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Optimal Real-Time Scheduling of Wind Integrated Power System Presented with Storage and Wind Forecast Uncertainties

Author

Listed:
  • Yuchong Huo

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ping Jiang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yuan Zhu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Shuang Feng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xi Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

The volatility of wind power poses great challenges to the operation of power systems. This paper deals with the economic dispatch problems presented by energy storage in wind integrated systems. A policy iteration algorithm for deriving the cost optimal policy of real-time scheduling is proposed, taking the effect of wind forecast uncertainties into account. First, energy loss and use of fast-ramping generation are selected as the performance metrics. Then, a policy iteration algorithm is developed using the Perturbed Markov decision process. This algorithm has a two-level optimization structure in which both the long-term and short-term behaviors of real-time scheduling policy are optimized. In addition, a unified optimal storage control strategy is presented. The feasibility of the proposed methodology is demonstrated via the wind power archive of Electric Reliability Council of Texas (ERCOT). Through comparative numerical experiments, both the performance of the policy iteration algorithm in the short-term and long-term are verified and the consistency, robustness, good convergence and high computational efficiency of the proposed algorithm are also corroborated.

Suggested Citation

  • Yuchong Huo & Ping Jiang & Yuan Zhu & Shuang Feng & Xi Wu, 2015. "Optimal Real-Time Scheduling of Wind Integrated Power System Presented with Storage and Wind Forecast Uncertainties," Energies, MDPI, vol. 8(2), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:1080-1100:d:45364
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    Citations

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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. Yonggang Li & Yue Wang & Binyuan Wu, 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    3. Ning Li & Fuxing He & Wentao Ma, 2019. "Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p -Power Error Loss," Energies, MDPI, vol. 12(4), pages 1-19, February.
    4. Xiuyun Wang & Jian Wang & Biyuan Tian & Yang Cui & Yu Zhao, 2018. "Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints," Energies, MDPI, vol. 11(4), pages 1-19, April.
    5. Boyang Qu & Baihao Qiao & Yongsheng Zhu & Jingjing Liang & Ling Wang, 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(12), pages 1-28, December.
    6. Yi Tang & Yuqian Liu & Jia Ning & Jingbo Zhao, 2017. "Multi-Time Scale Coordinated Scheduling Strategy with Distributed Power Flow Controllers for Minimizing Wind Power Spillage," Energies, MDPI, vol. 10(11), pages 1-15, November.
    7. Hong-Chao Gao & Joon-Ho Choi & Sang-Yun Yun & Hak-Ju Lee & Seon-Ju Ahn, 2018. "Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty," Energies, MDPI, vol. 11(6), pages 1-15, May.

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