronics"},{"key":"1916_CR10","doi-asserted-by":"publisher","first-page":"186926","DOI":"10.1109\/ACCESS.2024.3436935","volume":"12","author":"A Karaki","year":"2024","unstructured":"Karaki A, Al-Fagih L (2024) Evolutionary game theory as a catalyst in smart grids: From theoretical insights to practical strategies. IEEE Access (Early Access) 12:186926\u2013186940. https:\/\/doi.org\/10.1109\/ACCESS.2024.3436935","journal-title":"IEEE Access (Early Access)"},{"key":"1916_CR11","doi-asserted-by":"crossref","DOI":"10.2307\/j.ctvjghw98","volume-title":"Evolutionary dynamics: exploring the equations of life","author":"MA Nowak","year":"2006","unstructured":"Nowak MA (2006) Evolutionary dynamics: exploring the equations of life. Harvard University Press, Cambridge"},{"key":"1916_CR12","doi-asserted-by":"crossref","unstructured":"Zhang Q, Li J (2012) Demand response in electricity markets: a review. In: Proceedings of the 2012 9th international conference on the European Energy Market. IEEE, pp 1\u20138","DOI":"10.1109\/EEM.2012.6254817"},{"issue":"9","key":"1916_CR13","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.3390\/pr12091995","volume":"12","author":"J Sun","year":"2024","unstructured":"Sun J, Wu F, Shi M, Yuan X (2024) Coordination of renewable energy integration and peak shaving through evolutionary game theory. Processes 12(9):1995","journal-title":"Processes"},{"issue":"2","key":"1916_CR14","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11036-018-1124-x","volume":"26","author":"PA Apostolopoulos","year":"2021","unstructured":"Apostolopoulos PA, Tsiropoulou EE, Papavassiliou S (2021) Demand response management in smart grid networks: a two-stage game-theoretic learning-based approach. Mob Netw Appl 26(2):548\u2013561","journal-title":"Mob Netw Appl"},{"key":"1916_CR15","volume-title":"Game theory in wireless and communication networks: theory, models, and applications","author":"Z Han","year":"2012","unstructured":"Han Z (2012) Game theory in wireless and communication networks: theory, models, and applications. Cambridge University Press, Cambridge"},{"key":"1916_CR16","volume-title":"The microeconomics of complex economies: evolutionary, institutional, neoclassical, and complexity perspectives","author":"W Elsner","year":"2014","unstructured":"Elsner W, Heinrich T, Schwardt H (2014) The microeconomics of complex economies: evolutionary, institutional, neoclassical, and complexity perspectives. Academic Press, Cambridge"},{"issue":"4","key":"1916_CR17","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.plrev.2009.08.001","volume":"6","author":"CP Roca","year":"2009","unstructured":"Roca CP, Cuesta JA, S\u00e1nchez A (2009) Evolutionary game theory: temporal and spatial effects beyond replicator dynamics. Phys Life Rev 6(4):208\u2013249","journal-title":"Phys Life Rev"},{"issue":"23","key":"1916_CR18","doi-asserted-by":"crossref","first-page":"6293","DOI":"10.3390\/en13236293","volume":"13","author":"T Zhang","year":"2020","unstructured":"Zhang T, Cao S, Pan L, Zhou C (2020) A policy effect analysis of China\u2019s energy storage development based on a multi-agent evolutionary game model. Energies 13(23):6293","journal-title":"Energies"},{"key":"1916_CR19","doi-asserted-by":"crossref","first-page":"50227","DOI":"10.1109\/ACCESS.2022.3174077","volume":"10","author":"JW Lee","year":"2022","unstructured":"Lee JW, Kim MK (2022) An evolutionary game theory-based optimal scheduling strategy for multiagent distribution network operation considering voltage management. IEEE Access 10:50227\u201350241","journal-title":"IEEE Access"},{"issue":"1","key":"1916_CR20","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MIS.2019.2950903","volume":"35","author":"T Pinto","year":"2019","unstructured":"Pinto T (2019) Adaptive learning in electricity market negotiations based on determinism theory. IEEE Intell Syst 35(1):62\u201373","journal-title":"IEEE Intell Syst"},{"key":"1916_CR21","doi-asserted-by":"crossref","first-page":"106965","DOI":"10.1016\/j.eneco.2023.106965","volume":"126","author":"C Zhao","year":"2023","unstructured":"Zhao C, Guo Q, Jia R, Dong K, Wang K (2023) How does clean energy transition promote original design manufacturers? A three-party evolutionary game analysis. Energy Econ 126:106965","journal-title":"Energy Econ"},{"key":"1916_CR22","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s11831-018-9299-7","volume":"27","author":"S Abapour","year":"2020","unstructured":"Abapour S, Nazari-Heris M, Mohammadi-Ivatloo B, Tarafdar Hagh M (2020) Game theory approaches for the solution of power system problems: a comprehensive review. Arch Comput Methods Eng 27:81\u2013103","journal-title":"Arch Comput Methods Eng"},{"issue":"5","key":"1916_CR23","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1108\/IJCCSM-11-2019-0066","volume":"12","author":"N Shen","year":"2020","unstructured":"Shen N, Zhao Y, Deng R (2020) A review of carbon trading based on an evolutionary perspective. Int J Clim Chang Strateg Manag 12(5):739\u2013756","journal-title":"Int J Clim Chang Strateg Manag"},{"issue":"17","key":"1916_CR24","doi-asserted-by":"crossref","first-page":"4464","DOI":"10.3390\/en17174464","volume":"17","author":"Y Yang","year":"2024","unstructured":"Yang Y, Pan L (2024) An evolutionary game model of market participants and government in carbon trading markets with virtual power plant strategies. Energies 17(17):4464","journal-title":"Energies"},{"key":"1916_CR25","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.egyr.2021.11.231","volume":"8","author":"G Wang","year":"2022","unstructured":"Wang G, Chao Y, Cao Y, Jiang T, Han W, Chen Z (2022) A comprehensive review of research works based on evolutionary game theory for sustainable energy development. Energy Rep 8:114\u2013136","journal-title":"Energy Rep"},{"key":"1916_CR26","doi-asserted-by":"crossref","first-page":"100024","DOI":"10.1016\/j.adapen.2021.100024","volume":"2","author":"Z Liu","year":"2021","unstructured":"Liu Z, Wang S, Lim MQ, Kraft M, Wang X (2021) Game theory-based renewable multi-energy system design and subsidy strategy optimization. Adv Appl Energy 2:100024","journal-title":"Adv Appl Energy"},{"key":"1916_CR27","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1016\/j.renene.2020.06.066","volume":"159","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Gu C, Yan X, Li F (2020) Cournot oligopoly game-based local energy trading considering renewable energy uncertainty costs. Renew Energy 159:1117\u20131127","journal-title":"Renew Energy"},{"key":"1916_CR28","doi-asserted-by":"crossref","first-page":"129806","DOI":"10.1016\/j.energy.2023.129806","volume":"288","author":"H Yan","year":"2024","unstructured":"Yan H, Hou H, Deng M, Si L, Wang X, Hu E, Zhou R (2024) Stackelberg game theory based model to guide users\u2019 energy use behavior, with the consideration of flexible resources and consumer psychology, for an integrated energy system. Energy 288:129806","journal-title":"Energy"},{"issue":"8","key":"1916_CR29","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1109\/TAC.2018.2797217","volume":"63","author":"SR Etesami","year":"2018","unstructured":"Etesami SR, Saad W, Mandayam NB, Poor HV (2018) Stochastic games for the smart grid energy management with prospect prosumers. IEEE Trans Autom Control 63(8):2327\u20132342","journal-title":"IEEE Trans Autom Control"},{"key":"1916_CR30","first-page":"2327","volume":"63","author":"Q Li","year":"2023","unstructured":"Li Q, Pi B, Feng M, Kurths J (2023) Open data in the digital