維持系統頻率的穩定與品質為電力系統運轉上一項重要的課題，台灣電力公司於民國102年在系統中導入CPS1標準藉以評估系統頻率的穩定性。本研究參考歷年研究報告及運轉資料分析台電系統影響CPS1分數的因素，歸納出數個在不同運轉時段下與CPS1分數相關性較高的頻控參數。接著採用廣義迴歸類神經架構建立能反映頻控響應的系統模型。最後將此系統模型與具頻控約束之機組排程進行整合，藉由系統模型與機組排程的相互調整，達成具頻率控制品質之機組排程。

Maintaining frequency stability and quality of the power system is an important operating issue. From the year of 2013, Taiwan Power Company (Taipower) enforced control performance standard 1 (CPS1) to assess frequency stability of the Taipower system. In this thesis, several frequency control factors that highly correlate with CPS1 score under different op-erating periods are identified by analyzing the historical data. Following that, this research adopts General Regression Neural Network (GRNN) to establish the power system model which can reflect frequency control re-sponse. Finally, the developed system model is integrated with unit com-mitment program to come up with CPS1 constraint. By going through the mutual interaction between system model and unit commitment program, the frequency constrained unit commitment can be derived to ensure fre-quency quality.

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