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The increase in renewable energy on the consumer side gives place to newdynamics in the energy grids. Participants in a microgrid can produce energyand trade it with their peers (peer-to-peer) with the permission of the energyprovider. In such a scenario, the stochastic nature of distributed renewableenergy generators and energy consumption increases the complexity of definingfair prices for buying and selling energy. In this study, we introduce areinforcement learning framework to help solve this issue by training an agentto set the prices that maximize the profit of all components in the microgrid,aiming to facilitate the implementation of P2P grids in real-life scenarios.The microgrid considers consumers, prosumers, the service provider, and acommunity battery. Experimental results on the \textit{Pymgrid} dataset show asuccessful approach to price optimization for all components in the microgrid.The proposed framework ensures flexibility to account for the interest of thesecomponents, as well as the ratio of consumers and prosumers in the microgrid.The results also examine the effect of changing the capacity of the communitybattery on the profit of the system. The implementation code is available\href{https://github.com/Artifitialleap-MBZUAI/rl-p2p-price-prediction}{here}.

Energy Pricing in P2P Energy Systems Using Reinforcement Learning