Peer-to-Peer transactive energy trading of multiple microgrids considering renewable energy uncertainty

Abstract

Distributed renewable energy requires market-based measures to remain competitive as subsidies are phased out. However, the intermittence and volatility of renewable energy power generation lead to great challenges in decision-making. To address the uncertainty issues induced by inaccurate RE forecast, this paper proposed a peer-to-peer transactive energy trading strategy for multiple microgrids based on distributionally robust optimization. First, an uncertainty fuzzy set based on Wasserstein distance is created for the renewable energy prediction errors in each microgrid. Second, a day-ahead microgrids peer-to-peer transactive energy trading model is proposed based on the distributionally robust optimization theory to address the power fluctuation problems of renewable energy. Third, using the dual theory, the proposed nonlinear model is addressed by transforming it into a linear and convex programming problem. Considering the independence of microgrids, a distributed strategy based on the alternating direction method of multipliers is then developed to preserve their privacy. Finally, the case study proves that the method proposed can increase the income of microgrids containing renewable energy through peer-to-peer transactions, protect the privacy of microgrids, and then promote the development of renewable energy. The distributionally robust optimization approach also guarantees the economy and reliability of the transaction results for real-time deployment.