Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique

Hsin-Wei Chiu, Le-Ren Chang-Chien and Chin-Chung Wu
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Hsin-Wei Chiu: Department of Electrical Engineering, National Cheng Kung University, East District, Tainan City 701, Taiwan
Le-Ren Chang-Chien: Department of Electrical Engineering, National Cheng Kung University, East District, Tainan City 701, Taiwan
Chin-Chung Wu: Taiwan Power Company, Taipei 10016, Taiwan

Energies, 2021, vol. 14, issue 2, 1-19

Abstract: Frequency control is essential to ensure reliability and quality of power systems. North American Electric Reliability Corporation’s (NERC) Control Performance Standard 1 (CPS1) is widely adopted by many operating authorities to examine the quality of the frequency control. The operating authority would have a strong interest in knowing how the frequency-sensitive features affect the CPS1 score and finding out more effective unit-dispatch schedules for reaching the CPS1 goal. As frequency-sensitive features usually possess multi-variable and high-correlated characteristics, this paper employed an ensemble learning technique (the Gradient Boosting Decision Tree algorithm, GBDT) to construct Frequency Response Model (FRM) of the Taipower system in Taiwan to evaluate by CPS1 score. The proposed CPS1 model was then integrated with Unit Commitment (UC) program to determine the unit-dispatch that achieves the targeted CPS1 score. The feasibility and effectiveness of the proposed CPS1-UC platform were validated and compared with the other benchmark model-based UC methods by two operating cases. The proposed model shows promising results: the system frequency could be maintained well, especially in the periods of the early morning or the high renewable penetration.

Keywords: Control Performance Standard 1 (CPS1); frequency control; machine learning; unit commitment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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