Review

Multi-agent system implementation in demand response: A literature review and bibliometric evaluation

  • Received: 28 July 2023 Revised: 23 October 2023 Accepted: 09 November 2023 Published: 20 November 2023
  • This research provides a comprehensive literature overview and bibliometric evaluation of multi-agent system (MAS) implementation in energy demand response (DR) to identify gaps. The review encompasses 39 relevant papers from searches in three academic databases, focusing on studies published from 2012 to the middle of 2023. The review includes MAS frameworks, optimization algorithms, communication protocols, market structures and evaluation methodologies. Bibliometric analysis of 587 documents from the search on the Scopus database identified prolific authors, influential articles and collaborative networks within the field. The findings reveal growing research interest in implementing an MAS for DR, focusing on integrating intelligent agents into electricity grids to enable effective load management and enhance grid stability. Additionally, the review outlines potential research directions, including exploring advanced MAS techniques, interoperability challenges, policy implications and the integration of renewable energy sources.

    Citation: Benjamin O. Olorunfemi, Nnamdi Nwulu. Multi-agent system implementation in demand response: A literature review and bibliometric evaluation[J]. AIMS Energy, 2023, 11(6): 1179-1210. doi: 10.3934/energy.2023054

    Related Papers:

  • This research provides a comprehensive literature overview and bibliometric evaluation of multi-agent system (MAS) implementation in energy demand response (DR) to identify gaps. The review encompasses 39 relevant papers from searches in three academic databases, focusing on studies published from 2012 to the middle of 2023. The review includes MAS frameworks, optimization algorithms, communication protocols, market structures and evaluation methodologies. Bibliometric analysis of 587 documents from the search on the Scopus database identified prolific authors, influential articles and collaborative networks within the field. The findings reveal growing research interest in implementing an MAS for DR, focusing on integrating intelligent agents into electricity grids to enable effective load management and enhance grid stability. Additionally, the review outlines potential research directions, including exploring advanced MAS techniques, interoperability challenges, policy implications and the integration of renewable energy sources.



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    [1] Sun Z, Liu Y, Yu Y (2019) China's carbon emission peak pre-2030: Exploring multi-scenario optimal low-carbon behaviors for China's regions. J Clean Prod 231: 963–979. https://doi.org/10.1016/j.jclepro.2019.05.159 doi: 10.1016/j.jclepro.2019.05.159
    [2] Kok K, Widergren S, Yang G, et al. (2019) Guest editorial introduction to the special section on transactive approaches to integration of flexible demand and distributed generation. IEEE Trans Power Syst 34: 3991–3993. https://doi.org/10.1109/TPWRS.2019.2932563 doi: 10.1109/TPWRS.2019.2932563
    [3] Phuangpornpitak N, Tia S (2013) Opportunities and challenges of integrating renewable energy in smart grid system. Energy Procedia 34: 282–290. https://doi.org/10.1016/j.egypro.2013.06.756 doi: 10.1016/j.egypro.2013.06.756
    [4] Ferreira P, Rocha A, Araujo M, et al. (2023) Assessing the societal impact of smart grids: Outcomes of a collaborative research project. Technol Soc 72: 102164. https://doi.org/10.1016/j.techsoc.2022.102164 doi: 10.1016/j.techsoc.2022.102164
