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Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length

Author

Listed:
  • Xiaoming Liu

    (Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250061, China)

  • Liang Wang

    (State Grid Shandong Electric Power Company, Jinan 250001, China)

  • Yongji Cao

    (Academy of Intelligent Innovation, Shandong University, Jinan 250101, China
    Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China)

  • Ruicong Ma

    (Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China)

  • Yao Wang

    (Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250061, China)

  • Changgang Li

    (Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China)

  • Rui Liu

    (Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250061, China)

  • Shihao Zou

    (Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China)

Abstract

Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a renewable scenario generation method based on the hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) is used to divide the original data into linear and fluctuant parts according to the length of time scales. The HGAVCL is designed to optimally divide the linear part into different time sections. Additionally, each time section is described by the autoregressive integrated moving average (ARIMA) model. With the consideration of temporal correlation, the Copula joint probability density function is established to model the fluctuant part. Based on the attained ARIMA model and joint probability density function, a number of data are generated by the Monte Carlo method, and the time autocorrelation, average offset rate, and climbing similarity indexes are established to assess the data quality of generated scenarios. A case study is conducted to verify the effectiveness of the proposed approach. The calculated time autocorrelation, average offset rate, and climbing similarity are 0.0515, 0.0396, and 0.9035, respectively, which shows the superior performance of the proposed approach.

Suggested Citation

  • Xiaoming Liu & Liang Wang & Yongji Cao & Ruicong Ma & Yao Wang & Changgang Li & Rui Liu & Shihao Zou, 2023. "Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length," Energies, MDPI, vol. 16(7), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3180-:d:1113263
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    References listed on IDEAS

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    1. Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
    2. Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
    3. Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando, 2022. "A diversity-based genetic algorithm for scenario generation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1128-1141.
    4. Ekata Kaushik & Vivek Prakash & Om Prakash Mahela & Baseem Khan & Almoataz Y. Abdelaziz & Junhee Hong & Zong Woo Geem, 2022. "Optimal Placement of Renewable Energy Generators Using Grid-Oriented Genetic Algorithm for Loss Reduction and Flexibility Improvement," Energies, MDPI, vol. 15(5), pages 1-20, March.
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    Cited by:

    1. Haoling Min & Pinkun He & Chunlai Li & Libin Yang & Feng Xiao, 2024. "The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function," Energies, MDPI, vol. 17(5), pages 1-13, February.

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