本文旨在探討微電網基於主從控制下各種運轉的狀況與透過蟻群最佳化演算法制定儲能設備的排程策略，並透過蟻群最佳化演算法找出建置儲能設備的路徑規劃之最佳解。本文以核研所微電網作為模擬系統，並利用Matlab/Simulink模擬軟體建立微電網系統模型。模型可模擬多種微電網運轉模式：併網運轉、孤島運轉及併網運轉至孤島運轉，透過電壓調節將可有效降低分散式能源併網所產生的擾動。其次，本文應用蟻群最佳化演算法於微電網以優化儲能設備運轉排程策略，解決微電網的儲能設備充放電排程之問題，經由實驗結果顯示，透過蟻群最佳化演算法可滿足微電網的多種特性需求，使微電網達到削峰填谷、尖峰用電調節之作用，並可降低關鍵時段尖峰負載用電和保有系統之可靠性。最後，本文使用蟻群最佳化演算法應用於儲能設備建置之最佳路徑規劃，透過費洛蒙更新機制，並使用演算法排除冗餘路徑，模擬結果證實本文提出的方法用於路徑規劃具有相當穩健且搜尋全局最佳解時能更快收斂。

This thesis aims to discuss the various operating conditions of microgrid based on master slave control and the scheduling strategy of energy storage devices through ant colony optimization algorithm and try to find the optimal solution for the path planning of the energy storage devices through the ant colony optimization algorithm. This thesis uses the microgrid in the nuclear research institute as the simulation system and uses Matlab/Simulink simulation software to build models in microgrid systems. The model can simulate a variety of microgrid operating modes, such as grid-connected, islanded, and grid-connected to islanded operations. Through the voltage regulation, the disturbance caused by the interconnection of the distributed energy sources can be effectively reduced. Secondly, this thesis applies the ant colony optimization algorithm to the operation scheduling strategy of microgrid energy storage devices and solves the problem of charge and discharge scheduling of energy storage devices in microgrid. The experimental results show that the ant colony optimization algorithm can meet the multiple characteristics of the microgrid. The microgrid can achieve the function of peak cutting and peak shaving, and can reduce the peak load power during critical periods and maintain the reliability of the system. Finally, the ant colony optimization algorithm is applied to the optimal path planning for energy storage device construction. The pheromone update mechanism is used and the algorithm is used to eliminate redundant paths. The simulation results confirm that the proposed method is used for the path. The plan is fairly robust and searches for the global optimal solution for faster convergence.

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