economy: an evolutionary game theory perspective. IEEE Trans. Comput Soc Syst 63:2327\u20132342","journal-title":"IEEE Trans. Comput Soc Syst"},{"key":"1916_CR31","doi-asserted-by":"crossref","first-page":"128398","DOI":"10.1016\/j.energy.2023.128398","volume":"284","author":"M Teng","year":"2023","unstructured":"Teng M, Lv K, Han C, Liu P (2023) Trading behavior strategy of power plants and the grid under renewable portfolio standards in China: a tripartite evolutionary game analysis. Energy 284:128398","journal-title":"Energy"},{"key":"1916_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mathsocsci.2018.08.002","volume":"96","author":"W Leininger","year":"2018","unstructured":"Leininger W, Moghadam HM (2018) Asymmetric oligopoly and evolutionary stability. Math Soc Sci 96:1\u20139","journal-title":"Math Soc Sci"},{"issue":"1","key":"1916_CR33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0022-0531(92)90099-4","volume":"58","author":"L Blume","year":"1992","unstructured":"Blume L, Easley D (1992) Evolution and market behavior. J Econ Theory 58(1):9\u201340","journal-title":"J Econ Theory"},{"key":"1916_CR34","volume-title":"Complex social and behavioral systems: game theory and agent-based models","author":"D P\u00e9rez-Castrillo","year":"2020","unstructured":"P\u00e9rez-Castrillo D, Sotomayor M, Castiglione F (2020) Complex social and behavioral systems: game theory and agent-based models. Springer, New York"},{"key":"1916_CR35","doi-asserted-by":"crossref","DOI":"10.1201\/9780429051739","volume-title":"Economics and the theory of games","author":"M Bacharach","year":"2019","unstructured":"Bacharach M (2019) Economics and the theory of games. CRC Press, Boca Raton"},{"issue":"1\/2","key":"1916_CR36","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3938\/PhiT.24.006","volume":"24","author":"S-W Son","year":"2015","unstructured":"Son S-W (2015) Evolutionary games on complex networks. Phys High Technol 24(1\/2):27","journal-title":"Phys High Technol"},{"key":"1916_CR37","unstructured":"Samadi Kouhi M (2022) Data-driven demand management for smart grid. Diss. Universit\u00e9 d'Ottawa\/University of Ottawa Ottawa, Canada"},{"key":"1916_CR38","first-page":"102273","volume":"52","author":"N Rezaei","year":"2022","unstructured":"Rezaei N, Meyabadi AF, Deihimi M (2022) A game theory based demand-side management in a smart microgrid considering price-responsive loads via a twofold sustainable energy justice portfolio. Sustain Energy Technol Assess 52:102273","journal-title":"Sustain Energy Technol Assess"},{"key":"1916_CR39","volume-title":"Narrow roads of gene land: volume 1: evolution of social behaviour","author":"WD Hamilton","year":"1996","unstructured":"Hamilton WD (1996) Narrow roads of gene land: volume 1: evolution of social behaviour. Oxford University Press, Oxford"},{"key":"1916_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbenv.2023.12.002","author":"Q Hassan","year":"2024","unstructured":"Hassan Q, Algburi S, Sameen AZ, Tariq J, Al-Jiboory AK, Salman HM, Ali BM, Jaszczur M (2024) A comprehensive review of international renewable energy growth. Energy Built Environ. https:\/\/doi.org\/10.1016\/j.enbenv.2023.12.002","journal-title":"Energy Built Environ"},{"key":"1916_CR41","doi-asserted-by":"crossref","unstructured":"Tan Q, Yu S (2021) Dynamics of renewable energy in China: drivers and challenges. In: Renewable energy transition in Asia: policies, markets and emerging issues. Springer Nature, Cham, pp 113\u2013135","DOI":"10.1007\/978-981-15-8905-8_6"},{"key":"1916_CR42","first-page":"39","volume":"8","author":"R Newell","year":"2021","unstructured":"Newell R, Raimi D, Villanueva S, Prest B (2021) Global energy outlook 2021: pathways from Paris. Resour Future 8:39","journal-title":"Resour