A Multiagent Minority-Game-Based Demand-Response Management of Smart Buildings Toward Peak Load Reduction

TLDR

The study focuses on a building with multiple rooms powered by a main electricity grid and an auxiliary solar grid. The paper proposes a cyber‑physical smart‑building management system that uses a smart‑gateway network and a multiagent minority‑game demand‑response scheme to reduce peak grid demand and fairly distribute solar energy. The system extracts uncertain energy signatures from each room via the smart‑gateway network, classifies rooms into agent types, and applies a minority‑game algorithm to orchestrate demand‑response. Experimental results show that the uncertainty‑aware MG‑EMS improves solar‑energy utilization by up to 50× and 145× over static and price‑competition EMS, cuts main‑grid peak load by 38.5% in summer and 15.8% in winter, and reduces uncertainty by 23% while boosting balanced allocation by 37%.

Abstract

This paper presents a cyber-physical management of smart buildings based on smart-gateway network with distributed and real-time energy data collection and analytics. We consider a building with multiple rooms supplied with one main electricity grid and one additional solar energy grid. Based on smart-gateway network, energy signatures of rooms are first extracted with consideration of uncertainty and further classified as different types of agents. Then, a multiagent minority-game (MG)-based demand-response management is introduced to reduce peak demand on the main electricity grid and also to fairly allocate solar energy on the additional grid. Experiment results show that compared to the traditional static and centralized energy-management system (EMS), and the recent multiagent EMS using price-demand competition, the proposed uncertainty-aware MG-EMS can achieve up to 50× and 145× utilization rate improvements, respectively, regarding to the fairness of solar energy resource allocation. More importantly, the peak load from the main electricity grid is reduced by 38.50% in summer and 15.83% in winter based on benchmarked energy data of building. Lastly, an average 23% uncertainty can be reduced with an according 37% balanced energy allocation improved comparing to the MG-EMS without consideration of uncertainty.