Safe resource management of non-cooperative microgrids based on deep reinforcement learning

Engineering Applications of Artificial Intelligence - Tập 126 - Trang 106865 - 2023
Mahdi Shademan1, Hamid Karimi1, Shahram Jadid1
1Department of Electrical Engineering, Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran

Tài liệu tham khảo

Bahrami, 2021, Deep reinforcement learning for demand response in distribution networks, IEEE Trans. Smart Grid, 12, 1496, 10.1109/TSG.2020.3037066 Bazmohammadi, 2019, A hierarchical energy management strategy for interconnected microgrids considering uncertainty, Int. J. Electr. Power. Energy Syst., 109, 597, 10.1016/j.ijepes.2019.02.033 Chen, 2021, Networked microgrids for grid resilience, robustness, and efficiency: A review, IEEE Trans. Smart Grid, 12, 18, 10.1109/TSG.2020.3010570 Du, 2020, A hierarchical real-time balancing market considering multi-microgrids with distributed sustainable resources, IEEE Trans. Sustain. Energy, 11, 72, 10.1109/TSTE.2018.2884223 Du, 2020, Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning, IEEE Trans. Smart Grid, 11, 1066, 10.1109/TSG.2019.2930299 Du, 2018, A cooperative game approach for coordinating multi-microgrid operation within distribution systems, Appl. Energy, 222, 383, 10.1016/j.apenergy.2018.03.086 Dutta, 2017, Optimality conditions for bilevel programming: An approach through variational analysis, 43, 10.1007/978-981-10-4774-9_3 Fang, 2021, Multi-agent deep reinforcement learning for distributed energy management and strategy optimization of microgrid market, Sustainable Cities Soc., 74, 10.1016/j.scs.2021.103163 Garifi, 2018 Hirsch, 2018, Microgrids: A review of technologies, key drivers, and outstanding issues, Renew. Sustain. Energy Rev., 90, 402, 10.1016/j.rser.2018.03.040 Jafari, 2020, A fair electricity market strategy for energy management and reliability enhancement of islanded multi-microgrids, Appl. Energy, 270, 10.1016/j.apenergy.2020.115170 Karimi, 2021, Dynamic transactive energy in multi-microgrid systems considering independence performance index: A multi-objective optimization framework, Int. J. Electr. Power Energy Syst., 126, 10.1016/j.ijepes.2020.106563 Karimi, 2021, Modeling of transactive energy in multi-microgrid systems by hybrid of competitive-cooperative games, Electr. Power Syst. Res., 201, 10.1016/j.epsr.2021.107546 Khan, 2022, Introducing urdu digits dataset with demonstration of an efficient and robust noisy decoder-based pseudo example generator, Symmetry, 14, 1976, 10.3390/sym14101976 Li, 2017, Networked microgrids for enhancing the power system resilience, Proc. IEEE, 105, 1289, 10.1109/JPROC.2017.2685558 Liu, 2019, A secure distributed transactive energy management scheme for multiple interconnected microgrids considering misbehaviors, IEEE Trans. Smart Grid, 10, 5975, 10.1109/TSG.2019.2895229 Liu, 2018, A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids, Appl. Energy, 228, 130, 10.1016/j.apenergy.2018.04.087 Liu, 2021, Multi-agent based optimal scheduling and trading for multi-microgrids integrated with urban transportation networks, IEEE Trans. Power. Syst., 36, 2197, 10.1109/TPWRS.2020.3040310 Lu, 2018, A systematic review of supply and demand side optimal load scheduling in a smart grid environment, J. Clean. Prod., 203, 757, 10.1016/j.jclepro.2018.08.301 Mansour-Saatloo, A., et al., Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies. Appl. Energy 304, 2021. http://dx.doi.org/10.1016/j.apenergy.2021.117635, (in English), Art No. 117635. Mo, 2021, A stochastic spatiotemporal decomposition decision-making