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Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction

Author

Listed:
  • Li Han

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Rongchang Zhang

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Xuesong Wang

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Yu Dong

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

This paper looks at the ability to cope with the uncertainty of wind power and reduce the impact of wind power forecast error (WPFE) on the operation and dispatch of power system. Therefore, several factors which are related to WPFE will be studied. By statistical analysis of the historical data, an indicator of real-time error based on these factors is obtained to estimate WPFE. Based on the real-time estimation of WPFE, a multi-time scale rolling dispatch model for wind/storage power system is established. In the real-time error compensation section of this model, the previous dispatch plan of thermal power unit is revised according to the estimation of WPFE. As the regulating capacity of thermal power unit within a short time period is limited, the estimation of WPFE is further compensated by using battery energy storage system. This can not only decrease the risk caused by the wind power uncertainty and lessen wind spillage, but also reduce the total cost. Thereby providing a new method to describe and model wind power uncertainty, and providing economic, safe and energy-saving dispatch plan for power system. The analysis in case study verifies the effectiveness of the proposed model.

Suggested Citation

  • Li Han & Rongchang Zhang & Xuesong Wang & Yu Dong, 2018. "Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction," Energies, MDPI, vol. 11(8), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2124-:d:163827
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    References listed on IDEAS

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    Cited by:

    1. Pingping Yun & Yongfeng Ren & Yu Xue, 2018. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method," Energies, MDPI, vol. 11(12), pages 1-23, December.
    2. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
    3. Gejirifu De & Zhongfu Tan & Menglu Li & Liling Huang & Xueying Song, 2018. "Two-Stage Stochastic Optimization for the Strategic Bidding of a Generation Company Considering Wind Power Uncertainty," Energies, MDPI, vol. 11(12), pages 1-21, December.
    4. Weidong Li & Tie Li & Haixin Wang & Jian Dong & Yunlu Li & Dai Cui & Weichun Ge & Junyou Yang & Martin Onyeka Okoye, 2019. "Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response," Energies, MDPI, vol. 12(5), pages 1-18, March.

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