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Data-Driven Distributionally Robust Stochastic Control of Energy Storage for Wind Power Ramp Management Using the Wasserstein Metric

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  • Insoon Yang

    (Department of Electrical and Computer Engineering, Automation and Systems Research Institute, Seoul National University, Seoul 08826, Korea)

Abstract

The integration of wind energy into the power grid is challenging because of its variability, which causes high ramp events that may threaten the reliability and efficiency of power systems. In this paper, we propose a novel distributionally robust solution to wind power ramp management using energy storage. The proposed storage operation strategy minimizes the expected ramp penalty under the worst-case wind power ramp distribution in the Wasserstein ambiguity set , a statistical ball centered at an empirical distribution obtained from historical data. Thus, the resulting distributionally robust control policy presents a robust ramp management performance even when the future wind power ramp distribution deviates from the empirical distribution, unlike the standard stochastic optimal control method. For a tractable numerical solution, a duality-based dynamic programming algorithm is designed with a piecewise linear approximation of the optimal value function. The performance and utility of the proposed method are demonstrated and analyzed through case studies using the wind power data in the Bonneville Power Administration area for the year 2018.

Suggested Citation

  • Insoon Yang, 2019. "Data-Driven Distributionally Robust Stochastic Control of Energy Storage for Wind Power Ramp Management Using the Wasserstein Metric," Energies, MDPI, vol. 12(23), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4577-:d:292833
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    References listed on IDEAS

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    1. Lopez, Marco & Still, Georg, 2007. "Semi-infinite programming," European Journal of Operational Research, Elsevier, vol. 180(2), pages 491-518, July.
    2. Jae Ho Kim & Warren B. Powell, 2011. "Optimal Energy Commitments with Storage and Intermittent Supply," Operations Research, INFORMS, vol. 59(6), pages 1347-1360, December.
    3. Huan Xu & Shie Mannor, 2012. "Distributionally Robust Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 37(2), pages 288-300, May.
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    Cited by:

    1. Gengli Song & Hua Wei, 2022. "Distributionally Robust Multi-Energy Dynamic Optimal Power Flow Considering Water Spillage with Wasserstein Metric," Energies, MDPI, vol. 15(11), pages 1-18, May.
    2. Yiling Zhang & Jin Dong, 2022. "Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1531-1547, May.
    3. Wang, Xuejie & Li, Bingkang & Wang, Yuwei & Lu, Hao & Zhao, Huiru & Xue, Wanlei, 2022. "A bargaining game-based profit allocation method for the wind-hydrogen-storage combined system," Applied Energy, Elsevier, vol. 310(C).

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