Green power pricing and matching efficiency optimization for peer-to-peer trading platforms considering heterogeneity of supply and demand sides

The peer-to-peer (P2P) trading of green power through bilateral negotiation has led to issues such as the absence of a pricing mechanism and inefficiencies in power supply chains. In this study, a data-driven P2P online trading platform is designed, taking into account the preferences of suppliers for different customer groups. The platform includes two independent matching systems. The exclusive ( E ) system only accepts high-priced orders from green customers willing to pay a premium for green attributes. The shared ( S ) system handles orders from all customers, charging a floor price for conventional customers and an extra green premium for green customers. This study examines how heterogeneity in suppliers’ preferences affects their system selections and determines the optimal allocation scheme for green power orders of the platform and the pricing mechanism. The model is applied to real data from the Shanghai power market, revealing that the platform should increase the unit power price sold to green customers by 70% on top of the conventional power price and allocate 40% of the green demand to the System E to optimize the comprehensive matching efficiency. Interestingly, when the proportion of green customers in the market rises, the platform should reduce the green premium while increasing the proportion of green customers allocated to the System E . Furthermore, the comparison results with the single-system platform where all power supply and demand are integrated reveal that promoting the dual-system platform can significantly improve the average profit of power plants under China’s current energy consumption structure, thus facilitating the low-carbon transformation of the power supply chain.