    [5] Gao Y, Ai Q (2018) Distributed cooperative optimal control architecture for AC microgrid with renewable generation and storage. Int J Electr Power Energy Syst 96: 324–334. https://doi.org/10.1016/j.ijepes.2017.10.007 doi: 10.1016/j.ijepes.2017.10.007
    [6] Mughees N, Jaffery MH, Mughees AA, et al. (2023) Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids. Appl Energy 342: 121150. https://doi.org/10.1016/j.apenergy.2023.121150 doi: 10.1016/j.apenergy.2023.121150
    [7] Nwulu NI, Xia X (2016) Optimal dispatch for a microgrid incorporating renewables and demand response. Renewable Energy 101: 16–28. https://doi.org/10.1016/j.renene.2016.08.026 doi: 10.1016/j.renene.2016.08.026
    [8] Woltmann S, Kittel J (2022) Development and implementation of multi-agent systems for demand response aggregators in an industrial context. Appl Energy 314: 118841. https://doi.org/10.1016/j.apenergy.2022.118841 doi: 10.1016/j.apenergy.2022.118841
    [9] Nguyen DH, Azuma SI, Sugie T (2019) Novel control approaches for demand response with real-time pricing using parallel and distributed consensus-based ADMM. IEEE Trans Ind Electr 66: 7935–7945. https://doi.org/10.1109/TIE.2018.2881938 doi: 10.1109/TIE.2018.2881938
    [10] Olorunfemi TR, Nwulu N (2018) A review of demand response techniques and operational limitations. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Belgaum, India, 442–445. https://doi.org/10.1109/CTEMS.2018.8769181
    [11] Wang Y, Li H, Ju L, et al. (2018) Coordinated energy coupling control strategy and simulation analysis of microgrid cluster for intelligent scheduling. Dianwang Jishu/Power Syst Technol 42: 2232–2239. https://doi.org/10.13335/j.1000-3673.pst.2017.2927 doi: 10.13335/j.1000-3673.pst.2017.2927
    [12] Saeed F, Paul A, Ahmed MJamal, et al. (2021) Intelligent implementation of residential demand response using multi-agent system and deep neural networks. Concurr Comput Pract Exp 33. https://doi.org/10.1002/cpe.6168 doi: 10.1002/cpe.6168
    [13] Olorunfemi TR, Nwulu NI (2021) Multi-agent based optimal operation of hybrid energy sources coupled with demand response programs. Sustainability 13: 7756. https://doi.org/10.3390/su13147756 doi: 10.3390/su13147756
    [14] Golmohamadi H, Keypour R, Bak-Jensen B, et al. (2019) A multi-agent-based optimization of residential and industrial demand response aggregators. Int J Electr Power Energy Syst 107: 472–485. https://doi.org/10.1016/j.ijepes.2018.12.020 doi: 10.1016/j.ijepes.2018.12.020
    [15] Hou F, Mao XJ, Wu Wei (2015) Self-Organizing management approach for cloud services based on multi-agent system. Ruan Jian Xue Bao/J Softw 26: 835–848. https://doi.org/10.13328/j.cnki.jos.004760 doi: 10.13328/j.cnki.jos.004760
    [16] Mishra N, Singh A, Kumari S, et al. (2016) Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing. Int J Prod Res 54: 7115–7128. https://doi.org/10.1080/00207543.2016.1165359 doi: 10.1080/00207543.2016.1165359
    [17] Zulfiqar M, Kamran M, Rasheed MB (2022) A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smart grid. Energy 255: 124450. https://doi.org/10.1016/j.energy.2022.124450 doi: 10.1016/j.energy.2022.124450
    [18] Cha HJ, Won DJ, Kim SH, et al. (2015) Multi-agent system-based microgrid operation strategy for demand response. Energies 8: 14272–14286. https://doi.org/10.3390/en81212430 doi: 10.3390/en81212430
    [19] Zhang W, Xu Y, Liu W, et al. (2015) Distributed online optimal energy management for smart grids. IEEE Trans Ind Inf 11: 717–727. https://doi.org/10.1109/TII.2015.2426419 doi: 10.1109/TII.2015.2426419
    [20] Zeng L, Qiu D, Sun M (2022) Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks. Appl Energy 324. https://doi.org/10.1016/j.apenergy.2022.119688 doi: 10.1016/j.apenergy.2022.119688
