Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids
In extreme disasters where severe main grid failures lead to widespread power outages in distribution networks, rapid critical load restoration (CLR) becomes crucial for enhancing power supply reliability. Aiming to improve distribution network resilience, this paper proposes an entropy-driven multi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525005162 |
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| author | Changchun Cai Fenglu Gan Yong Cui Bo Li Shixi Hou |
| author_facet | Changchun Cai Fenglu Gan Yong Cui Bo Li Shixi Hou |
| author_sort | Changchun Cai |
| collection | DOAJ |
| description | In extreme disasters where severe main grid failures lead to widespread power outages in distribution networks, rapid critical load restoration (CLR) becomes crucial for enhancing power supply reliability. Aiming to improve distribution network resilience, this paper proposes an entropy-driven multi-agent deep reinforcement learning (MADRL) framework coordinating mobile energy storage systems (MESS) and microgrid reconfiguration. First, with critical load restoration as the objective function, coordinated optimization model for MESS dispatch and network reconfiguration is constructed, which comprehensively considers security constraints of both distribution networks and microgrids. Subsequently, the coordinated optimization problem is formulated as a Markov Decision Process (MDP). Then, a multi-agent deep Q-learning (MADQL) algorithm is developed to search for optimal strategies, featuring a topology aware entropy-driven exploration (TAEE) mechanism to discover high-value actions and accelerate training convergence. Additionally, an action masking technique is introduced to enforce operational safety by dynamically filtering constraint-violating actions. Finally, extensive numerical results validate the effectiveness of our proposed method. |
| format | Article |
| id | doaj-art-979b260c08804e43b9cd4fa2ceec4361 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-979b260c08804e43b9cd4fa2ceec43612025-08-20T03:36:06ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011096810.1016/j.ijepes.2025.110968Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgridsChangchun Cai0Fenglu Gan1Yong Cui2Bo Li3Shixi Hou4College of Artificial Intelligence and Automation, Hohai University, Jiangsu, China; Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Hohai University, Jiangsu, China; Corresponding author.College of Artificial Intelligence and Automation, Hohai University, Jiangsu, China; Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Hohai University, Jiangsu, ChinaCollege of Management, Anhui Science and Technology University, Anhui, ChinaCollege of Artificial Intelligence and Automation, Hohai University, Jiangsu, China; Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Hohai University, Jiangsu, ChinaCollege of Artificial Intelligence and Automation, Hohai University, Jiangsu, China; Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Hohai University, Jiangsu, ChinaIn extreme disasters where severe main grid failures lead to widespread power outages in distribution networks, rapid critical load restoration (CLR) becomes crucial for enhancing power supply reliability. Aiming to improve distribution network resilience, this paper proposes an entropy-driven multi-agent deep reinforcement learning (MADRL) framework coordinating mobile energy storage systems (MESS) and microgrid reconfiguration. First, with critical load restoration as the objective function, coordinated optimization model for MESS dispatch and network reconfiguration is constructed, which comprehensively considers security constraints of both distribution networks and microgrids. Subsequently, the coordinated optimization problem is formulated as a Markov Decision Process (MDP). Then, a multi-agent deep Q-learning (MADQL) algorithm is developed to search for optimal strategies, featuring a topology aware entropy-driven exploration (TAEE) mechanism to discover high-value actions and accelerate training convergence. Additionally, an action masking technique is introduced to enforce operational safety by dynamically filtering constraint-violating actions. Finally, extensive numerical results validate the effectiveness of our proposed method.http://www.sciencedirect.com/science/article/pii/S0142061525005162Distribution systemNature disasterDeep reinforcement learningMicrogridsMobile energy storageResilience |
| spellingShingle | Changchun Cai Fenglu Gan Yong Cui Bo Li Shixi Hou Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids International Journal of Electrical Power & Energy Systems Distribution system Nature disaster Deep reinforcement learning Microgrids Mobile energy storage Resilience |
| title | Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids |
| title_full | Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids |
| title_fullStr | Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids |
| title_full_unstemmed | Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids |
| title_short | Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids |
| title_sort | entropy driven multi agent deep reinforcement learning for resilient distribution networks coordinating mess and microgrids |
| topic | Distribution system Nature disaster Deep reinforcement learning Microgrids Mobile energy storage Resilience |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525005162 |
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