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Volume 10 Issue 7
Jul.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(7): 1513-1529
A. Joshi, S. Capezza, A. Alhaji, and  M.-Y. Chow,  “Survey on AI and machine learning techniques for microgrid energy management systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1513–1529, Jul. 2023. doi: 10.1109/JAS.2023.123657
Citation: A. Joshi, S. Capezza, A. Alhaji, and  M.-Y. Chow,  “Survey on AI and machine learning techniques for microgrid energy management systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1513–1529, Jul. 2023. doi: 10.1109/JAS.2023.123657
shu

Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

doi: 10.1109/JAS.2023.123657
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    Abstract

  • In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS’s potential in real-world applications.

     

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  • [1]
    Z. Y. Cheng, J. Duan, and M.-Y. Chow, “To centralize or to distribute: That is the question: A comparison of advanced microgrid management systems,” IEEE Ind. Electron. Mag., vol. 12, no. 1, pp. 6–24, Mar. 2018. doi: 10.1109/MIE.2018.2789926
    [2]
    E. Mohammadi, M. Alizadeh, M. Asgarimoghaddam, X. Y. Wang, and M. G. Simões, “A review on application of artificial intelligence techniques in microgrids,” IEEE J. Emerg. Sel. Topic Ind. Electron., vol. 3, no. 4, pp. 878–890, Oct. 2022. doi: 10.1109/JESTIE.2022.3198504
    [3]
    D. X. Zhang, X. Q. Han, and C. Y. Deng, “Review on the research and practice of deep learning and reinforcement learning in smart grids,” CSEE J. Power Energy Syst., vol. 4, no. 3, pp. 362–370, Sept. 2018. doi: 10.17775/CSEEJPES.2018.00520
    [4]
    L. Yu, S. Q. Qin, M. Zhang, C. Shen, T. Jiang, and X. H. Guan, “A review of deep reinforcement learning for smart building energy management,” IEEE Internet Things J., vol. 8, no. 15, pp. 12046–12063, Aug. 2021. doi: 10.1109/JIOT.2021.3078462
    [5]
    D. G. Rosero, N. L. Díaz, and C. L. Trujillo, “Cloud and machine learning experiments applied to the energy management in a microgrid cluster,” Appl. Energy, vol. 304, p. 117770, Dec. 2021. doi: 10.1016/j.apenergy.2021.117770
    [6]
    R. Trivedi and S. Khadem, “Implementation of artificial intelligence techniques in microgrid control environment: Current progress and future scopes,” Energy AI, vol. 8, p. 100147, 2022. 2022. doi: 10.1016/j.egyai.2022.100147
    [7]
    D. T. Ton and M. A. Smith, “The U.S. department of energy’s microgrid initiative,” Electr. J., vol. 25, no. 8, pp. 84–94, Oct. 2012. doi: 10.1016/j.tej.2012.09.013
    [8]
    R. H. Lasseter, “MicroGrids,” in Proc. IEEE Power Engineering Society Winter Meeting. Conf., New York, USA, 2002, pp. 305–308.
    [9]
    IEEE Vision for Smart Grid Controls: 2030 and Beyond, 2013.
    [10]
    IEEE Guide for Distributed Energy Resources Management Systems (DERMS) Functional Specification, IEEE Std 2030.11–2021, 2021.
    [11]
    R. L. Deng, Z. Y. Yang, M.-Y. Chow, and J. M. Chen, “A survey on demand response in smart grids: Mathematical models and approaches,” IEEE Trans. Industr. Inform., vol. 11, no. 3, pp. 570–582, Jun. 2015. doi: 10.1109/TII.2015.2414719
    [12]
    N. Rahbari-Asr, U. Ojha, Z. A. Zhang, and M.-Y. Chow, “Incremental welfare consensus algorithm for cooperative distributed generation/demand response in smart grid,” IEEE Trans. Smart Grid, vol. 5, no. 6, pp. 2836–2845, Nov. 2014. doi: 10.1109/TSG.2014.2346511
    [13]
    Y. N. Wan, J. H. Qin, X. H. Yu, T. Yang, and Y. Kang, “Price-based residential demand response management in smart grids: A reinforcement learning-based approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 123–134, Jan. 2022. doi: 10.1109/JAS.2021.1004287
    [14]
    W. Saad, Z. Han, H. V. Poor, and T. Basar, “Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications,” IEEE Signal Process. Mag., vol. 29, no. 5, pp. 86–105, Sept. 2012. doi: 10.1109/MSP.2012.2186410
    [15]
    Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798–1828, Aug. 2013. doi: 10.1109/TPAMI.2013.50
    [16]
    M. R. Samsami and H. Alimadad, “Distributed deep reinforcement learning: An overview,” arXiv preprint arXiv: 2011.11012, 2020.
