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.

翻译：兼具生产与消费功能的分布式能源系统需要新方法协调各主体间的能量交换。联盟控制提供了一种框架，使主体能够通过形成群体来合作降低成本；然而，现有的自下而上联盟形成方法通常需要完全信息共享，这引发了隐私担忧并带来显著计算负担。本研究提出一种有限信息联盟形成算法，该方法仅需主体间共享有限聚合信息。通过构建候选联盟价值的上界，我们消除了对每个潜在合并求解优化问题的需求，在限制信息交换的同时显著降低了计算复杂度。我们证明了所提方法能保证成本不高于分散式运行模式。采用基于交替方向乘子法的分布式策略优化联盟，进一步限制了联盟内信息共享。将本框架嵌入模型预测控制方案，并基于真实数据进行评估，结果表明相较于完全信息方法，本方法在实现更优经济性能的同时计算成本显著降低。