微電網系統可整合系統中所有設備，透過對系統進行電力調度，有效分配系統設備於各時段適當的充放電量。本論文提出一種微電網系統架構，系統中包含市電、太陽能發電系統、風力發電機、電池儲能系統、飛輪儲能系統和負載，且僅考慮系統操作在併網模式下。本文將探討系統1：單一電池儲能和系統2：電池-飛輪混合儲能等兩種架構間運轉成本的差異。系統的最佳化目標定義為年運轉總成本，其中為真實考量電池隨使用情況不同所產生的更換成本，本論文亦引入實際電池老化模型，直接將電池老化的狀況等效轉換成其更換成本。
本論文使用最佳化方法對系統進行求解，且為驗證所使用方法的成效，將利用相同的MATLAB模擬平台對本文所提出的流程圖法以及9種曾被使用在電力調度相關研究的方法進行模擬，並對模擬結果中表現最優的方法—人工蜂群演算法加以改良。為避免演算法的隨機性造成模擬結果不公正，本論文選用3項統計方法—弗里德曼檢驗、單因子方差分析以及魏克生符號等級檢定對11種方法的模擬結果進行驗證分析。模擬結果指出，使用混合儲能可有效改善系統年運轉總成本，其中表現最佳者為所提出的改良人工蜂群演算法(Modified Artificial Bee Colony, MABC)，其改善率為5.05%。根據弗里德曼檢驗結果，MABC之表現為所有方法中的第一名；根據單因子方差分析結果，MABC的平均值在系統1中會與6種比較方法存有顯著性差異，而在系統2中則會與7種比較方法存有顯著性差異；根據魏克生符號等級檢定的統計結果，MABC方法在系統1中顯著優於6種方法，而在系統2中則顯著優於其它10種比較方法。

The micro-grid system can integrate all the equipment in the system, it can effectively allocate the appropriate charging and discharging capacity of the system equipment at each time period. This paper proposes a microgrid system architecture, which includes mains power, solar power generation systems, wind turbines, battery energy storage systems, flywheel energy storage systems and loads, and only considers the system operating in grid-connected mode. This article will explore the difference in operating costs between the two architectures of system 1: single battery energy storage and system 2: battery-flywheel hybrid energy storage. The optimization objective of the system is defined as the total annual operating cost. This paper also introduces the actual battery aging model to directly convert the battery aging status into its replacement cost equivalently.
In this paper, MATLAB simulation platform will be used to carry out the proposed flow chart method and 9 optimization methods that have been used in power dispatching related research to solve the system. In order to avoid the unfairness of the simulation results caused by the randomness of the algorithm, this paper uses three statistical methods—Friedman test, ANOVA and Wilcoxon signed-rank test to verify and analyze the simulation results of 11 methods. The simulation results indicate that the use of hybrid energy storage can effectively improve the total annual operating cost of the system, and the proposed modified artificial bee colony algorithm has the best performance, with an improvement rate of 5.05%. According to the results of Friedman test, the performance of MABC ranks first among all methods. According to the results of ANOVA, the average value of MABC significantly outperforms other 6 and 7 methods in system 1 and system 2, respectively. According to the statistical results of Wilcoxon signed-rank test, the MABC method is significantly better than other 6 and 10 methods in system 1 and system 2, respectively.

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