本論文旨在研究含分散式儲能系統之微電網經濟調度問題。本論文以標的系統之歷史負載量與再生能源發電資料為基礎，利用徑狀類神經網路，訓練匯流排電壓、相角與負載實、虛功率間之神經層與權重值，以加速電力潮流分析，並以IEEE 30-bus標準測試系統進行驗證。所開發之電力潮流分析模組可以輸入各匯流排24小時之負載量，並據以算出系統線路逐時總線損，俾納為後續經濟調度考量。在經濟調度部分則以IEEE 10 units系統進行情境模擬與演算法驗證，以已排定之機組排程為基礎，考量分散式儲能系統之效用，以多目標蝙蝠演算法進行最佳經濟調度及提升負載因數，研析在不同再生能源滲透率下對可調度發電組之影響。本論文研究結果將有助微電網電力調度系統在高滲透率再生能源發電之情況下，做出妥適之調度作為。

The purpose of this thesis is to simulate and analyze economic dispatch for a microgrid with distributed storage systems. Based on historical load data and renewable energy data of microgrid, the neural layer and weight values between voltage, phase angle, and active and reactive power of loads are trained by a radial basis function neural network. The radial basis function neural network can speed up the calculation of power flow analysis and it will be verification by the IEEE 30-bus standard test system. The developed power flow method can calculate the system line loss per hour for economic dispatch application by inputting load demands at different buses for the next 24 hours. First, bat algorithm is verified and simulated by the IEEE 10 units test system for economic dispatch problems. Secondly, the benefit of the distributed storage system is taken into consideration under a day ahead of unit commitment. Third, the multiple-objective bat algorithm is used to optimize economic dispatch and load factor by the distributed energy storage systems. Finally, the effects of different renewable energy penetration on power systems are analyzed. The research outcomes of this thesis are value to a microgrid for making more moderately scheduling under the high renewable energy penetration.

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