Networked Multiagent Reinforcement Learning for Peer-to-Peer Energy Trading
CoRR(2024)
摘要
Utilizing distributed renewable and energy storage resources in local
distribution networks via peer-to-peer (P2P) energy trading has long been
touted as a solution to improve energy systems' resilience and sustainability.
Consumers and prosumers (those who have energy generation resources), however,
do not have the 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 multi-agent reinforcement learning
(MARL) frameworks 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 frameworks can integrate physical network constraints to realize voltage
control, hence ensuring physical feasibility of the P2P energy trading and
paving way for real-world implementations.
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