| 摘要: | 隨著微電網蓬勃發展，讓微電網高效運轉已是一門重要議題。在這方面，多目標最佳化調度技術被廣泛應用，可以因應不同使用者的使用情境，同時考慮多個目標，實現微電網的減碳、經濟和安全穩定等多個方面的優化。本論文針對微電網多目標最佳化調度的方法和技術進行研究，首先，對微電網的架構和模式進行了介紹，分析了微電網的特點和面臨的問題。其次，介紹了電網設備及數學模型和多目標最佳化調度的基本概念和方法，包括模型建立、目標函數設計、ㄒㄧ限制式定義等等。接著，針對微電網的多目標最佳化調度問題，使用熵值法結合AHP進行權重分配，並使用粒子群優化的調度方法，進行了Matlab模擬實驗與實際場域測試。實驗結果表明，該方法可以有效地提高微電網的運行效率和經濟性，同時減少碳排量，並針對不同的使用情境，做出不同的因應對策。最後，針對微電網多目標最佳化調度的未來研究方向進行了討論，針對可行的研究方向，包括進一步改進算法性能、增加目標、增強系統韌性和智能化管理等方面。;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. |