    [17]
    N. Rahbari-Asr, Y. Zhang, and M.-Y. Chow, “Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids,” IET Gener. Trans. Dis., vol. 10, no. 5, pp. 1268–1277, Apr. 2016. doi: 10.1049/iet-gtd.2015.0159
    [18]
    L. An, J. Duan, M.-Y. Chow, and A. Duel-Hallen, “A distributed and resilient bargaining game for weather-predictive microgrid energy cooperation,” IEEE Trans. Industr. Inform., vol. 15, no. 8, pp. 4721–4730, Aug. 2019. doi: 10.1109/TII.2019.2907380
    [19]
    O. Bouachir, M. Aloqaily, Ö. Özkasap, and F. Ali, “FederatedGrids: Federated learning and blockchain-assisted P2P energy sharing,” IEEE Trans. Green Commun. Netw., vol. 6, no. 1, pp. 424–436, Mar. 2022. doi: 10.1109/TGCN.2022.3140978
    [20]
    M. E. G. Urias, E. N. Sanchez, and L. J. Ricalde, “Electrical microgrid optimization via a new recurrent neural network,” IEEE Syst. J., vol. 9, no. 3, pp. 945–953, Sept. 2015. doi: 10.1109/JSYST.2014.2305494
    [21]
    M. AlOwaifeer and A. P. S. Meliopoulos, “Centralized microgrid energy management system based on successive linearization,” in Proc. North American Power Symp., Fargo, USA, 2018, pp. 1–6.
    [22]
    Y. Sabri, N. El Kamoun, and F. Lakrami, “A survey: Centralized, decentralized, and distributed control scheme in smart grid systems,” in Proc. 7th Mediterranean Congr. Telecommunications, Fez, Morocco, 2019, pp. 1–11.
    [23]
    N. Rahbari-Asr, Y. Zhang, and M.-Y. Chow, “Cooperative distributed scheduling for storage devices in microgrids using dynamic KKT multipliers and consensus networks,” in Proc. IEEE Power & Energy Society General Meeting, Denver, USA, 2015, pp. 1–5.
    [24]
    J. Faraji, A. Ketabi, H. Hashemi-Dezaki, M. Shafie-Khah, and J. P. S. Catalão, “Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting,” IEEE Access, vol. 8, pp. 157284–157305, Aug. 2020. doi: 10.1109/ACCESS.2020.3019562
    [25]
    Y. Zhang, N. Rahbari-Asr, J. Duan, and M.-Y. Chow, “Day-ahead smart grid cooperative distributed energy scheduling with renewable and storage integration,” IEEE Trans. Sustain. Energy, vol. 7, no. 4, pp. 1739–1748, Oct. 2016. doi: 10.1109/TSTE.2016.2581167
    [26]
    H. Shuai, J. K. Fang, X. M. Ai, Y. F. Tang, J. Y. Wen, and H. B. He, “Stochastic optimization of economic dispatch for microgrid based on approximate dynamic programming,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 2440–2452, May 2019. doi: 10.1109/TSG.2018.2798039
    [27]
    D. E. Olivares, C. A. Cañizares, and M. Kazerani, “A centralized energy management system for isolated microgrids,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1864–1875, Jul. 2014. doi: 10.1109/TSG.2013.2294187
    [28]
    H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, “Stochastic energy management of microgrids during unscheduled islanding period,” IEEE Trans. Industr. Inform., vol. 13, no. 3, pp. 1079–1087, Jun. 2017. doi: 10.1109/TII.2016.2646721
    [29]
    L. I. Minchala-Avila, L. Garza-Castañon, Y. M. Zhang, and H. J. A. Ferrer, “Optimal energy management for stable operation of an islanded microgrid,” IEEE Trans. Industr. Inform., vol. 12, no. 4, pp. 1361–1370, Aug. 2016. doi: 10.1109/TII.2016.2569525
    [30]
