本研究之目標為針對智慧家庭，發展整合太陽能發電、市電及儲能系統之微電網系統，透過設計多能源系統最佳化能量管理技術(Energy Management System, EMS)，適當調度各電源之間之功率流向，並對儲能系統進行必要之儲能與釋能，以降低整體用電成本。在此研究中，首先發展基於規則控制策略(Rule-Based Control Strategy, RBCS )於微電網系統中，以達到節省電能消耗、降低碳排放量與減少用戶電費支出等目的。然而，由於實際微電網系統在運作時，家用負載、太陽能發電功率、儲電量與即時電價等各項數值均會隨時間變化而縝密變動，且RBCS之切換條件無法兼顧所有可能性。因此，為提高整體用電成本最小化之目標，本研究進一步以最小等效能耗策略(Equivalent Consumption Minimization Strategy, ECMS) 設計多能源之電能管理策略，因應不同再生能源發電量、即時電價與負載需求進行功率分配最佳化，將能源更有效率地使用，進而達到電價最小化之目標。礙於最小等效能耗策略搜尋時間過於冗長，最終提出適應性人工蜂群演算法(Adaptive Artificial Bee Colony Algorithm, AABC)設計多能源之電能管理策略來降低搜索時間，實驗結果表明以月計電費夏日時段為例，使用AABC之控制策略比RBCS之控制策略能省下9.8%的電費；使用ECMS之控制策略比RBCS之控制策略一個月能省下11.2%的電費。

In the light of this, the objective of this thesis is to develop a smart home which is based on photovoltaic, mains supply and battery in three kinds of microgrid. This thesis presents a Rule-Based Control Strategy (RBCS) to conserve electricity and reduce carbon emissions of Home Energy Management System (HEMS). Nevertheless, considering about the real load, solar power and electricity rates situation will be different with estimation. Consequently, the objective is to minimize electricity payment when satisfying conditions. Further more, the standard way to solve this kind of problem is Equivalent Consumption Minimization Strategy (ECMS) and Adaptive Artificial Bee Colony Algorithm (AABC).Simulation result demonstrate RBCS electricity rates is 9.8% worth more than EMS(AABC) for a month. Then RBCS electricity rates is 11.2% worth more than EMS (ECMS) for a month.

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