| dc.description.abstract | With the rapid development of microgrids, achieving efficient operation has emerged as a critical research topic. In this context, multi-objective optimization scheduling techniques have gained significant attention, as they enable the consideration of various user scenarios while simultaneously addressing multiple objectives, such as carbon reduction, economic efficiency, and security and stability enhancement in microgrids. This thesis investigates the methodologies and technologies for multi-objective optimization scheduling in microgrids. Firstly, the structure and models of microgrids are introduced, followed by an analysis of their distinctive features and existing challenges. Subsequently, the fundamental concepts and methods of electrical grid equipment, mathematical modeling, and multi-objective optimization scheduling are expounded, encompassing model formulation, objective function design, and constraint formulation. Moreover, addressing the multi-objective optimization scheduling problem in microgrids, a hybrid approach incorporating the entropy method and the Analytic Hierarchy Process (AHP) is proposed to allocate appropriate weights, while a particle swarm optimization (PSO) algorithm is employed for scheduling. Comprehensive Matlab simulations and practical field experiments are conducted to evaluate the performance of the proposed approach. The experimental results demonstrate that the proposed methodology can effectively enhance the operational efficiency and economic feasibility of microgrids, while concurrently reducing carbon emissions. Furthermore, it enables tailored strategies for diverse usage scenarios. Lastly, prospective research directions pertaining to multi-objective optimization scheduling in microgrids are discussed, including further algorithmic enhancements, incorporation of additional objectives, reinforcement of system resilience, and the integration of intelligent management strategies. | en_US |