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Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control

Author

Listed:
  • Deyou Yang

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

  • Jiaxin Wen

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

  • Ka-wing Chan

    (Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong, China)

  • Guowei Cai

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

Abstract

The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS) based on model predictive control (MPC). The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm.

Suggested Citation

  • Deyou Yang & Jiaxin Wen & Ka-wing Chan & Guowei Cai, 2016. "Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control," Energies, MDPI, vol. 9(8), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:629-:d:75799
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    References listed on IDEAS

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    1. Kou, Peng & Gao, Feng & Guan, Xiaohong, 2015. "Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts," Renewable Energy, Elsevier, vol. 80(C), pages 286-300.
    2. Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim, 2015. "A Flywheel Energy Storage System Based on a Doubly Fed Induction Machine and Battery for Microgrid Control," Energies, MDPI, vol. 8(6), pages 1-16, June.
    3. Tran Thai Trung & Seon-Ju Ahn & Joon-Ho Choi & Seok-Il Go & Soon-Ryul Nam, 2014. "Real-Time Wavelet-Based Coordinated Control of Hybrid Energy Storage Systems for Denoising and Flattening Wind Power Output," Energies, MDPI, vol. 7(10), pages 1-25, October.
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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. Zhe Jiang & Xueshan Han & Zhimin Li & Wenbo Li & Mengxia Wang & Mingqiang Wang, 2016. "Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station," Energies, MDPI, vol. 9(11), pages 1-17, October.
    3. 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.
    4. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.

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