Coalition Formation with Limited Information Sharing for Local Energy Management

2026-03-30

AI Summary

This work addresses the challenges of privacy leakage and high computational overhead in cooperative energy exchange among prosumers within distributed energy systems. To this end, a coalition formation algorithm based on limited information exchange is proposed. By constructing an upper bound on the value of candidate coalitions, the method avoids repeatedly solving optimization problems, while leveraging the Alternating Direction Method of Multipliers (ADMM) to enable distributed optimization within coalitions. The approach is embedded within a Model Predictive Control (MPC) framework to support real-time scheduling. It achieves total operational costs no higher than those of fully decentralized operation while substantially reducing both information-sharing requirements and computational complexity. Experimental results using real-world data demonstrate that the proposed scheme improves economic performance over fully decentralized control and incurs significantly lower computational overhead than full-information-sharing approaches, effectively balancing privacy preservation with coordination efficiency.

Abstract

Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to cooperatively reduce costs; however, existing bottom-up coalition-formation methods typically require full information sharing, raising privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition-formation algorithm that requires only limited aggregate information exchange among agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real-world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full-information approaches.

Problem

• Coalition Formation
• Limited Information Sharing
• Local Energy Management
• Privacy Preservation
• Computational Complexity

Innovation

• limited information sharing
• coalition formation
• distributed optimization
• ADMM
• model predictive control