2021
DOI: 10.1049/esi2.12030
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Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

Abstract: The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed control mechanisms necessary to ensure reliable power system operations. In this article, the use of Reinforcement Learning techniques is proposed to implement load frequency control (LFC) without requiring a central authority. To this end, a detailed model of power system dynamic behaviour is formulated by representing individual generator dynamics, generator rate and n… Show more

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Cited by 5 publications
(3 citation statements)
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“…A control method for load frequency management has been proposed for all layers of microgrid control with no communication required between operating nodes, providing an extra security layer. This proposed DRL method is implemented with central learning but in a distributed manner [47]. Optimizing a microgrid in real-time is a challenging process that can be achieved using a double-deep Q network-based algorithm [48].…”
Section: Relevant Published Work In the Year 2021mentioning
confidence: 99%
“…A control method for load frequency management has been proposed for all layers of microgrid control with no communication required between operating nodes, providing an extra security layer. This proposed DRL method is implemented with central learning but in a distributed manner [47]. Optimizing a microgrid in real-time is a challenging process that can be achieved using a double-deep Q network-based algorithm [48].…”
Section: Relevant Published Work In the Year 2021mentioning
confidence: 99%
“…When applied to complex scenarios, such as multimicrogrids with EVs, the 'dimension explosion' problem in the action space may occur. In [31], the LFC problem was reconstructed as a Markov decision process, and a deep deterministic policy gradient (DDPG) algorithm with continuous action space was used to approximate the optimal solution for the LFC layer to realise the frequency control of a single island microgrid. Evidently, this cannot be applied to the coordination of multiple microgrids.…”
Section: Introductionmentioning
confidence: 99%
“…Outstanding research has achieved good evaluations in hybrid environments 7 or multiobjective tasks, 8 but it is only in recent years that MARL has been used in engineering applications. They mainly focus on scheduling and optimization problems in multiple engineering domains, such as traffic control, 9 autonomous driving, 10,11 base station communication, 12 load frequency optimization, 13 electric vehicle charging and discharging planning, 14 power allocation, 15 and so forth. In addition, as more complex problems are considered, more applicable systems are modeled and analyzed, such as Markov Repairable Systems, 16 Markov Jumping Systems, 17 and so forth.…”
Section: Introductionmentioning
confidence: 99%