    D. Arcos-Aviles, J. Pascual, F. Guinjoan, L. Marroyo, P. Sanchis, and M. P. Marietta, “Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting,” Appl. Energy, vol. 205, pp. 69–84, Nov. 2017. doi: 10.1016/j.apenergy.2017.07.123
    [31]
    B. Zhao, X. S. Zhang, J. Chen, C. S. Wang, and L. Guo, “Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system,” IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 934–943, Oct. 2013. doi: 10.1109/TSTE.2013.2248400
    [32]
    J. Li, F. Liu, Z. J. Wang, S. H. Low, and S. W. Mei, “Optimal power flow in stand-alone DC microgrids,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5496–5506, Sept. 2018. doi: 10.1109/TPWRS.2018.2801280
    [33]
    N. Liu, X. H. Yu, C. Wang, and J. J. Wang, “Energy sharing management for microgrids with PV prosumers: A Stackelberg game approach,” IEEE Trans. Industr. Inform., vol. 13, no. 3, pp. 1088–1098, Jun. 2017. doi: 10.1109/TII.2017.2654302
    [34]
    T. C. Wang, X. He, and T. Deng, “Neural networks for power management optimal strategy in hybrid microgrid,” Neural Comput. Appl., vol. 31, no. 7, pp. 2635–2647, Jul. 2019. doi: 10.1007/s00521-017-3219-x
    [35]
    M. Dabbaghjamanesh, A. Kavousi-Fard, and J. Zhang, “Stochastic modeling and integration of plug-in hybrid electric vehicles in reconfigurable microgrids with deep learning-based forecasting,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4394–4403, Jul. 2021. doi: 10.1109/TITS.2020.2973532
    [36]
    G. K. Venayagamoorthy, R. K. Sharma, P. K. Gautam, and A. Ahmadi, “Dynamic energy management system for a smart microgrid,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 8, pp. 1643–1656, Aug. 2016. doi: 10.1109/TNNLS.2016.2514358
    [37]
    V. Suresh, P. Janik, J. M. Guerrero, Z. Leonowicz, and T. Sikorski, “Microgrid energy management system with embedded deep learning forecaster and combined optimizer,” IEEE Access, vol. 8, pp. 202225–202239, Nov. 2020. doi: 10.1109/ACCESS.2020.3036131
    [38]
    Y. Ji, J. H. Wang, J. C. Xu, X. K. Fang, and H. G. Zhang, “Real-time energy management of a microgrid using deep reinforcement learning,” Energies, vol. 12, no. 12, p. 2291, Jun. 2019. doi: 10.3390/en12122291
    [39]
    Y. Ji, J. H. Wang, J. C. Xu, and D. L. Li, “Data-driven online energy scheduling of a microgrid based on deep reinforcement learning,” Energies, vol. 14, no. 8, p. 2120, Apr. 2021. doi: 10.3390/en14082120
    [40]
    E. Kuznetsova, Y.-F. Li, C. Ruiz, E. Zio, G. Ault, and K. Bell, “Reinforcement learning for microgrid energy management,” Energy, vol. 59, pp. 133–146, Sept. 2013. doi: 10.1016/j.energy.2013.05.060
    [41]
    F. Fele, J. M. Maestre, and E. F. Camacho, “Coalitional control: Cooperative game theory and control,” IEEE Control Syst., vol. 37, no. 1, pp. 53–69, Feb. 2017. doi: 10.1109/MCS.2016.2621465
    [42]
    A. Bidram, F. L. Lewis, and A. Davoudi, “Distributed control systems for small-scale power networks: Using multiagent cooperative control theory,” IEEE Control Syst. Mag., vol. 34, no. 6, pp. 56–77, Dec. 2014. doi: 10.1109/MCS.2014.2350571
    [43]
    S. Patel, K. Murari, and S. Kamalasadan, “A spectral clustering based distributed control of distributed energy resources (DERs) integrated power distribution system,” in Proc. IEEE Int. Conf. Power Electronics, Drives and Energy Systems, Jaipur, India, 2020, pp. 1–6.
