本研究以風力發電為微型電網之再生能源，在考慮併聯電網的情況下，供應含電動車及儲能電池組的家庭負載，特別針對一般用戶將電動車於夜間充電的習慣，在以購電成本最低的目標下進行電能調度最佳化。
利用Matlab內建的基因演算法，在已知時間電價、預測風力發電量與負載的條件下，提出一種近似權重分配的概念於目標函數上，期待以權重參數的大小來影響充放電的優先順序以達到預期的充放電效果及最小成本。藉由不同案例的模擬，除驗證此法之可行性，同時也求得最佳的參數設定值。
為了使最佳化函數更貼近真實狀況，故以預先調度好的結果為基礎，再加上預測的誤差項於最佳化問題中，最後將預測誤差考量在模擬中，驗證了此法確實更能反應出真實狀況。

This research focuses on the power dispatch optimization for achieving minimal electricity cost under a utility-grid connected microgrid scheme which includes wind power generator, household load, electric vehicles (EVs), and battery energy storage system (BESS). The operating principle is to make balance between generation and demand while EV loads are active during the nighttime.

Based on the preknowledge of time-of-use (TOU) tariff as well as forecasts of wind and load, a weighting factor concept is applied to the objective function to prioritize the behaviors of power charging and discharging for achieving the desired condition at minimal cost. This optimization problem is solved by genetic algorithm (GA) written in MATLAB. Various simulation scenarios are conducted not only to validate the effectiveness of the proposed method but also to survey the best weighting values in the objective function.

In order to make the optimization problem be more practical, considering a prediction error item is supplemented to the optimization constraints. Simulation considering the prediction error is conducted and the result shows that the proposed optimization function could respond closer to the real conditions.

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