Real-Time Peer-to-Peer Energy Trading for Multi-Microgrids: Improved Double Auction Mechanism and Prediction-Free Online Trading Approach
Kaidi Huang, Lin Cheng, Yue Zhou, Fashun Shi, Yufei Xi, Yingrui Zhuang, Ning Qi
TL;DR
The paper tackles real-time P2P energy trading among interconnected microgrids, tackling two core problems: high computational burden from high-dimensional bids and myopic decision-making in prediction-free online settings. It introduces an improved double auction with an adaptive step-size search (ASSA) that achieves exact market clearing with significantly reduced participant burden, plus a prediction-free data-driven dual-reference online optimization (DDOO) that uses an informative reference trajectory and a real-time price benchmark to guide storage decisions. Empirical results on a 20-microgrid system show substantial gains: self-sufficiency increases, reverse power flows decrease, and average operating costs drop (e.g., ~19% vs baseline), while achieving rapid convergence (≈2.07 iterations per 5-minute interval) and favorable comparisons to Lyapunov optimization and MPC (10–13% cost reductions with a 5.76% optimality gap vs hindsight. The framework preserves privacy, scales to large markets, and demonstrates practical potential for enhancing local RES utilization and economic efficiency in real-time P2P trading.
Abstract
Peer-to-peer energy trading offers a promising solution for enhancing renewable energy utilization and economic benefits within interconnected microgrids. However, existing real-time P2P markets face two key challenges: high computational complexity in trading mechanisms, and suboptimal participant decision-making under diverse uncertainties. Existing prediction-based decision-making methods rely heavily on accurate forecasts, which are typically unavailable for microgrids, while prediction-free methods suffer from myopic behaviors. To address these challenges, this paper proposes an improved double auction mechanism combined with an adaptive step-size search algorithm to reduce computational burden, and a data-driven dual-reference online optimization (DDOO) framework to enhance participant decision-making. The improved mechanism simplifies bidding procedures, significantly reducing computational burden and ensuring rapid convergence to the market equilibrium. Additionally, the prediction-free DDOO framework mitigates myopic decision-making by introducing two informative reference signals. Case studies on a 20-microgrid system demonstrate the effectiveness and scalability of the proposed mechanism and approach. The improved mechanism significantly decreases the computational time while increasing local energy self-sufficiency periods from 0.01% to 29.86%, reducing reverse power flow periods from 24.51% to 3.96%, and lowering average operating costs by 19.20%. Compared with conventional approaches such as Lyapunov optimization and model predictive control, the DDOO framework achieves a 10%-13% reduction in operating costs with an optimality gap of only 5.76%.