    [44]
    W. Ananduta and C. Ocampo-Martinez, “Decentralized energy management of power networks with distributed generation using periodical self-sufficient repartitioning approach,” in Proc. American Control Conf., Philadelphia, USA, 2019, pp. 3230–3235.
    [45]
    S. Patel, K. Murari, and S. Kamalasadan, “Distributed control of distributed energy resources in active power distribution system for local power balance with optimal spectral clustering,” IEEE Trans. Ind. Appl., vol. 58, no. 4, pp. 5395–5408, Jul.–Aug. 2022. doi: 10.1109/TIA.2022.3172391
    [46]
    H. X. Li, H. S. Sun, J. Y. Wen, S. J. Cheng, and H. B. He, “A fully decentralized multi-agent system for intelligent restoration of power distribution network incorporating distributed generations [application notes],” IEEE Comput. Intell. Mag., vol. 7, no. 4, pp. 66–76, Nov. 2012. doi: 10.1109/MCI.2012.2215152
    [47]
    Y. P. Shen, Y. M. Xu, H. W. Yao, J. X. Shi, F. W. Li, Z. Wen, C. B. Zhang, and W. Liu, “Distributed cluster control for multi-microgrids using pinning-based group consensus of multi-agent system,” in Proc. 5th IEEE Int. Conf. Cloud Computing and Intelligence Systems, Nanjing, China, 2018, pp. 1077–1080.
    [48]
    R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proc. IEEE, vol. 95, no. 1, pp. 215–233, Jan. 2007. doi: 10.1109/JPROC.2006.887293
    [49]
    N. Rahbari Asr, Z. A. Zhang, and M.-Y. Chow, “Consensus-based distributed energy management with real-time pricing,” in Proc. IEEE Power & Energy Society General Meeting, Vancouver, Canada, 2013, pp. 1–5.
    [50]
    Z. A. Zhang, N. Rahbari-Asr, and M.-Y. Chow, “Asynchronous distributed cooperative energy management through gossip-based incremental cost consensus algorithm,” in Proc. North American Power Symp., Manhattan, USA, 2013, pp. 1–6.
    [51]
    Z. Y. Cheng and M.-Y. Chow, “Resilient collaborative distributed energy management system framework for cyber-physical DC microgrids,” IEEE Trans. Smart Grid, vol. 11, no. 6, pp. 4637–4649, Nov. 2020. doi: 10.1109/TSG.2020.3001059
    [52]
    Z. A. Zhang and M.-Y. Chow, “Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid,” IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1761–1768, Nov. 2012. doi: 10.1109/TPWRS.2012.2188912
    [53]
    M. Yazdanian and A. Mehrizi-Sani, “Distributed control techniques in microgrids,” IEEE Trans. Smart Grid, vol. 5, no. 6, pp. 2901–2909, Nov. 2014. doi: 10.1109/TSG.2014.2337838
    [54]
    J. R. Wang, Y. T. Hong, J. L. Wang, J. P. Xu, Y. Tang, Q.-L. Han, and J. Kurths, “Cooperative and competitive multi-agent systems: From optimization to games,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 763–783, May 2022. doi: 10.1109/JAS.2022.105506
    [55]
    F. Ye, Z. Y. Cheng, X. H. Cao, and M.-Y. Chow, “A random-weight privacy-preserving algorithm with error compensation for microgrid distributed energy management,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 4352–4362, Aug. 2021. doi: 10.1109/TIFS.2021.3106161
    [56]
    J.-S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, May–Jun. 1993. doi: 10.1109/21.256541
    [57]
    S. Leonori, A. Rizzi, M. Paschero, and F. M. F. Mascioli, “Microgrid energy management by ANFIS supported by an ESN based prediction algorithm,” in Proc. Int. Joint Conf. Neural Networks, Rio de Janeiro, Brazil, 2018, pp. 1–8.