Future"},{"key":"1916_CR43","doi-asserted-by":"crossref","first-page":"110307","DOI":"10.1016\/j.rser.2020.110307","volume":"134","author":"S Kolb","year":"2020","unstructured":"Kolb S, Dillig M, Plankenb\u00fchler T, Karl J (2020) The impact of renewables on electricity prices in Germany\u2014an update for the years 2014\u20132018. Renew Sustain Energy Rev 134:110307","journal-title":"Renew Sustain Energy Rev"},{"key":"1916_CR44","first-page":"46","volume":"178","author":"A Koliesnichenko","year":"2019","unstructured":"Koliesnichenko A, Yuryeva I (2019) Restructuring of the electricity market in view of transformation and pricing challenges. Econ Ann XXI 178:46\u201356","journal-title":"Econ Ann XXI"},{"issue":"1","key":"1916_CR45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1057\/s41599-022-01360-9","volume":"9","author":"D Ding","year":"2022","unstructured":"Ding D (2022) The impacts of carbon pricing on the electricity market in Japan. Humanit Soc Sci Commun 9(1):1\u20138","journal-title":"Humanit Soc Sci Commun"},{"key":"1916_CR46","doi-asserted-by":"crossref","first-page":"105247","DOI":"10.1016\/j.eneco.2021.105247","volume":"98","author":"K Du","year":"2021","unstructured":"Du K, Cheng Y, Yao X (2021) Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Econ 98:105247","journal-title":"Energy Econ"},{"key":"1916_CR47","doi-asserted-by":"crossref","unstructured":"Wiser RH, Bolinger M (2019) 2018 wind technologies market report (No. DOE\/GO-102019-5191). Lawrence Berkeley National Lab. (LBNL), Berkeley","DOI":"10.2172\/1559241"},{"key":"1916_CR48","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-15-2624-4","volume-title":"Smart meter data analytics: electricity consumer behavior modeling, aggregation, and forecasting","author":"Y Wang","year":"2020","unstructured":"Wang Y, Chen Q, Kang C (2020) Smart meter data analytics: electricity consumer behavior modeling, aggregation, and forecasting. Springer Nature, Cham"},{"issue":"20","key":"1916_CR49","doi-asserted-by":"publisher","first-page":"3241","DOI":"10.3390\/math12203241","volume":"12","author":"L Cheng","year":"2024","unstructured":"Cheng L, Wei X, Li M, Tan C, Yin M, Shen T, Zou T (2024) Integrating evolutionary game-theoretical methods and deep reinforcement learning for adaptive strategy optimization in user-side electricity markets: a comprehensive review. Mathematics 12(20):3241. https:\/\/doi.org\/10.3390\/math12203241","journal-title":"Mathematics"},{"key":"1916_CR50","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1007\/s42835-024-01797-9","volume":"19","author":"GS Shin","year":"2024","unstructured":"Shin GS, Kim HY, Mahseredjian J (2024) Smart vehicle-to-grid operation of power system based on EV user behavior. J Electr Eng Technol 19:2941\u20132952. https:\/\/doi.org\/10.1007\/s42835-024-01797-9","journal-title":"J Electr Eng Technol"},{"key":"1916_CR51","doi-asserted-by":"publisher","unstructured":"Zhong C, Shao J, Zheng F (2018) Research on electricity consumption behavior of electric power users based on tag technology and clustering algorithm. In: Proceedings of the 2018 5th international conference on Information Science and Control Engineering (ICISCE), Zhengzhou, China, 20\u201322 July 2018, pp 459\u2013462. https:\/\/doi.org\/10.1109\/ICISCE.2018.00102","DOI":"10.1109\/ICISCE.2018.00102"},{"key":"1916_CR52","doi-asserted-by":"crossref","first-page":"121237","DOI":"10.1016\/j.energy.2021.121237","volume":"234","author":"H Ji","year":"2021","unstructured":"Ji H, Wang H, Yang J, Feng J, Yang Y, Okoye MO (2021) Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks. Energy 234:121237","journal-title":"Energy"},{"key":"1916_CR53","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1016\/j.ejor.2021.03.032","volume":"295","author":"G