    [21] Patsonakis C, Bintoudi AD, Kostopoulos K, et al. (2021) Optimal, dynamic and reliable demand-response via OpenADR-compliant multi-agent virtual nodes: Design, implementation & evaluation. J Clean Prod 314. https://doi.org/10.1016/j.jclepro.2021.127844 doi: 10.1016/j.jclepro.2021.127844
    [22] Aladdin S, El-Tantawy S, Fouda MM, et al. (2020) MARLA-SG: Multi-Agent reinforcement learning algorithm for efficient demand response in smart grid. IEEE Access 8: 210626–210639. https://doi.org/10.1109/ACCESS.2020.3038863 doi: 10.1109/ACCESS.2020.3038863
    [23] Ghazimirsaeid SS, Jonban MS, Mudiyanselage MW, et al. (2023) Multi-agent-based energy management of multiple grid-connected green buildings. J Build Eng 74: 106866. https://doi.org/10.1016/j.jobe.2023.106866 doi: 10.1016/j.jobe.2023.106866
    [24] Dinh HT, Lee K, Kim D (2022) Supervised-learning-based hour-ahead demand response for a behavior-based home energy management system approximating MILP optimization. Appl Energy 321. https://doi.org/10.1016/j.apenergy.2022.119382 doi: 10.1016/j.apenergy.2022.119382
    [25] Linnenluecke MK, Marrone Mauricio, Singh AK (2020) Conducting systematic literature reviews and bibliometric analyses. Aust J Manag 45: 175–194. https://doi.org/10.1177/0312896219877678 doi: 10.1177/0312896219877678
    [26] Wolfswinkel JF, Furtmueller E, Wilderom CPM (2013) Using grounded theory as a method for rigorously reviewing the literature. Eur J Inf Syst 22: 45–55. https://doi.org/10.1057/ejis.2011.51 doi: 10.1057/ejis.2011.51
    [27] Bose S, Kremers E, Mengelkamp EM, et al. (2021) Reinforcement learning in local energy markets. Energy Inf 4. https://doi.org/10.1186/s42162-021-00141-z doi: 10.1186/s42162-021-00141-z
    [28] Onile AE, Belikov J, Levron Y, et al. (2023) Energy efficient behavior modeling for demand side recommender system in solar microgrid applications using multi-agent reinforcement learning model. Sustainable Cities Soc 90: 104392. https://doi.org/10.1016/j.scs.2023.104392 doi: 10.1016/j.scs.2023.104392
    [29] Zhang S, May D, Gul M, et al (2022) Reinforcement learning-driven local transactive energy market for distributed energy resources. Energy AI 8: 100150. https://doi.org/10.1016/j.egyai.2022.100150 doi: 10.1016/j.egyai.2022.100150
    [30] Werth A, Kitamura N, Tanaka K (2015) Conceptual study for open energy systems: Distributed energy network using interconnected DC nanogrids. IEEE Trans Smart Grid 6: 1621–1630. https://doi.org/10.1109/TSG.2015.2408603 doi: 10.1109/TSG.2015.2408603
    [31] Lu RZ, Li YC, Li YT, et al. (2020) Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management. Appl Energy 276: 115473. https://doi.org/10.1016/j.apenergy.2020.115473 doi: 10.1016/j.apenergy.2020.115473
    [32] Elshaafi H, Vinyals M, Grimaldi I, et al. (2018) Secure automated home energy management in multi-agent smart grid architecture. Technol Econ Smart Grids Sustainable Energy 3. https://doi.org/10.1007/s40866-018-0042-0 doi: 10.1007/s40866-018-0042-0
    [33] Wang Z, Zhang C, Li H, et al (2021) A multi-agent-based optimal control method for combined cooling and power systems with thermal energy storage. Build Simul 14: 1709–1723. https://doi.org/10.1007/s12273-021-0768-9 doi: 10.1007/s12273-021-0768-9
    [34] Oh SJ, Yoo CH, Chung IY, et al. (2013) Hardware-in-the-Loop simulation of distributed intelligent energy management system for microgrids. Energies 6: 3263–3283. https://doi.org/10.3390/en6073263 doi: 10.3390/en6073263
    [35] Wu Q, Xie Z, Ren H, et al. (2022) Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: A comparison study. Energy 254: 124448. https://doi.org/10.1016/j.energy.2022.124448 doi: 10.1016/j.energy.2022.124448
    [36] Mollayousefi ZM, MohammadAli RP, Ghafouri S, et al. (2023) IoT-based stochastic EMS using multi-agent system for coordination of grid-connected multi-microgrids. Int J Electr Power Energy Syst 151: 109191. https://doi.org/10.1016/j.ijepes.2023.109191 doi: 10.1016/j.ijepes.2023.109191
