Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach | Paper Citation Record

Observation 564904a9-a669-4278-971d-2180db90adbf · outbound

This paper cites Battling the extreme: A study on the power system resilience,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Battling the extreme: A study on the power system resilience,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.326022Z

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Observation 070eb8a3-6433-4c02-a03f-54e51b30a59e · outbound

This paper cites Power system resilience enhancement in typhoons using a three-stage day-ahead unit commitment,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Power system resilience enhancement in typhoons using a three-stage day-ahead unit commitment,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.319305Z

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Observation 7e835aa4-dbf1-4f86-8bed-8780707c5292 · outbound

This paper cites Seismic-resilient electric power distribution systems: Harnessing the mobility of power sources,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Seismic-resilient electric power distribution systems: Harnessing the mobility of power sources,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.312596Z

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Observation 4b42eae9-4e90-46e5-af24-e27c56a65341 · outbound

This paper cites On microgrids and resilience: A comprehensive review on modeling and operational strategies,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach On microgrids and resilience: A comprehensive review on modeling and operational strategies,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.306689Z

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Observation 41421fd0-b171-4f66-89f8-20fdc9f6513c · outbound

This paper cites Optimizing service restoration in distribution systems with uncertain repair time and demand,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Optimizing service restoration in distribution systems with uncertain repair time and demand,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.300471Z

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Observation de4e7c58-3f04-4881-9bdf-e1b10d461027 · outbound

This paper cites Hybrid modeling based co-optimization of crew dispatch and distribution system restoration considering multiple uncertainties,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Hybrid modeling based co-optimization of crew dispatch and distribution system restoration considering multiple uncertainties,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.293990Z

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Observation b2b2097e-cee6-4353-994d-d6d66069fd12 · outbound

This paper cites Mobile emergency generator pre-positioning and real-time allocation for resilient response to natural disasters,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Mobile emergency generator pre-positioning and real-time allocation for resilient response to natural disasters,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.287891Z

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Observation 9290afaf-4737-48f0-a81f-7011756f77db · outbound

This paper cites Mobile emergency generator planning in resilient distribution systems: A three-stage stochastic model with nonanticipativity constraints,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Mobile emergency generator planning in resilient distribution systems: A three-stage stochastic model with nonanticipativity constraints,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.281801Z

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Observation 9c170fe3-f53f-4443-bae8-f4887396338e · outbound

This paper cites Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.274592Z

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Observation f5de6a67-bf0a-412c-b7f2-e7c01d5075ca · outbound

This paper cites Routing and scheduling of mobile power sources for distribution system resilience enhancement,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Routing and scheduling of mobile power sources for distribution system resilience enhancement,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.266752Z

Source-reported events for the cited work

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Observation d5da2621-a86b-4a93-9e2e-b1c67911ddc5 · outbound

This paper cites Resilient disaster recovery logistics of distribution systems: Co-optimize service restoration with repair crew and mobile power source dispatch,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilient disaster recovery logistics of distribution systems: Co-optimize service restoration with repair crew and mobile power source dispatch,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.259667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9dfe08e2-9201-4ede-8ad7-95eeb3bd22a9 · outbound

This paper cites Resilient service restoration for unbalanced distribution systems with distributed energy resources by leveraging mobile generators,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilient service restoration for unbalanced distribution systems with distributed energy resources by leveraging mobile generators,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.251346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 863a0f22-a4f6-4a87-8ed1-028434515375 · outbound

This paper cites Multiperiod distribution system restoration with routing repair crews, mobile electric vehicles, and soft-open-point networked microgrids,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multiperiod distribution system restoration with routing repair crews, mobile electric vehicles, and soft-open-point networked microgrids,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.241978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e220f412-fa65-4add-b7b3-f6e11a842c38 · outbound

This paper cites Stochastic pre-event prepa- ration for enhancing resilience of distribution systems,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Stochastic pre-event prepa- ration for enhancing resilience of distribution systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.234228Z

Source-reported events for the cited work

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Observation 7191b6a6-3c00-4d05-9379-4604a9d371a7 · outbound

This paper cites Multi-period restoration model for integrated power-hydrogen systems considering transportation states,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multi-period restoration model for integrated power-hydrogen systems considering transportation states,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.225996Z

Source-reported events for the cited work

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Observation d86c70ff-429a-4369-abf8-6d72f6a54b48 · outbound

This paper cites A sequential black-start restoration model for resilient active distribution networks,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A sequential black-start restoration model for resilient active distribution networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.219246Z

Source-reported events for the cited work

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Observation ec508d92-d3d4-412a-859a-59fbb07f48bb · outbound

This paper cites A new model for resilient distribution systems by microgrids formation,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A new model for resilient distribution systems by microgrids formation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.211287Z

Source-reported events for the cited work

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Observation e94e7da9-9658-41c8-a980-f3d2f5386b03 · outbound

This paper cites A resilient microgrid formation strategy for load restoration considering master-slave distributed genera- tors and topology reconfiguration,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A resilient microgrid formation strategy for load restoration considering master-slave distributed genera- tors and topology reconfiguration,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.202602Z

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Observation ead924e4-967a-4672-92eb-2fe2429df70f · outbound

This paper cites A full decentralized multi-agent service restoration for distribution network with dgs,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A full decentralized multi-agent service restoration for distribution network with dgs,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.194292Z

Source-reported events for the cited work

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Observation 71ea2c5c-4d56-4b67-ba67-1f59c4e17f7d · outbound

This paper cites A resilience-oriented centralised-to-decentralised framework for networked microgrids man- agement,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A resilience-oriented centralised-to-decentralised framework for networked microgrids man- agement,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.185309Z

