A robust framework for peer-to-peer energy trading with transmission costs consideration: A fuzzy possibilistic programming model

Abstract

The restructuring of traditional electricity market paradigms and the transition to decentralized energy systems, aligned with advancements in communication and control technologies, have led to the emergence of innovative trading mechanisms. Peer-to-peer (P2P) energy trading as a decentralized approach, enables direct transactions between producers and consumers without intermediaries. Large producers and consumers, as independent entities in wholesale markets, can participate in bilateral exchanges at the transmission network level. This paper designs a fully decentralized P2P energy trading framework for direct transactions between large producers and consumers within the transmission network. In this trading system, market players freely and independently select their trading peers to maximize their social welfare. Since energy exchanges occur within the network’s physical layer, incorporating technical network constraints becomes inevitable. Hence, in this study, the power transfer distribution factor (PTDF) method is developed to fairly and distributively allocate power loss costs and network utilization fees based on the network’s physical topology and the electrical distance between players. In this research, consumers’ demand is considered uncertain. To model this uncertainty, the robust possibilistic programming (RPP) method, based on fuzzy theory, is applied. RPP is efficient in scenarios without sufficient historical data, operating fully decentralized and proactive, avoiding decision-maker (DM) reliance for defining confidence levels of uncertain constraints. Finally, an alternating direction method of multipliers (ADMM) approach is employed to clear the proposed decentralized market, ensuring high rate convergence, participants’ privacy, and an optimal solution. Case studies on a 9-bus transmission network demonstrated the feasibility and effectiveness of the proposed decentralized market model. The simulation results indicate that considering network utilization fees in player transactions reduces the overall market social welfare by 20.1 %, accounting for power loss costs decreases it by 8.6 %, and applying both costs results in a 28.8 % reduction compared to the baseline scenario.