Two-stage peer-to-peer energy trading with combined uniform and discriminatory pricing mechanism

In peer-to-peer (P2P) energy trading, uniform pricing can improve market efficiency by lowering trading prices. However, this may result in fewer participants, particularly if P2P energy trading involves peers with a broad range of bidding prices. In contrast, discriminatory pricing can maximize the number of participants by offering multiple trading prices. Despite larger trading profits, discriminatory pricing mechanism is sensitive to computational issues and higher trading prices. Therefore, this study proposes two-stage P2P energy trading to obtain the merits of both methods by combining uniform and discriminatory pricing methods to improve the performance of market operations. In the first stage, market participants are classified using the Gaussian mixture model (GMM) and then, to handle security issues due to the uniform pricing mechanism, Stackelberg game theory is applied. The optimal number of trading capacity of each cluster was then determined through discriminatory pricing mechanism and alternating direction method of multipliers (ADMM) to improve the performance through distributive manner. The proposed method was evaluated based on community-based P2P energy trading by incorporating various types of customers. The findings demonstrate that the proposed method can obtain optimal compromised results that balance the features of uniform and discriminatory pricing methods.