Optimization Design of Energy Management System for Based on Microservice Architecture and Reinforcement Learning

As software architecture continues to evolve and system scales expand, microservices architecture has gradually become the mainstream paradigm in software development, particularly well-suited for addressing increasingly complex business logic and the need for rapid feature iteration. The scalability, flexibility, and fault tolerance of microservices provide new solutions for traditional Energy Management Systems (EMS) in handling complex energy demands and supply management. However, challenges persist in areas such as resource scheduling in dynamic and complex environments, load balancing, and fault recovery mechanisms, making it difficult to meet the requirements of distributed energy and rapidly changing market conditions. To address these issues, this paper proposes an EMS architecture optimization design based on a combination of microservices architecture and reinforcement learning techniques. By decoupling the system through microservices, the scalability and flexibility of the EMS are enhanced. Furthermore, leveraging the advantages of reinforcement learning algorithms in complex decision-making and adaptability to dynamic environments, the proposed system achieves intelligent decision-making and control. Based on this approach, a web-based microservice EMS for process industries has been developed. Experimental results demonstrate that the proposed method significantly improves system performance and exhibits strong adaptability to complex business demands.