A priority-based approach for peer-to-peer energy trading using cooperative game theory in local energy community

Abstract

The energy sector is undergoing a paradigm shift to integrate the increasing volume of embedded renewable energy generation and create Local Energy Communities (LEC). Peer-to-Peer (P2P) energy trading is an encouraging paradigm used to increase usage of renewable energy, decrease consumers' electricity bills, and provide revenue to prosumers. It also improves the usage of Distributed Energy Resources (DERs) in the smart grid and reduces transmission and distribution losses. However, challenges such as unpredictability and intermittency of DER's may result in instability of P2P energy trading. In our work, we propose a cooperative game theory framework to expedite stable trading algorithms and incentivize individual users. This trading algorithm offers various priorities at each time interval depending on parameters such as geographic location, maximum energy demand, maximum energy generated, and pricing mechanism. We have considered a grand coalition whose objective is to maximize the coalition's social welfare and ensure a win-win approach for both consumers and prosumers. Hence the grand coalition made by the cooperative game is in Nash equilibrium as no peer wants to perform the merge and split from its current location. In the proposed algorithm, LEC includes 100 players (50 prosumers and 50 consumers), a community energy storage system (CES), and 15 Electric Vehicle charging points. The best operational output priority was also evaluated in this work for each time interval with associated distributed solar PV and CES. Results strongly support that using the best suitable priority for each time interval is beneficial rather than having one priority for an entire day. An economic analysis to distribute the revenue generated from the grand coalition in a fair manner is analyzed in this work. From the economic evaluation, it is apparent that prosumers have high revenue, and consumers save electricity bills when using the proposed algorithm.