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Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

Author

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  • Pingping Yun

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China)

  • Yongfeng Ren

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China
    Inner Mongolia Engineering Research Center for Wind Power Technology and Testing, Hohhot 010051, Inner Mongolia Autonomous Region, China)

  • Yu Xue

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia Autonomous Region, China)

Abstract

Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3393-:d:187673
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    Cited by:

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    2. 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.
    3. Eleonora Achiluzzi & Kirushaanth Kobikrishna & Abenayan Sivabalan & Carlos Sabillon & Bala Venkatesh, 2020. "Optimal Asset Planning for Prosumers Considering Energy Storage and Photovoltaic (PV) Units: A Stochastic Approach," Energies, MDPI, vol. 13(7), pages 1-20, April.
    4. Jae Woong Shim & Heejin Kim & Kyeon Hur, 2019. "Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency," Energies, MDPI, vol. 12(7), pages 1-13, March.
    5. Mandisi Gwabavu & Atanda Raji, 2021. "Dynamic Control of Integrated Wind Farm Battery Energy Storage Systems for Grid Connection," Sustainability, MDPI, vol. 13(6), pages 1-27, March.
    6. Nikita Dmitrievich Senchilo & Denis Anatolievich Ustinov, 2021. "Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption," Energies, MDPI, vol. 14(21), pages 1-25, October.
    7. Daniel Gutierrez-Reina & Federico Barrero & Jose Riveros & Ignacio Gonzalez-Prieto & Sergio L. Toral & Mario J. Duran, 2019. "Interest and Applicability of Meta-Heuristic Algorithms in the Electrical Parameter Identification of Multiphase Machines," Energies, MDPI, vol. 12(2), pages 1-15, January.

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