Energy trading and scheduling in networked microgrids using fuzzy bargaining game theory and distributionally robust optimization

Abstract

To ensure the robust performance of networked microgrids (MGs) against uncertainty and provide a fair energy trading scheme, this paper proposes a decentralized bi-level energy trading and scheduling framework equipped with an innovative incentive mechanism. The upper-level determines the robust decisions of internal scheduling within MGs and peer-to-peer (P2P) energy trading between MGs using a distributionally robust optimization model under an ambiguity set formed by principal component analysis (PCA). The ambiguity set accurately captures distributional information from renewable power generation data, reducing the unnecessary conservatism of robust solutions. The lower-level develops an asymmetric Nash bargaining game model with a new index, named fuzzy bargaining power (FBP), to fairly allocate trading benefits to MGs. This fuzzy index incentivizes MGs to proactively trade energy throughout the entire day, not just when energy selling or buying is in their interest. The upper and lower level problems are solved in a privacy-preserving manner by proposing a decentralized optimization algorithm based on the asynchronous alternating direction method of multipliers (ADMM). Numerical tests demonstrate the effectiveness of the proposed models in terms of solution robustness, profit distribution fairness, and computational performance.