Computational modeling demonstrates reduced operational costs and enhanced green energy sharing in agricultural microgrids, indicating scalable decentralized coordination without central authorities.
Driven by rural energy decarbonization and the electrification of agricultural production, agricultural microgrids (agri-microgrids) have become a critical paradigm for accommodating distributed renewable generation and volatile farming loads. However, agri-microgrids usually face acute cost sensitivity. Coordinated operation of autonomous agri-microgrids offers a promising solution. Yet coordinating heterogeneous energy resources to exploit temporal complementarity while fairly allocating costs remains a key challenge. Information asymmetry, the lack of trusted node, and the computational complexity of revenue allocation impede cooperative dispatch of multi-microgrids.This paper proposes a decentralized game-theoretic framework for cooperative multi-microgrid dispatch. Firstly, a distributed node information collection algorithm is designed to acquire necessary data while preserving local privacy. Secondly, a trusted-node election method is proposed to enable reliable coalition profit calculation without external authorities or predefined centralized coordinators. Then, an enhanced Shapley value-based revenue allocation strategy, which reduces the computational complexity, is presented. Finally, case studies verify the effectiveness of the proposed algorithms in reducing operational costs and promoting green energy coordination among agri-microgrids.
No takes yet. Share an insight, caveat, or question.
Han et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: