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

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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)

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.

Suggested Citation

  • Hsin-Wei Chiu & Le-Ren Chang-Chien & Chin-Chung Wu, 2021. "Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique," Energies, MDPI, vol. 14(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:310-:d:476848
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    References listed on IDEAS

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    1. Takashi Mitani & Muhammad Aziz & Takuya Oda & Atsuki Uetsuji & Yoko Watanabe & Takao Kashiwagi, 2017. "Annual Assessment of Large-Scale Introduction of Renewable Energy: Modeling of Unit Commitment Schedule for Thermal Power Generators and Pumped Storages," Energies, MDPI, vol. 10(6), pages 1-19, May.
    2. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Yu-Qing Bao & Yang Li & Beibei Wang & Minqiang Hu & Yanmin Zhou, 2017. "Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource," Energies, MDPI, vol. 10(1), pages 1-16, January.
    4. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    5. Felipe Pérez-Illanes & Eduardo Álvarez-Miranda & Claudia Rahmann & Camilo Campos-Valdés, 2016. "Robust Unit Commitment Including Frequency Stability Constraints," Energies, MDPI, vol. 9(11), pages 1-16, November.
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