Xie","year":"2021","unstructured":"Xie G, Chen X, Weng Y (2021) Enhance load forecastability: optimize data sampling policy by reinforcing user behaviors. Eur J Oper Res 295:924\u2013934. https:\/\/doi.org\/10.1016\/j.ejor.2021.03.032","journal-title":"Eur J Oper Res"},{"key":"1916_CR54","doi-asserted-by":"crossref","unstructured":"Hu H, Wang Y, Han J, Zhang Y, Yan Q (2021) Analysis of user power consumption characteristics and behavior portrait based on KS-rf algorithm. In: Proceedings of the 2021 IEEE\/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, pp 1586\u20131590","DOI":"10.1109\/ICPSAsia52756.2021.9621677"},{"key":"1916_CR55","doi-asserted-by":"publisher","first-page":"110898","DOI":"10.1016\/j.epsr.2024.110898","volume":"237","author":"H Yu","year":"2024","unstructured":"Yu H, He J, Wang Y (2024) Spike load smoothing strategy considering behavioral decision under vehicle-road-network convergence. Electr Power Syst Res 237:110898. https:\/\/doi.org\/10.1016\/j.epsr.2024.110898","journal-title":"Electr Power Syst Res"},{"key":"1916_CR56","doi-asserted-by":"publisher","first-page":"121770","DOI":"10.1016\/j.apenergy.2023.121770","volume":"350","author":"B Tan","year":"2023","unstructured":"Tan B, Lin Z, Zheng X (2023) Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors. Appl Energy 350:121770. https:\/\/doi.org\/10.1016\/j.apenergy.2023.121770","journal-title":"Appl Energy"},{"key":"1916_CR57","doi-asserted-by":"crossref","DOI":"10.1017\/9781108566506","volume-title":"Smart grid technology: a cloud computing and data management approach","author":"S Misra","year":"2018","unstructured":"Misra S, Bera S (2018) Smart grid technology: a cloud computing and data management approach. Cambridge University Press, Cambridge"},{"key":"1916_CR58","doi-asserted-by":"crossref","first-page":"101047","DOI":"10.1016\/j.jup.2020.101047","volume":"64","author":"J Lin","year":"2020","unstructured":"Lin J, Liu X, He G (2020) Regional electricity demand and economic transition in China. Util Policy 64:101047","journal-title":"Util Policy"},{"key":"1916_CR59","doi-asserted-by":"crossref","unstructured":"Shao YT, Ma KSK (2022) Renewable energy policies and the design of electricity market for energy transition: benefits, challenges, and strategies. In: Proceedings of the 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST). IEEE, pp 446\u2013452","DOI":"10.1109\/GECOST55694.2022.10010446"},{"key":"1916_CR60","doi-asserted-by":"crossref","first-page":"114306","DOI":"10.1016\/j.enpol.2024.114306","volume":"194","author":"X Cheng","year":"2024","unstructured":"Cheng X, Liu P, Zhu L (2024) The impact of electricity market reform on renewable energy production. Energy Policy 194:114306","journal-title":"Energy Policy"},{"key":"1916_CR61","doi-asserted-by":"crossref","first-page":"120371","DOI":"10.1016\/j.energy.2021.120371","volume":"227","author":"IJ Scott","year":"2021","unstructured":"Scott IJ, Carvalho PM, Botterud A, Silva CA (2021) Long-term uncertainties in generation expansion planning: implications for electricity market modelling and policy. Energy 227:120371","journal-title":"Energy"},{"key":"1916_CR62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2016.11.049","volume":"51","author":"F Zaman","year":"2017","unstructured":"Zaman F, Elsayed SM, Ray T, Sarker RA (2017) Co-evolutionary approach for strategic bidding in competitive electricity markets. Appl Soft Comput 51:1\u201322","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1916_CR63","doi-asserted-by":"publisher","first-page":"819","DOI":"10.32604\/csse.2023.028525","volume":"45","author":"M Mythreyee","year":"2023","unstructured":"Mythreyee M, Nalini A (2023) Genetic algorithm based smart grid system for distributed renewable energy sources. Comput