Enabling self-approaching optimization of Home Energy Management System through multi-agent systems

With the diversification of application scenarios in Home Energy Management Systems (HEMS), its autonomous control systems must dynamically adapt to evolving electricity usage demands and operational environment, akin to intelligent robots, to deliver automated, precise, responsive, and sustainable electricity services. Although dedicated solutions for these electricity services perform well under specified application scenarios, differences in focus and implementation prerequisites result in suboptimal system performance in highly fuzzy and temporally variable operational environments. This presents significant challenges for HEMS in delivering automated, precise, responsive, and sustainable electricity services. In this paper, a comprehensive, flexible, and actionable framework, termed the Electricity Usage Scenario (EUS), is first proposed to uniformly describe the models underlying these electricity services. The standard EUS is adopted to support ongoing decision-making for the optimization and control of electrical appliances. Additionally, a compatibility assessment method that incorporates spatiotemporal features is proposed. The degree of compatibility reflects the adaptation between the operation state of electrical appliances, electricity usage demands, and the operational environment, while its slope serves as the trigger for consistency control. Subsequently, a non-hierarchical multi-agent system based on an Autonomous Decentralized System (ADS) is designed. This architecture offers advantages in terms of online fault tolerance, system scalability, and maintainability. Furthermore, the types and functions of specialized agents within the multi-agent system are defined to ensure that HEMS can autonomously provide responsive electricity services. To further tackle the adverse effects caused by temporal variability, a self-approaching optimization consistency control method is introduced, implemented through Behavior Tree (BT), aiming to keep the multi-agent system in a state that persistently approaches optimal performance, thereby sustaining electricity services. Finally, the proposed methodologies are validated through both a real-world office environment and a virtual simulation system based on the discrete event simulation framework SimPy. The results demonstrate that the proposed methods effectively deliver automated, precise, responsive, and sustainable electricity services, affirming their practical feasibility and application potential.