Source-reported events for the cited work

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Observation 5f23e7b1-3638-4a69-bf53-720d163116c8 · outbound

This paper cites an unresolved cited work.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Unresolved cited work

Reference 21

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Source-reported events for the cited work

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Observation 4bcefbea-347e-4dee-83a5-7c3abddc73c1 · outbound

This paper cites Distribution system resilience under asynchronous information using deep reinforcement learning,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Distribution system resilience under asynchronous information using deep reinforcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.173896Z

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Observation fda80481-86d4-42c4-ab02-e02f41a6f23d · outbound

This paper cites Deep reinforcement learning based model-free on-line dynamic multi-microgrid formation to enhance resilience,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Deep reinforcement learning based model-free on-line dynamic multi-microgrid formation to enhance resilience,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.167293Z

Source-reported events for the cited work

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Observation 2c20145f-5c4c-4d4f-8edc-385fec5be849 · outbound

This paper cites A deep reinforce- ment learning-based multi-agent framework to enhance power system resilience using shunt resources,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A deep reinforce- ment learning-based multi-agent framework to enhance power system resilience using shunt resources,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.160685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1fd9916-2e61-4d17-9608-f030ac48f760 · outbound

This paper cites Resilient load restoration in microgrids considering mobile energy storage fleets: A deep reinforcement learning approach,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilient load restoration in microgrids considering mobile energy storage fleets: A deep reinforcement learning approach,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.151547Z

Source-reported events for the cited work

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Observation 21312d87-6ff0-43b2-a7fc-307050df121e · outbound

This paper cites Multi-agent safe policy learning for power management of networked microgrids,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multi-agent safe policy learning for power management of networked microgrids,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.143588Z

Source-reported events for the cited work

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Observation db581155-79e8-4e35-b10a-a41de20ad792 · outbound

This paper cites Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.136077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca6dffcd-d95d-46cb-ad50-d872e2c21520 · outbound

This paper cites A three-level planning model for optimal sizing of networked microgrids considering a trade-off between resilience and cost,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A three-level planning model for optimal sizing of networked microgrids considering a trade-off between resilience and cost,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.127796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c6d810c-9473-4286-a09e-8940d51f4119 · outbound

This paper cites Research on resilience of power systems under natural disasters—a review,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Research on resilience of power systems under natural disasters—a review,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.120781Z

Source-reported events for the cited work

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Observation fdb57cf6-c437-4e94-b5f9-9e956a39754c · outbound

This paper cites Theory and application study of the road traffic impedance function,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Theory and application study of the road traffic impedance function,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.113888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b760787-dc8b-4b0e-910c-34a1d1738298 · outbound

This paper cites Network reconfiguration in distribution systems for loss reduction and load balancing,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Network reconfiguration in distribution systems for loss reduction and load balancing,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.106529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5623dd97-3af2-4036-a94e-8e2ebb11fca0 · outbound

This paper cites A two-level simulation- assisted sequential distribution system restoration model with frequency dynamics constraints,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A two-level simulation- assisted sequential distribution system restoration model with frequency dynamics constraints,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.098850Z

Source-reported events for the cited work

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Observation 59e9f1c0-d2ae-42b3-9112-1c00abfa3284 · outbound

This paper cites an unresolved cited work.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:44:09.091328Z

Source-reported events for the cited work

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Observation be706a7e-743d-4d18-9d1b-27ba91178454 · outbound

This paper cites A learning-based power management method for networked microgrids under incomplete information,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A learning-based power management method for networked microgrids under incomplete information,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.082623Z

Source-reported events for the cited work

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Observation 97e181bd-6554-4a6c-a43e-6c89d08d3b15 · outbound

This paper cites Estimating demand flexibility using siamese lstm neural networks,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Estimating demand flexibility using siamese lstm neural networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.075166Z

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Observation f25a2a6e-f657-407e-b294-5dc69b2b96cd · outbound

This paper cites A hybrid of deep reinforcement learning and local search for the vehicle routing problems,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A hybrid of deep reinforcement learning and local search for the vehicle routing problems,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.067765Z

Source-reported events for the cited work

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Observation dc21cec3-2450-4073-a9c7-e9e056ec7d62 · outbound

This paper cites Real-time operation management for battery swapping-charging system via multi-agent deep reinforcement learning,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Real-time operation management for battery swapping-charging system via multi-agent deep reinforcement learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.058332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b3a043b-23d1-4ef1-a5d3-4252712874c2 · outbound

This paper cites Charging cost aware fleet manage- ment for shared on-demand green logistic system,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Charging cost aware fleet manage- ment for shared on-demand green logistic system,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:09.048822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e397b75-6034-47f9-9bbe-be2aab9fddd4 · outbound

This paper cites an unresolved cited work.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:44:08.935050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 581d3924-92a7-4517-ab6e-27b0fdb53ad8 · outbound

This paper cites Hybrid multi-agent reinforcement learning for electric vehicle resilience control towards a low-carbon transition,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Hybrid multi-agent reinforcement learning for electric vehicle resilience control towards a low-carbon transition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:08.723114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48cb6cb2-a50a-4238-93e9-d9597dc2f5ee · outbound

This paper cites The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T14:44:07.828501Z

Source-reported events for the cited work

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Observation 116e38d6-2a14-4b46-9d0a-5c29f7ee37c8 · outbound

This paper cites Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:08.503927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47f01509-4b76-443b-b117-da043d5ec7b5 · outbound

This paper cites Residential load and rooftop pv generation: an australian distribution network dataset,.

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Residential load and rooftop pv generation: an australian distribution network dataset,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:44:08.266399Z

Source-reported events for the cited work

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