Syst Sci Eng 45(1):819\u2013837. https:\/\/doi.org\/10.32604\/csse.2023.028525","journal-title":"Comput Syst Sci Eng"},{"key":"1916_CR64","doi-asserted-by":"crossref","DOI":"10.1049\/PBPO098E","volume-title":"Large scale grid integration of renewable energy sources","author":"A Moreno-Munoz","year":"2017","unstructured":"Moreno-Munoz A (2017) Large scale grid integration of renewable energy sources. IET, London"},{"key":"1916_CR65","doi-asserted-by":"crossref","first-page":"25727","DOI":"10.1109\/ACCESS.2019.2900356","volume":"7","author":"L Cheng","year":"2019","unstructured":"Cheng L, Yu T (2019) Game-theoretic approaches applied to transactions in the open and ever-growing electricity markets from the perspective of power demand response: an overview. IEEE Access 7:25727\u201325762","journal-title":"IEEE Access"},{"key":"1916_CR66","doi-asserted-by":"crossref","first-page":"101033","DOI":"10.1016\/j.jup.2020.101033","volume":"64","author":"X Lambin","year":"2020","unstructured":"Lambin X (2020) Integration of demand response in electricity market capacity mechanisms. Util Policy 64:101033","journal-title":"Util Policy"},{"key":"1916_CR67","doi-asserted-by":"crossref","first-page":"49206","DOI":"10.1109\/ACCESS.2021.3068730","volume":"9","author":"HT Doan","year":"2021","unstructured":"Doan HT, Cho J, Kim D (2021) Peer-to-peer energy trading in smart grid through blockchain: a double auction-based game theoretic approach. IEEE Access 9:49206\u201349218","journal-title":"IEEE Access"},{"issue":"5","key":"1916_CR68","doi-asserted-by":"crossref","first-page":"5163","DOI":"10.1109\/TSG.2018.2878445","volume":"10","author":"J Guerrero","year":"2018","unstructured":"Guerrero J, Chapman AC, Verbi\u010d G (2018) Decentralized P2P energy trading under network constraints in a low-voltage network. IEEE Trans Smart Grid 10(5):5163\u20135173","journal-title":"IEEE Trans Smart Grid"},{"key":"1916_CR69","doi-asserted-by":"publisher","first-page":"124123","DOI":"10.1016\/j.jclepro.2020.124123","volume":"276","author":"L Cheng","year":"2020","unstructured":"Cheng L, Liu G, Huang H, Wang X, Chen Y, Zhang J, Meng A, Yang R, Yu T (2020) Equilibrium analysis of general n-population multi-strategy games for generation-side long-term bidding: an evolutionary game perspective. J Clean Prod 276:124123. https:\/\/doi.org\/10.1016\/j.jclepro.2020.124123","journal-title":"J Clean Prod"},{"issue":"16","key":"1916_CR70","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.3390\/math12162537","volume":"12","author":"L Cheng","year":"2024","unstructured":"Cheng L, Peng P, Lu W, Huang P, Chen Y (2024) Study of flexibility transformation in thermal power enterprises under multi-factor drivers: application of complex-network evolutionary game theory. Mathematics 12(16):2537. https:\/\/doi.org\/10.3390\/math12162537","journal-title":"Mathematics"},{"issue":"19","key":"1916_CR71","doi-asserted-by":"publisher","first-page":"3109","DOI":"10.3390\/math12193109","volume":"12","author":"Z Zhu","year":"2024","unstructured":"Zhu Z, Cheng L, Shen T (2024) Spontaneous formation of evolutionary game strategies for long-term carbon emission reduction based on low-carbon trading mechanism. Mathematics 12(19):3109. https:\/\/doi.org\/10.3390\/math12193109","journal-title":"Mathematics"},{"issue":"Part A","key":"1916_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2022.108182","volume":"142","author":"LF Cheng","year":"2022","unstructured":"Cheng LF, Chen Y, Liu GY (2022) 2PnS-EG: a general two-population n-strategy evolutionary game for strategic long-term bidding in a deregulated market under different market clearing mechanisms. Int J Electr Power Energy Syst 142(Part A):108182. https:\/\/doi.org\/10.1016\/j.ijepes.2022.108182","journal-title":"Int