於智慧電網下電能管理是一項重要的工作。藉由實施電能管理之客戶可藉由時間電價以節省電力花費，並降低尖峰負載。電力公司因尖峰負載降低，可延緩電力設備投資、增加電力設備之利用率。
本文提出一家庭電能管理系統，目標在滿足用戶設定之條件下，電費支出最小化。本文提出一改進之粒子群演算法以執行電能管理最佳化，其中家庭電器之電能管理使用負載平移方式，並為獲得較佳解本文所提演算法採用初始化策略、渾沌慣性權重、單形交叉法、子群、修補策略與懲罰因子等技術。
本文為驗證所提方法，模擬住宅區內三種大小之家庭電能管理系統，其負載包括電器、電動車、蓄電池、太陽能板與風力發電機。模擬結果顯示所提演算法能在減少配電變壓器尖峰負載下，同時降低各住戶電力花費。模擬結果也顯示所提方法較現有技術可在相同呼叫適應性函數次數內獲得較佳解。

Energy management is an important function of smart grid. The customers, which implement energy management, can reduce cost through smart price and reduce peak load. Because of peak load reduction, power utility can delay investment of electrical infrastructure and increase utilization of electrical infrastructure.
This thesis presents an optimal energy management system for residential loads, and the objective is to minimize electricity payment when satisfying conditions, which set by customer. This thesis presents an improved PSO (IPSO) to implement energy management optimization. The improved PSO applies load shifting on home applications, and in order to find more optimal solution, the algorithm, which proposed in this thesis, adopts initialization strategy, chaotic inertial weight, simplex crossover operation, subswarms, repair algorithm and penalty factor.
In order to examine the proposed algorithm, simulations were carried out on a residential area which contains some important appliances, EVs, battery, photovoltaic (PV) and wind turbine in three kinds of home energy management systems (HEMS). The simulation results show that the proposed algorithm achieves electricity payment reduction, while reducing the peak demand of the distribution transformer. The simulation results also show that the proposed algorithm compared with existing algorithm can find more optimal solution when number of function evaluations is the same.

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