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Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

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  • Nhung Nguyen Hong

    (Graduate School of Environment and Energy Engineering, Waseda University, Tokyo 169-8050, Japan)

  • Yosuke Nakanishi

    (Graduate School of Environment and Energy Engineering, Waseda University, Tokyo 169-8050, Japan)

Abstract

Nowadays, the hybrid wind–diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithm—a combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

Suggested Citation

  • Nhung Nguyen Hong & Yosuke Nakanishi, 2019. "Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error," Energies, MDPI, vol. 12(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:843-:d:210840
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    References listed on IDEAS

    as
    1. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    2. B. K. Pagnoncelli & S. Ahmed & A. Shapiro, 2009. "Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications," Journal of Optimization Theory and Applications, Springer, vol. 142(2), pages 399-416, August.
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    Cited by:

    1. Yang Yang & Chong Lian & Chao Ma & Yusheng Zhang, 2019. "Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode," Energies, MDPI, vol. 13(1), pages 1-20, December.
    2. Sijia Tu & Bingda Zhang & Xianglong Jin, 2019. "Research on DFIG-ES System to Enhance the Fast-Frequency Response Capability of Wind Farms," Energies, MDPI, vol. 12(18), pages 1-20, September.
    3. Carlos A. Platero & José A. Sánchez & Christophe Nicolet & Philippe Allenbach, 2019. "Hydropower Plants Frequency Regulation Depending on Upper Reservoir Water Level," Energies, MDPI, vol. 12(9), pages 1-15, April.

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