為了因應未來高再生能源佔比導致傳統機組減少的情況下，系統慣量變低以及系統等效機組調速機頻率響應能力下降，一旦發生事故容易造成電網系統的衝擊，影響頻率穩定。為了避免系統頻率快速下降並觸發低頻電驛卸載，本研究主要探討如何評估快速反應備轉輔助服務需求量為主軸，藉由統計歷史事故之機組出力資料線性擬合化二元一次方程式去推測出當下機組排程之系統等效機組調速機頻率響應係數。另外，使用系統總裝置容量、系統淨負載量、再生能源量及系統等效機組調速機頻率響應係數等變數納入監督式學習之GBR(Gradient Boosting Regressor)模型，由此模型預估當日系統負載阻尼係數。獲得前述兩參數即可使用搖擺方程式(Swing Equation)去計算所需之快速反應備轉輔助服務需求量。所有預測結果皆透會PSSE輸電系統衝擊分析軟體證明此方法挽救頻率快速掉落之有效性。

High renewable penetration into the power system could reduce the participation of traditional units in the unit commitment, which results in a lower system inertia and less equivalent speed-governor response. Therefore, system frequency would be vulnerable to any contingent event, which is easy to trigger underfrequency load shedding. To avoid rapid frequency fall, this thesis proposes a method to quantify the demand of the fast response reserve ancillary service. Firstly, the coefficients of the linear equation are fitted into the governor response curve using the unit output data retrieved from the historical accidents. Secondly, system load damping is estimated using the GBR (Gradient Boosting Regressor) model. The model makes use of several system variables including system generation capacity, net load, and renewable energy output to train the model using supervised learning. As the aforementioned two parameters are available, the swing equation can be used to calculate the demand of the fast response reserve. The effectiveness of the proposed methodology is validated by the PSSE simulation software.

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