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

Date
2023-01-03
Authors
Feng, Chen
Chen, Yihsu
Liu, Andrew Lu
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2600
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Abstract
Utilizing 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.
Description
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Distributed, Renewable, and Mobile Resources, multi-agent reinforcement learning, peer-to-peer trading, voltage control
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10
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Proceedings of the 56th Hawaii International Conference on System Sciences
Table of Contents
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Attribution-NonCommercial-NoDerivatives 4.0 International
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