Coalition Formation with Limited Information Sharing for Local Energy Management

TL;DR

This paper introduces a limited information coalition formation algorithm for local energy management that reduces computational complexity and privacy concerns while maintaining cost efficiency.

Contribution

It proposes a novel coalition-formation method requiring only limited aggregate information, with proven cost guarantees and improved efficiency over existing approaches.

Findings

Reduces computational complexity compared to full-information methods.

Guarantees cost no greater than decentralized operation.

Demonstrates improved economic performance on real-world data.

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.