    [37] Davarzani S, Granell R, Taylor GA, et al. (2019) Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks. Appl Energy 253: 113516. https://doi.org/10.1016/j.apenergy.2019.113516 doi: 10.1016/j.apenergy.2019.113516
    [38] Khan DA, Arshad A, Lehtonen M, et al. (2022) Combined DR pricing and voltage control using reinforcement learning based multi-agents and load forecasting. IEEE Access 10: 130839–130849. https://doi.org/10.1109/ACCESS.2022.3228836 doi: 10.1109/ACCESS.2022.3228836
    [39] Rwegasira D, Dhaou IB, Ebrahimi M, et al. (2021) Energy trading and control of islanded DC microgrid using multi-agent systems. Multi-agent Grid Syst 17: 113–128. https://doi.org/10.3233/MGS-210345 doi: 10.3233/MGS-210345
    [40] Prinsloo G, Dobson R, Mammoli A (2018) Synthesis of an intelligent rural village microgrid control strategy based on smart-grid multi-agent modeling and transactive energy management principles. Energy 147: 263–278. https://doi.org/10.1016/j.energy.2018.01.056 doi: 10.1016/j.energy.2018.01.056
    [41] Raju L, Morais AA, Rathnakumar R, et al. (2017) Micro-grid grid outage management using multi-agent systems. Energy Procedia 117: 112–119. https://doi.org/10.1016/j.egypro.2017.05.113 doi: 10.1016/j.egypro.2017.05.113
    [42] Marinescu A, Taylor A, Clarke S, et al. (2019) Optimising residential electric vehicle charging under renewable energy: Multi-agent learning in software simulation and hardware-in-the-loop evaluation. Int J Energy Res 43: 3853–3868. https://doi.org/10.1002/er.4559 doi: 10.1002/er.4559
    [43] Vázquez-Canteli JR, Nagy ZZoltan, Vazquez-Canteli JR, et al. (2019) Reinforcement learning for demand response: A review of algorithms and modeling techniques. Appl Energy 235: 1072–1089. https://doi.org/10.1016/j.apenergy.2018.11.002 doi: 10.1016/j.apenergy.2018.11.002
    [44] Mehdi A, Mohammad R, Ali RS (2021) Multi-agent reinforcement learning for energy management in residential buildings. IEEE Trans Ind Inf 17: 659–666. https://doi.org/10.1109/TII.2020.2977104 doi: 10.1109/TII.2020.2977104
    [45] Madler J, Harding S, Weibelzahl M (2023) A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data. Appl Energy 343: 121180. https://doi.org/10.1016/j.apenergy.2023.121180 doi: 10.1016/j.apenergy.2023.121180
    [46] Mareike D, Simon S, Johannes Z, et al. (2020) Simulation of smart factory processes applying multi-agent-systems—A knowledge management perspective. J Manuf Mater Proc 4: 89. https://doi.org/10.3390/JMMP4030089 doi: 10.3390/JMMP4030089
    [47] Jimenez VA, Lizondo DF, Araujo PB, et al. (2022) A conceptual microgrid management framework based on adaptive and autonomous multi-agent systems. J Comput Sci Technol 22: e01. https://doi.org/10.24215/16666038.22.e01 doi: 10.24215/16666038.22.e01
    [48] Antonopoulos I, Robu V, Couraud B, et al. (2020) Artificial intelligence and machine learning approach to energy demand-side response: A systematic review. Renewable Sustainable Energy Rev 130: 109899. https://doi.org/10.1016/j.rser.2020.109899 doi: 10.1016/j.rser.2020.109899
    [49] Langer L, Volling T (2022) A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems. Appl Energy 327: 120020. https://doi.org/10.1016/j.apenergy.2022.120020 doi: 10.1016/j.apenergy.2022.120020
    [50] Hou L, Li Y, Yan J, et al. (2023) Multi-agent reinforcement mechanism design for dynamic pricing-based demand response in charging network. Int J Electr Power Energy Syst 147: 108843. https://doi.org/10.1016/j.ijepes.2022.108843 doi: 10.1016/j.ijepes.2022.108843
