Peer-to-peer energy trading among multiple microgrids considering risks over uncertainty and distribution network reconfiguration: A fully distributed optimization method

Abstract

This paper proposes a two-layer optimization framework to co-optimize the P2P energy trading among multiple microgrids (MMGs) under uncertainty and optimal topology planning of the distribution networks (DNs). At the upper layer, the traditional verification optimal power flow model of DNs is transformed into a prosumer-focused and transaction-oriented dynamic network reconfiguration model. At the lower layer, uncertainty from wind power generations is integrated into the operating model of individual MGs and addressed by the stochastic programming (SP) method. Meanwhile, the conditional value at risk technique is introduced to find a trade-off between cost minimization and risk aversion flexibly. To establish the global negotiation mechanism among all participants (not only between distribution system operators and MGs, but also among MMGs), a fully distributed method is developed by combining an analytical target cascading algorithm and an alternating direction multiplier method. Furthermore, a diagonal quadratic approximation method is utilized to linearize the quadratic penalty term so that achieving parallel computing for all independent optimization subproblems. Simulations of different strategies, models, and distributed algorithms are implemented to verify the rationality and validity of the proposed method. The results of these case studies demonstrate that the proposed risk-averse SP approach can avoid over-optimistic solutions, the obtained P2P trading strategies are immune to uncertainty and P2P trading behaviors among MMGs can help reduce network losses of DNs. In addition, comparisons with other distributed algorithms verify the high performance of the proposed fully distributed method.