    [58]
    C.-H. Yoo, I.-Y. Chung, H.-J. Lee, and S.-S. Hong, “Intelligent control of battery energy storage for multi-agent based microgrid energy management,” Energies, vol. 6, no. 10, pp. 4956–4979, Sept. 2013. doi: 10.3390/en6104956
    [59]
    H. Z. Liu, X. Zhang, X. W. Shen, and H. B. Sun, “A federated learning framework for smart grids: Securing power traces in collaborative learning,” arXiv preprint arXiv: 2103.11870, 2021.
    [60]
    A. Rosato, M. Panella, R. Araneo, and A. Andreotti, “A neural network based prediction system of distributed generation for the management of microgrids,” IEEE Trans. Ind. Appl., vol. 55, no. 6, pp. 7092–7102, Nov.-Dec. 2019. doi: 10.1109/TIA.2019.2916758
    [61]
    A. T. Eseye, M. Lehtonen, T. Tukia, S. Uimonen, and R. J. Millar, “Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems,” IEEE Access, vol. 7, pp. 91463–91475, Jun. 2019. doi: 10.1109/ACCESS.2019.2924685
    [62]
    X. H. Fang, Q. Zhao, J. K. Wang, Y. H. Han, and Y. C. Li, “Multi-agent deep reinforcement learning for distributed energy management and strategy optimization of microgrid market,” Sustain. Cities Soc., vol. 74, p. 103163, Nov. 2021. doi: 10.1016/j.scs.2021.103163
    [63]
    E. Samadi, A. Badri, and R. Ebrahimpour, “Decentralized multi-agent based energy management of microgrid using reinforcement learning,” Int. J. Electr. Power Energy Syst., vol. 122, p. 106211, Nov. 2020. doi: 10.1016/j.ijepes.2020.106211
    [64]
    E. Foruzan, L.-K. Soh, and S. Asgarpoor, “Reinforcement learning approach for optimal distributed energy management in a microgrid,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5749–5758, Sept. 2018. doi: 10.1109/TPWRS.2018.2823641
    [65]
    A. L. Dimeas and N. D. Hatziargyriou, “Multi-agent reinforcement learning for microgrids,” in Proc. IEEE PES General Meeting, Minneapolis, USA, 2010, pp. 1–8.
    [66]
    Y. Xu, L. Yu, G. Bi, M. Zhang, and C. Shen, “Deep reinforcement learning and blockchain for peer-to-peer energy trading among microgrids,” in Proc. Int. Conf. Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congr. Cybermatics (Cybermatics), Rhodes, Greece, 2020, pp. 360–365.
    [67]
    T. J. Zhang, D. Yue, L. Yu, C. X. Dou, and X. P. Xie, “Joint energy and workload scheduling for fog-assisted multimicrogrid systems: A deep reinforcement learning approach,” IEEE Syst. J., vol. 17, no. 1, pp. 164–175, Mar. 2023. doi: 10.1109/JSYST.2022.3171534
    [68]
    D. J. B. Harrold, J. Cao, and Z. Fan, “Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning,” Appl. Energy, vol. 318, p. 119151, Jul. 2022. doi: 10.1016/j.apenergy.2022.119151
    [69]
    K. Zhang, J. Zhang, P.-D. Xu, T. L. Gao, and D. W. Gao, “Explainable AI in deep reinforcement learning models for power system emergency control,” IEEE Trans. Comput. Soc. Syst., vol. 9, no. 2, pp. 419–427, Apr. 2022. doi: 10.1109/TCSS.2021.3096824
    [70]
    W. Duo, M. C. Zhou, and A. Abusorrah, “A survey of cyber attacks on cyber physical systems: Recent advances and challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 784–800, May 2022. doi: 10.1109/JAS.2022.105548
    [71]
    A. P. S. Meliopoulos, G. Cokkinides, R. K. Huang, E. Farantatos, S. Choi, Y. Lee, and X. B. Yu, “Smart grid technologies for autonomous operation and control,” IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 1–10, Mar. 2011. doi: 10.1109/TSG.2010.2091656
    [72]
