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Distributed Active Power Optimal Dispatching of Wind Farm Cluster Considering Wind Power Uncertainty

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  • Peizhao Hong

    (College of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Zhijun Qin

    (College of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

With the large-scale volatility and uncertainty of the centralized grid connection of wind power, the dimensionality disaster problem of wind farm cluster (WFC) scheduling optimization calculation becomes more and more significant. In view of these challenges, a distributed active power optimal dispatch model for WFC based on the alternating direction multiplier method (ADMM) is proposed, and the analytical description of the distribution characteristics of the active power output of wind farms is introduced into the proposed model. Firstly, based on the wake effect, the Weibull distribution of wind speed is transformed by the impulse function to establish an analytical model of the active output distribution of the wind farm. Secondly, the optimization goal is to minimize the expected sum of the deviations of the dispatch instructions and the output probability density function of each wind farm, constructing a WFC active power optimal dispatch model considering uncertainty. Finally, the proposed model is decoupled in space and time into sub-optimization problems, and the ADMM is improved to construct an efficient solution algorithm that can iterate in parallel and decouple a large number of decision variables at the same time. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–7% reduction in dispatched power shortfalls and a 4–21% improvement in wind power utilization. The optimization algorithm for model construction has extremely high computational efficiency and good convergence. The results show that when the update step size is three, the convergence is the fastest, and when the update step size is six, the convergence is the slowest; in addition, when the number of wind farms is greater than eight, the distributed computing efficiency has an incomparable advantage over the centralized one. It plays a helpful role in wind power consumption and the efficient calculation of the power grid and effectively improves the reliability of the power grid.

Suggested Citation

  • Peizhao Hong & Zhijun Qin, 2022. "Distributed Active Power Optimal Dispatching of Wind Farm Cluster Considering Wind Power Uncertainty," Energies, MDPI, vol. 15(7), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2706-:d:788326
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    References listed on IDEAS

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    1. Jianqiu Shi & Yubao Liu & Yang Li & Yuewei Liu & Gregory Roux & Lan Shi & Xiaowei Fan, 2022. "Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts," Energies, MDPI, vol. 15(3), pages 1-18, January.
    2. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    3. Ebrahimi, F.M. & Khayatiyan, A. & Farjah, E., 2016. "A novel optimizing power control strategy for centralized wind farm control system," Renewable Energy, Elsevier, vol. 86(C), pages 399-408.
    4. Saeedreza Jadidi & Hamed Badihi & Youmin Zhang, 2021. "Fault-Tolerant Cooperative Control of Large-Scale Wind Farms and Wind Farm Clusters," Energies, MDPI, vol. 14(21), pages 1-29, November.
    5. Zhichang Liang & Haixiao Liu, 2022. "Layout Optimization of a Modular Floating Wind Farm Based on the Full-Field Wake Model," Energies, MDPI, vol. 15(3), pages 1-15, January.
    6. Jayasekara, Saliya & Halgamuge, Saman K. & Attalage, Rahula A. & Rajarathne, Rohitha, 2014. "Optimum sizing and tracking of combined cooling heating and power systems for bulk energy consumers," Applied Energy, Elsevier, vol. 118(C), pages 124-134.
    7. Yongqian Liu & Yanhui Qiao & Shuang Han & Yanping Xu & Tianxiang Geng & Tiandong Ma, 2021. "Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid," Energies, MDPI, vol. 14(16), pages 1-14, August.
    8. Fang, Xin & Hodge, Bri-Mathias & Jiang, Huaiguang & Zhang, Yingchen, 2019. "Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow," Applied Energy, Elsevier, vol. 239(C), pages 938-947.
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    1. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.

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