    [51] Nie QW, Tang DB, Zhu HH, et al. (2022) A multi-agent and internet of things framework of a digital twin for optimized manufacturing control. Int J Comput Integr Manuf 35: 1205–1226. https://doi.org/10.1080/0951192X.2021.2004619 doi: 10.1080/0951192X.2021.2004619
    [52] Meena NK, Kumar A, Singh AR, et al. (2019) Optimal planning of hybrid energy conversion systems for annual energy cost minimization in Indian residential buildings. Energy Procedia 158: 2979–2985. https://doi.org/10.1016/j.egypro.2019.01.965 doi: 10.1016/j.egypro.2019.01.965
    [53] Fattahi J, Wright D, Schriemer H (2020) An energy internet DERMS platform using a multi-level Stackelberg game. Sustainable Cities Soc 60: 102262. https://doi.org/10.1016/j.scs.2020.102262 doi: 10.1016/j.scs.2020.102262
    [54] Wang XH, Liu P, Ji Z (2021) Trading platform for cooperation and sharing based on blockchain within multi-agent energy internet. Glob Energy Interconnect 4: 384–393. https://doi.org/10.1016/j.gloei.2021.09.009 doi: 10.1016/j.gloei.2021.09.009
    [55] Mezquita Y, Gazafroudi AS, Corchado JM, et al. (2019) Multi-agent architecture for peer-to-peer electricity trading based on blockchain technology. 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, Bosnia and Herzegovina, 1–6. https://doi.org/10.1109/icat47117.2019.8938926
    [56] Xu Y, Zhang W, Liu WX, et al. (2014) Distributed subgradient-based coordination of multiple renewable generators in a microgrid. IEEE Trans Power Syst 29: 23–33. https://doi.org/10.1109/TPWRS.2013.2281038 doi: 10.1109/TPWRS.2013.2281038
    [57] Zhang H, Xiao F, Zhang C, et al. (2023) A multi-agent system based coordinated multi-objective optimal load scheduling strategy using marginal emission factors for building cluster demand response. Energy Build 281: 112765. https://doi.org/10.1016/j.enbuild.2022.112765 doi: 10.1016/j.enbuild.2022.112765
    [58] Singh A, Sethi BK, Kumar A, et al. (2023) Three-level hierarchical management of active distribution system with multimicrogrid. IEEE Syst J 17: 605–616. https://doi.org/10.1109/JSYST.2022.3208032 doi: 10.1109/JSYST.2022.3208032
    [59] Blanco-Zaitegi G, Álvarez EI, Moneva JM (2022) Biodiversity accounting and reporting: A systematic literature review and bibliometric analysis. J Clean Prod 371: 133677. https://doi.org/10.1016/J.JCLEPRO.2022.133677 doi: 10.1016/J.JCLEPRO.2022.133677
    [60] McAllister JT, Lennertz L, Atencio MZ (2021) Mapping a discipline: A guide to using VOSviewer for bibliometric and visual analysis. Sci Technol Libr, 319–348. https://doi.org/10.1080/0194262X.2021.1991547 doi: 10.1080/0194262X.2021.1991547
    [61] Khiste GP, Paithankar RR (2017) Analysis of bibliometric term in Scopus. IJLSIM Int Res J 1: 78–83. ISSN: 2454-910X.
    [62] Lee JW, Kim MK (2022) An evolutionary game theory-based optimal scheduling strategy for multi-agent distribution network operation considering voltage management. IEEE Access 10: 50227–50241. https://doi.org/10.1109/ACCESS.2022.3174077 doi: 10.1109/ACCESS.2022.3174077
    [63] Hurtado LA, Mocanu E, Nguyen PH, et al. (2018) Enabling cooperative behavior for building demand response based on extended joint action learning. IEEE Trans Ind Inf 14: 127–136. https://doi.org/10.1109/TII.2017.2753408 doi: 10.1109/TII.2017.2753408
    [64] Oliveira P, Vale Z, Morais H, et al. (2012) A multi-agent based approach for intelligent smart grid management. IFAC Proc Vol 45: 109–114. https://doi.org/10.3182/20120902-4-FR-2032.00021 doi: 10.3182/20120902-4-FR-2032.00021
    [65] Biabani M, Sajadi A, Golkar MA, et al. (2013) A two ways communication-based distributed control for direct load control in the smart distribution system. Przegl Elektrotech 89: 126–131. Available from: https://www.scopus.com/inward/record.uri?eid = 2-s2.0-84874614153 & partnerID = 40 & md5 = e9b098244708d2caed3a704becc34c3c.