J Electr Power Energy Syst"},{"key":"1916_CR73","doi-asserted-by":"publisher","first-page":"105926","DOI":"10.1016\/j.ocecoaman.2021.105926","volume":"215","author":"J Chen","year":"2021","unstructured":"Chen J, Xiong W, Xu L, Di Z (2021) Evolutionary game analysis on supply side of the implement shore-to-ship electricity. Ocean Coast Manag 215:105926. https:\/\/doi.org\/10.1016\/j.ocecoaman.2021.105926","journal-title":"Ocean Coast Manag"},{"key":"1916_CR74","doi-asserted-by":"publisher","first-page":"5177","DOI":"10.1109\/ACCESS.2020.2965191","volume":"9","author":"L Cheng","year":"2020","unstructured":"Cheng L, Zhang J, Yin L, Chen Y, Wang J, Liu G, Wang X, Zhang D (2020) General three-population multi-strategy evolutionary games for long-term on-grid bidding of generation-side electricity market. IEEE Access 9:5177\u20135198. https:\/\/doi.org\/10.1109\/ACCESS.2020.2965191","journal-title":"IEEE Access"},{"issue":"2","key":"1916_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41601-024-00320-2","volume":"9","author":"Z Ji","year":"2024","unstructured":"Ji Z, Liu X, Tang D (2024) Game-theoretic applications for decision-making behavior on the energy demand side: a systematic review. Prot Control Mod Power Syst 9(2):1\u201320. https:\/\/doi.org\/10.1186\/s41601-024-00320-2","journal-title":"Prot Control Mod Power Syst"},{"key":"1916_CR76","doi-asserted-by":"publisher","first-page":"119083","DOI":"10.1016\/j.apenergy.2022.119083","volume":"318","author":"MB Jamali","year":"2022","unstructured":"Jamali MB, Rasti-Barzoki M, Khosroshahi H, Altmann J (2022) An evolutionary game-theoretic approach to study the technological transformation of the industrial sector toward renewable electricity procurement: a case study of Iran. Appl Energy 318:119083. https:\/\/doi.org\/10.1016\/j.apenergy.2022.119083","journal-title":"Appl Energy"},{"key":"1916_CR77","doi-asserted-by":"crossref","first-page":"107995","DOI":"10.1016\/j.ijepes.2022.107995","volume":"140","author":"MK Mishra","year":"2022","unstructured":"Mishra MK, Murari K, Parida SK (2022) Demand-side management and its impact on utility and consumers through a game theoretic approach. Int J Electr Power Energy Syst 140:107995","journal-title":"Int J Electr Power Energy Syst"},{"key":"1916_CR78","doi-asserted-by":"crossref","unstructured":"Pedasingu BS, Subramanian E, Bichpuriya Y, Sarangan V, Mahilong N (2020) Bidding strategy for two-sided electricity markets: a reinforcement learning based framework. In: Proceedings of the 7th ACM international conference on systems for energy-efficient buildings, cities, and transportation, pp 110\u2013119","DOI":"10.1145\/3408308.3427976"},{"issue":"20","key":"1916_CR79","doi-asserted-by":"crossref","first-page":"3249","DOI":"10.3390\/math12203249","volume":"12","author":"X Shen","year":"2024","unstructured":"Shen X, Tang J, Zhang Y, Qian B, Li J, Zhou M, Zhao Y, Yin Y (2024) Dynamic evolution game strategy of government, power grid, and users in electricity market demand-side management. Mathematics 12(20):3249","journal-title":"Mathematics"},{"key":"1916_CR80","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rser.2013.04.026","volume":"25","author":"Z Ming","year":"2013","unstructured":"Ming Z, Ximei L, Na L, Song X (2013) Overall review of renewable energy tariff policy in China: evolution, implementation, problems and countermeasures. Renew Sustain Energy Rev 25:260\u2013271","journal-title":"Renew Sustain Energy Rev"},{"key":"1916_CR81","doi-asserted-by":"crossref","first-page":"107838","DOI":"10.1016\/j.eneco.2024.107838","volume":"138","author":"CC Lee","year":"2024","unstructured":"Lee CC, Wang T (2024) The impact of renewable energy policies on the energy transition\u2014an empirical analysis of Chinese cities. Energy