    J. Zhang, L. Pan, Q.-L. Han, C. Chen, S. Wen, and Y. Xiang, “Deep learning based attack detection for cyber-physical system cybersecurity: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 377–391, Mar. 2022. doi: 10.1109/JAS.2021.1004261
    [73]
    M. Alipour, B. Mohammadi-Ivatloo, and K. Zare, “Stochastic scheduling of renewable and CHP-based microgrids,” IEEE Trans. Industr. Inform., vol. 11, no. 5, pp. 1049–1058, Oct. 2015. doi: 10.1109/TII.2015.2462296
    [74]
    L. Yu, T. Jiang, and Y. Cao, “Energy cost minimization for distributed internet data centers in smart microgrids considering power outages,” IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 1, pp. 120–130, Jan. 2015. doi: 10.1109/TPDS.2014.2308223
    [75]
    D. Sáez, F. Ávila, D. Olivares, C. Cañizares, and L. Marín, “Fuzzy prediction interval models for forecasting renewable resources and loads in microgrids,” IEEE Trans. Smart Grid, vol. 6, no. 2, pp. 548–556, Mar. 2015. doi: 10.1109/TSG.2014.2377178
    [76]
    Y. Li, Z. Yang, G. Q. Li, D. B. Zhao, and W. Tian, “Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties,” IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1565–1575, Feb. 2019. doi: 10.1109/TIE.2018.2840498
    [77]
    P. M. Costa, M. A. Matos, and J. A. P. Lopes, “A regulatory framework for microgeneration and microgrids,” in Proc. IEEE Lausanne Power Tech, Lausanne, Switzerland, 2007, pp. 633–638.
    [78]
    L. Tao, C. Schwaegerl, S. Narayanan, and J. H. Zhang, “From laboratory microgrid to real markets — challenges and opportunities,” in Proc. 8th Int. Conf. Power Electronics, Jeju, Korea (South), 2011, pp. 264–271.
    [79]
    A. Hooshyar and R. Iravani, “Microgrid protection,” Proc. IEEE, vol. 105, no. 7, pp. 1332–1353, Jul. 2017. doi: 10.1109/JPROC.2017.2669342
    [80]
    E. Casagrande, W. L. Woon, H. H. Zeineldin, and D. Svetinovic, “A differential sequence component protection scheme for microgrids with inverter-based distributed generators,” IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 29–37, Jan. 2014. doi: 10.1109/TSG.2013.2251017
    [81]
    J. Mohammadi, F. B. Ajaei, and G. Stevens, “Grounding the AC microgrid,” IEEE Trans. Ind. Appl., vol. 55, no. 1, pp. 98–105, Jan.–Feb. 2019. doi: 10.1109/TIA.2018.2864106
    [82]
    D. W. Varley, D. L. Van Bossuyt, and A. Pollman, “Feasibility analysis of a mobile microgrid design to support DoD energy resilience goals,” Systems, vol. 10, no. 3, p. 74, Jun. 2022. doi: 10.3390/systems10030074
    [83]
    J. Kim and Y. Dvorkin, “Enhancing distribution system resilience with mobile energy storage and microgrids,” IEEE Trans. Smart Grid, vol. 10, no. 5, pp. 4996–5006, Sept. 2019. doi: 10.1109/TSG.2018.2872521
    [84]
    M.-H. Khooban, T. Dragicevic, F. Blaabjerg, and M. Delimar, “Shipboard microgrids: A novel approach to load frequency control,” IEEE Trans. Sustain. Energy, vol. 9, no. 2, pp. 843–852, Apr. 2018. doi: 10.1109/TSTE.2017.2763605
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    Highlights

    • The future of scalable microgrids relies on the proper use of machine learning
    • AI in EMS has shown improved efficiency, accuracy, and robustness of MG systems
    • AI in EMS keeps the design generalized by adapting variations and human factors
    • This survey gives an up-to-date review of research directions in ML and AI in EMS

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