A Secure Intra-Regional-Inter-Regional Peer-to-Peer Electricity Trading System for Electric Vehicles

Peer-to-peer (P2P) trading is becoming a prominent topic and demonstrating the development trend of integration with other theories in order to achieve an efficient allocation of electricity resources in the electric vehicle (EV) market. In this article, we present a novel Secure iNtra-regional-Inter-regional P2P Electricity Trading System (SNIPPETS) for EVs. A trading information prediction model is constructed based on Ensemble Learning, upon which an intra-regional-inter-regional trading mechanism is proposed to find the optimal electricity allocation strategy, including the price and volume of electricity traded between EVs, in order to maximize the regional overall social welfare. In the intra-regional-inter-regional trading mechanism, multi-objective optimization is performed within each region to coordinately maximize the benefits of different types of EVs, followed by an investigation of pricing competition among neighboring regions based on a supermodular game. Furthermore, blockchain is introduced to support transaction payments and improve data security and privacy. Finally, the proposed SNIPPETS is validated through case studies. Compared to the traditional energy trading system and representative existing trading systems, SNIPPETS can effectively improve the regional overall social welfare and has higher computational efficiency.

Automated summary

This article presents a novel Secure iNtra-regional-Inter-regional P2P Electricity Trading System (SNIPPETS) for electric vehicles (EVs). It proposes a trading information prediction model based on Ensemble Learning and an intra-regional-inter-regional trading mechanism to maximize overall social welfare. The introduction of blockchain enhances transaction payments, data security, and privacy. Case studies validate the effectiveness and computational efficiency of SNIPPETS compared to traditional and existing trading systems.