Distributionally robust framework for decentralized P2P energy trading in modern transactive energy markets

This paper presents a hierarchical mechanism for trading the three main structures of power systems, i.e., transmission, distribution, and microgrid, aiming to maximize each player’s profit in the framework of transactive energy (TE) concepts. To implement the hierarchical mechanism, we propose a tri-level optimization approach that enables each strategic player to trade securely across different energy markets while preserving privacy. At the first level of this problem, prosumers participate in both peer-to-peer transactive energy markets (P2PTEM) and the local electricity market (LEM) by submitting their offers and bids while preserving privacy. LEM and wholesale electricity market (WEM) clearing are modeled at the second and third levels of the problem, respectively. A new hybrid alternating direction method of multipliers (ADMM) and Karush–Kuhn–Tucker (KKT) algorithm is used to achieve the equilibrium point of the proposed tri-level problem, guaranteeing a globally optimal solution and maintaining decision-making independence for each strategic player. Additionally, this study examines the effect of electric vehicle (EV) penetration and the implementation of a smart charging strategy in prosumers’ peer-to-peer (P2P) electric energy trading. To address the need to maintain supply–demand balance in environments with high renewable energy resource (RES) penetration, a moment-based distributionally robust optimization (MDRO) method is proposed to handle the uncertainty in RES. The 33-bus and 6-node system tests have been used to model the distribution and transmission networks. Four different system tests were used to model networked microgrids (NMGs), considering the physical constraints of the network. The proposed approach provides a hierarchical decentralized optimization framework that enables the trading of prosumers, LEMs, and WEMs with privacy preservation and under high uncertainty conditions. The obtained results show that the implementation of the EV smart charging strategy reduces the prosumer operating costs in trading with LEMs and the P2PTEM by 2.99%, 2.8%, 3.7%, and 1.2%. Furthermore, the proposed solution leads to a robust solution that avoids overly conservative solutions while considering the ambiguity of the probability distributions.