As subsidies for distributed renewable energy are reduced, market-based measures are essential to promote the integration of renewable energy into the grid. However, renewable energy generation’s intermittent and volatile nature creates significant decision-making challenges. To mitigate uncertainty from inaccurate renewable energy forecasts, this chapter presents a peer-to-peer (P2P) energy trading strategy for multiple microgrids (MGs) using distributionally robust optimization (DRO). Firstly, a fuzzy set based on Wasserstein distance is developed to model prediction errors for each MG’s renewable energy. Secondly, a day-ahead P2P energy trading model utilizing DRO is proposed to handle power fluctuations. Thirdly, dual theory converts the nonlinear model into a linear and convex programming problem. A distributed strategy based on the alternating direction method of multipliers (ADMM) is applied to maintain MG independence and privacy. Finally, the case study demonstrates that the proposed trading strategy can enhance MG revenue from P2P transactions, protect MG privacy, and support renewable energy development. The DRO approach also ensures the economic and reliable execution of transactions for real-time implementation.