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Two-Stage Optimization Research of Power System with Wind Power Considering Energy Storage Peak Regulation and Frequency Regulation Function

Author

Listed:
  • Juan Li

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Hongxu Zhang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value.

Suggested Citation

  • Juan Li & Hongxu Zhang, 2025. "Two-Stage Optimization Research of Power System with Wind Power Considering Energy Storage Peak Regulation and Frequency Regulation Function," Energies, MDPI, vol. 18(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4947-:d:1751579
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    References listed on IDEAS

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    1. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    2. Wu, Xiong & Li, Nailiang & He, Mingkang & Wang, Xiuli & Ma, Song & Cao, Jingjing, 2022. "Risk-constrained day-ahead scheduling for gravity energy storage system and wind turbine based on IGDT," Renewable Energy, Elsevier, vol. 185(C), pages 904-915.
    3. Chun-Yao Lee & Maickel Tuegeh, 2020. "An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem," Energies, MDPI, vol. 13(14), pages 1-16, July.
    4. Hao Yu & Yibo Wang & Chuang Liu & Shunjiang Wang & Chunyang Hao & Jian Xiong, 2024. "Optimization and Scheduling Method for Power Systems Considering Wind Power Forward/Reverse Peaking Scenarios," Energies, MDPI, vol. 17(5), pages 1-18, March.
    5. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
    6. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    7. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
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