Multilayer Collaborative Optimization for the System Configuration, Operation, and Maintenance of Smart Community Microgrids

• Published in: International journal of energy research Vol. 2025; no. 1
• Main Authors: Liu, Jiangshan, Zhou, Qi, Bi, Youyi
• Format: Journal Article
• Language: English
• Published: Bognor Regis John Wiley & Sons, Inc 01.01.2025
• Subjects: Algorithms; Alternative energy sources; Architecture; Collaboration; Configurations; Consumption; Cost effectiveness; Design optimization; Distributed generation; Electricity; Electricity consumption; Energy; Energy Internet; Energy resources; Energy storage; Genetic algorithms; Internet of Things; Linear programming; Maintenance; Methods; Multilayers; Multiple objective analysis; Optimization algorithms; Particle swarm optimization; Planning; Predictions; Renewable energy; Renewable energy sources; Renewable resources; Systems design; User groups; User needs
• ISSN: 0363-907X; 1099-114X; 1099-114X
• DOI: 10.1155/er/7756589

Smart community microgrids are capable of efficiently addressing the energy and environmental challenges faced by cities. However, the inherent instability of renewable energy sources and the diverse nature of user demands pose challenges to the safe operation of community power systems. In this article, we first introduce a comprehensive system architecture, and an operational framework based on Energy Internet of Things (EIoT), which considers system‐level safety, reliability, and cost‐effectiveness, thereby enhancing the system’s coordination and performance. Next, we propose a bi‐level coordinated optimization method based on the users’ electricity consumption behaviors. At the planning level, we employ a multiobjective optimization approach to determine the most suitable microgrid configurations that cater to the requirements of various user groups, and the results derived from adaptive weight particle swarm optimization (PSO) algorithm are fed back to the operational level. At the operational level, a 24‐h time scale is selected, and the economic efficiency problem is addressed using a linear programming method. The operational decision results are then fed back to the planning level for major maintenance of the microgrid system. Meanwhile, we employ trend prediction methods to categorize maintenance tasks into short‐term and long‐term operations based on an analysis of daily operational data. The short‐term prediction results can serve as a reference to guide daily short‐term operations and maintenance tasks, while the long‐term prediction results can inform renovation and reconstruction initiatives for community microgrid. Finally, we choose a community as the subject of our study, and the results indicate that our research can provide new methods for the design and operation of microgrid in smart communities, thereby improving the scalability of the community’s power system.