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.

使用多智能体强化学习 (MARL) 框架，通过特定的供需比例 P2P 结算机制，自动化处理消费者的太阳能光伏和能源储存资源的竞价和管理，以及实现电压控制，确保 P2P 能源交易的物理可行性，并为实际应用铺平道路。