    [66] Gomes L, Faria P, Morais H, et al. (2014) Distributed, agent-based intelligent system for demand response program simulation in smart grids. IEEE Intell Syst 29: 56–65. https://doi.org/10.1109/MIS.2013.2 doi: 10.1109/MIS.2013.2
    [67] Morais H, Sousa TM, Santos G, et al. (2015) Coalition of distributed generation units to Virtual Power Players—A game theory approach. Int Comput Aided Eng 22: 297–309. https://doi.org/10.3233/ICA-150490 doi: 10.3233/ICA-150490
    [68] Wang Z, Paranjape R, Chen Z, et al. (2019) Multi-agent optimization for residential demand response under real-time pricing. Energies 12: 2867. https://doi.org/10.3390/en12152867 doi: 10.3390/en12152867
    [69] Brandi S, Gallo A, Capozzoli A (2022) A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings. Energy Rep 8: 1550–1567. https://doi.org/10.1016/j.egyr.2021.12.058 doi: 10.1016/j.egyr.2021.12.058
    [70] Li H, Hong T (2022) A semantic ontology for representing and quantifying energy flexibility of buildings. Adv Appl Energy 8: 100113. https://doi.org/10.1016/j.adapen.2022.100113 doi: 10.1016/j.adapen.2022.100113
    [71] Naderi M, Khayat Y, Shafiee Q, et al. (2023) Dynamic modeling, stability analysis and control of interconnected microgrids: A review. Appl Energy 334: 120647. https://doi.org/10.1016/j.apenergy.2023.120647 doi: 10.1016/j.apenergy.2023.120647
    [72] Mohanty S, Panda S, Parida S, et al. (2022) Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization. Energy Rep 8: 12466–12490. https://doi.org/10.1016/j.egyr.2022.09.023 doi: 10.1016/j.egyr.2022.09.023
    [73] Vale Z, Faria P, Abrishambaf O, et al. (2021) MARTINE—A platform for real-time energy management in smart grids. Energies 14: 1820. https://doi.org/10.3390/en14071820 doi: 10.3390/en14071820
    [74] Salsabil G (2023) Design and implementation of an intelligent energy management system for smart home utilizing a multi-agent system. Ain Shams Eng J 14: 101897. https://doi.org/10.1016/j.asej.2022.101897 doi: 10.1016/j.asej.2022.101897
    [75] Massana J, Burgas L, Herraiz S, et al. (2022) Multi-vector energy management system including scheduling electrolyzer, electric vehicle charging station and other assets in a real scenario. J Clean Prod 380: 134996. https://doi.org/10.1016/j.jclepro.2022.134996 doi: 10.1016/j.jclepro.2022.134996
    [76] Pan Z, Yu T, Li J, et al. (2022) Multi-agent learning-based nearly non-iterative stochastic dynamic transactive energy control of networked microgrids. IEEE Trans Smart Grid 13: 688–701. https://doi.org/10.1109/TSG.2021.3116598 doi: 10.1109/TSG.2021.3116598
    [77] Nweye K, Liu B, Stone P, et al. (2022) Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings. Energy AI 10: 100202. https://doi.org/10.1016/j.egyai.2022.100202 doi: 10.1016/j.egyai.2022.100202
    [78] Lashmar N, Wade B, Molyneaux L, et al. (2022) Motivations, barriers, and enablers for demand response programs: A commercial and industrial consumer perspective. Energy Res Soc Sci 90: 102667. https://doi.org/10.1016/j.erss.2022.102667 doi: 10.1016/j.erss.2022.102667
    [79] Damisa U, Nwulu NI, Sun Y (2018) A robust energy and reserve dispatch model for prosumer microgrids incorporating demand response aggregators. J Renewable Sustainable Energy 10: 055301. https://doi.org/10.1063/1.5039747 doi: 10.1063/1.5039747
    [80] Tanjimuddin MD, Kannisto P, Jafary P, et al. (2022) A comparative study on multi-agent and service-oriented microgrid automation systems from an energy internet perspective. Sustainable Energy Grids Networks 32: 100856. https://doi.org/10.1016/j.segan.2022.100856 doi: 10.1016/j.segan.2022.100856
    [81] Huang J, Koroteev D, Rynkovskaya M (2023) Machine learning-based demand response in PV-based smart home considering energy management in digital twin. Sol Energy 252: 8–19. https://doi.org/10.1016/j.solener.2023.01.044 doi: 10.1016/j.solener.2023.01.044
    [82] Hussain MS, Ali M (2019) A multi-agent based dynamic scheduling of flexible manufacturing systems. Glob J Flex Syst Manag 20: 267–290. https://doi.org/10.1007/s40171-019-00214-9 doi: 10.1007/s40171-019-00214-9
    [83] Kumar N, Battula S, Doolla S, et al. (2018) Energy management in smart distribution systems with vehicle-to-grid integrated microgrids. IEEE Trans Smart Grid 9: 4004–4016. https://doi.org/10.1109/TSG.2016.2646779 doi: 10.1109/TSG.2016.2646779
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