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Energy Pricing in P2P Energy Systems Using Reinforcement Learning

arXiv · · Notable

RL Research Infrastructure Energy Optimization

Summary

This paper presents a reinforcement learning framework for optimizing energy pricing in peer-to-peer (P2P) energy systems. The framework aims to maximize the profit of all components in a microgrid, including consumers, prosumers, the service provider, and a community battery. Experimental results on the Pymgrid dataset demonstrate the approach's effectiveness in price optimization, considering the interests of different components and the impact of community battery capacity.

Keywords

reinforcement learning · peer-to-peer · energy pricing · microgrid · Pymgrid

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