Econ 138:107838","journal-title":"Energy Econ"},{"issue":"8","key":"1916_CR82","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1080\/14693062.2018.1467827","volume":"18","author":"E Narassimhan","year":"2018","unstructured":"Narassimhan E, Gallagher KS, Koester S, Alejo JR (2018) Carbon pricing in practice: a review of existing emissions trading systems. Clim Policy 18(8):967\u2013991","journal-title":"Clim Policy"},{"key":"1916_CR83","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1016\/j.rser.2017.03.105","volume":"76","author":"J Schallenberg-Rodriguez","year":"2017","unstructured":"Schallenberg-Rodriguez J (2017) Renewable electricity support systems: are feed-in systems taking the lead? Renew Sustain Energy Rev 76:1422\u20131439","journal-title":"Renew Sustain Energy Rev"},{"issue":"4","key":"1916_CR84","first-page":"26","volume":"5","author":"G Resch","year":"2007","unstructured":"Resch G, Ragwitz M, Held A, Faber T, Haas R (2007) Feed-in tariffs and quotas for renewable energy in Europe. CESifo DICE Rep 5(4):26\u201332","journal-title":"CESifo DICE Rep"},{"key":"1916_CR85","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.rser.2016.10.075","volume":"70","author":"G Shrimali","year":"2017","unstructured":"Shrimali G, Srinivasan S, Goel S, Nelson D (2017) The effectiveness of federal renewable policies in India. Renew Sustain Energy Rev 70:538\u2013550","journal-title":"Renew Sustain Energy Rev"},{"key":"1916_CR86","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.rser.2016.08.025","volume":"66","author":"S Thapar","year":"2016","unstructured":"Thapar S, Sharma S, Verma A (2016) Economic and environmental effectiveness of renewable energy policy instruments: best practices from India. Renew Sustain Energy Rev 66:487\u2013498","journal-title":"Renew Sustain Energy Rev"},{"key":"1916_CR87","doi-asserted-by":"crossref","unstructured":"Xue G, Wu Y, Zhu X, Tan J (2023) Optimal bidding strategy of generation companies participated in power and energy-consuming right trading markets. In: Proceedings of the 2023 5th Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, pp 1588\u20131593","DOI":"10.1109\/AEEES56888.2023.10114178"},{"key":"1916_CR88","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1109\/TPWRS.2024.3404472","volume":"40","author":"B Yin","year":"2024","unstructured":"Yin B, Weng H, Hu Y, Xi J, Ding P, Liu J (2024) Multi-agent deep reinforcement learning for simulating centralized double-sided auction electricity market. IEEE Trans Power Syst (Early Access) 40:518\u2013529. https:\/\/doi.org\/10.1109\/TPWRS.2024.3404472","journal-title":"IEEE Trans Power Syst (Early Access)"},{"key":"1916_CR89","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1109\/ACCESS.2015.2507372","volume":"3","author":"A Naeem","year":"2015","unstructured":"Naeem A, Shabbir A, Hassan NU, Yuen C, Ahmad A, Tushar W (2015) Understanding customer behavior in multi-tier demand response management program. IEEE Access 3:2613\u20132625","journal-title":"IEEE Access"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01916-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01916-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01916-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:09:01Z","timestamp":1750331341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01916-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,5]]},"references-count":89,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["1916"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01916-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,5]]},"assertion":[{"value":"5 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Institutional review board statement"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"320"}}