Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

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dc.contributor.authorFeng, Chen
dc.contributor.authorChen, Yihsu
dc.contributor.authorLiu, Andrew Lu
dc.date.accessioned2022-12-27T19:04:39Z
dc.date.available2022-12-27T19:04:39Z
dc.date.issued2023-01-03
dc.description.abstractUtilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system’s resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers’ bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.321
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/102952
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDistributed, Renewable, and Mobile Resources
dc.subjectmulti-agent reinforcement learning
dc.subjectpeer-to-peer trading
dc.subjectvoltage control
dc.titleDecentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network
dc.type.dcmitext
prism.startingpage2600

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