approach for real-time dynamic energy management of multi-microgrids, IEEE Trans. Sustain. Energy, 12, 821, 10.1109/TSTE.2020.3021226 Mohiti, 2019, A decentralized robust model for optimal operation of distribution companies with private microgrids, Int. J. Electr. Power. Energy Syst., 106, 105, 10.1016/j.ijepes.2018.09.031 Namar, 2022, The start of combustion prediction for methane-fueled HCCI engines: Traditional vs. Machine learning methods Qin, 2021, Privacy preserving load control of residential microgrid via deep reinforcement learning, IEEE Trans. Smart Grid, 12, 4079, 10.1109/TSG.2021.3088290 Qiu, 2020, Decentralized-distributed robust electric power scheduling for multi-microgrid systems, Appl. Energy, 269, 10.1016/j.apenergy.2020.115146 Qiu, 2018, Bi-level two-stage robust optimal scheduling for AC/DC hybrid multi-microgrids, IEEE Trans. Smart Grid, 9, 5455, 10.1109/TSG.2018.2806973 Qu, Z.L., Chen, J.J., Peng, K., Zhao, Y.L., Rong, Z.K., Zhang, M.Y., Enhancing stochastic multi-microgrid operational flexibility with mobile energy storage system and power transaction. Sustain. Cities Soc. 71, 2021. http://dx.doi.org/10.1016/j.scs.2021.102962, (in English), Art No. 102962. Ratnam, 2020, Future low-inertia power systems: Requirements, issues, and solutions - a review, Renew. Sustain. Energy Rev., 124, 10.1016/j.rser.2020.109773 Saeed, 2021, A review on microgrids’ challenges perspectives, IEEE Access, 9, 166502, 10.1109/ACCESS.2021.3135083 Singh, 2023, Deep learning-based cost-effective and responsive robot for autism treatment, Drones, 7, 81, 10.3390/drones7020081 Singh, 2023 Sutton, 2018 Wang, 2020, Sustainable and resilient distribution systems with networked microgrids, Proc. IEEE, 108, 238, 10.1109/JPROC.2019.2963605 Yan, 2021, Distribution network-constrained optimization of peer-to-peer transactive energy trading among multi-microgrids, IEEE Trans. Smart Grid, 12, 1033, 10.1109/TSG.2020.3032889 Yan, 2021, Blockchain for transacting energy and carbon allowance in networked microgrids, IEEE Trans. Smart Grid, 12, 4702, 10.1109/TSG.2021.3109103 Yang, 2019, Interactive energy management for enhancing power balances in multi-microgrids, IEEE Trans. Smart Grid, 10, 6055, 10.1109/TSG.2019.2896182 Ye, 2020, Deep reinforcement learning for strategic bidding in electricity markets, IEEE Trans. Smart Grid, 11, 1343, 10.1109/TSG.2019.2936142 Ye, 2021, A scalable privacy-preserving multi-agent deep reinforcement learning approach for large-scale peer-to-peer transactive energy trading, IEEE Trans. Smart Grid, 12, 5185, 10.1109/TSG.2021.3103917 Yin, 2021, Hybrid metaheuristic multi-layer reinforcement learning approach for two-level energy management strategy framework of multi-microgrid systems, Eng. Appl. Artif. Intell., 104, 10.1016/j.engappai.2021.104326 Yoldaş, 2017, Enhancing smart grid with microgrids: Challenges and opportunities, Renew. Sustain. Energy Rev., 72, 205, 10.1016/j.rser.2017.01.064 Zhang, 2021, Multi-agent safe policy learning for power management of networked microgrids, IEEE Trans. Smart Grid, 12, 1048, 10.1109/TSG.2020.3034827 Zhou, 2019, Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids, Appl. Energy, 253, 10.1016/j.apenergy.2019.113588 Zhu, 2022, Optimal bi-level bidding and dispatching strategy between active distribution network and virtual alliances using distributed robust multi-agent deep reinforcement learning, IEEE Trans. Smart Grid, 1 Zia, 2018, Microgrids energy management systems: A critical review on methods, solutions, and prospects, Appl. Energy, 222, 1033, 10.1016/j.apenergy.2018.04.103