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Data-driven real-time power dispatch for maximizing variable renewable generation

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  • Li, Zhigang
  • Qiu, Feng
  • Wang, Jianhui

Abstract

Traditional power dispatch methods have difficulties in accommodating large-scale variable renewable generation (VRG) and have resulted in unnecessary VRG spillage in the practical industry. The recent dispatchable-interval-based methods have the potential to reduce VRG curtailment, but the dispatchable intervals are not allocated effectively due to the lack of exploiting historical dispatch records of VRG units. To bridge this gap, this paper proposes a novel data-driven real-time dispatch approach to maximize VRG utilization by using do-not-exceed (DNE) limits. This approach defines the maximum generation output ranges that the system can accommodate without compromising reliability. The DNE limits of VRG units and operating base points of conventional units are co-optimized by hybrid stochastic and robust optimization, and the decision models are formulated as mixed-integer linear programs by the sample average approximation technique exploiting historical VRG data. A strategy for selecting historical data samples is also proposed to capture the VRG uncertainty more accurately under variant prediction output levels. Computational experiments show the effectiveness of the proposed methods.

Suggested Citation

  • Li, Zhigang & Qiu, Feng & Wang, Jianhui, 2016. "Data-driven real-time power dispatch for maximizing variable renewable generation," Applied Energy, Elsevier, vol. 170(C), pages 304-313.
    • Handle: RePEc:eee:appene:v:170:y:2016:i:c:p:304-313
    DOI: 10.1016/j.apenergy.2016.02.125
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    1. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    2. Liu, Heping & Erdem, Ergin & Shi, Jing, 2011. "Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed," Applied Energy, Elsevier, vol. 88(3), pages 724-732, March.
    3. MILLER, Andrew J. & WOLSEY, Laurence A., 2003. "Tight formulations for some simple mixed integer programs and convex objective integer programs," LIDAM Reprints CORE 1653, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Wang, Jiadong & Wang, Jianhui & Liu, Cong & Ruiz, Juan P., 2013. "Stochastic unit commitment with sub-hourly dispatch constraints," Applied Energy, Elsevier, vol. 105(C), pages 418-422.
    5. Zheng, J.H. & Chen, J.J. & Wu, Q.H. & Jing, Z.X., 2015. "Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs embedded," Applied Energy, Elsevier, vol. 154(C), pages 369-379.
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    12. Erfan Mohagheghi & Aouss Gabash & Pu Li, 2017. "A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration," Energies, MDPI, vol. 10(4), pages 1-28, April.
    13. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Frede Blaabjerg & Pu Li, 2020. "Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems," Energies, MDPI, vol. 13(7), pages 1-17, April.
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