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Real-Time Flexibility Assessment for Power Systems with High Wind Energy Penetration

Author

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  • Anna Glazunova

    (Department of Electric Power System, Melentiev Energy Systems Institute SB RAS, 664033 Irkutsk, Russia)

  • Evgenii Semshikov

    (School of Engineering, College of Science and Engineering, University of Tasmania, Hobart 7000, Australia)

  • Michael Negnevitsky

    (School of Engineering, College of Science and Engineering, University of Tasmania, Hobart 7000, Australia)

Abstract

To reduce the reliance on fossil fuel-based generation, many countries expand the use of renewable energy sources (RES) for electricity production. The stochastic and intermittent nature of such sources (i.e., wind and solar) poses challenges to the stable and reliable operation of the electric power system (EPS) and requires sufficient operational flexibility. With continuous and random changes in the EPS operational conditions, evaluating the system flexibility in a standardized manner may improve the robustness of planning and operating procedures. Therefore, the development of fast algorithms for determining system flexibility is a critical issue. In this paper, the flexibility of the EPS with high wind energy penetration is calculated in real time. In this context, the EPS flexibility is understood as the ability of the system to maintain a balance under irregular and short-term active power variations during a specified time by using the flexibility resources. The EPS flexibility calculation relies on a deterministic method developed to qualitatively and quantitatively assess the EPS readiness to changes in load. Accurate wind power forecasts and the observance of the electric circuit law when solving the optimization problem allow for determining the actual value of the EPS flexibility during a considered time.

Suggested Citation

  • Anna Glazunova & Evgenii Semshikov & Michael Negnevitsky, 2021. "Real-Time Flexibility Assessment for Power Systems with High Wind Energy Penetration," Mathematics, MDPI, vol. 9(17), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2056-:d:622276
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    References listed on IDEAS

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    1. Rostislav Krč & Martina Kratochvílová & Jan Podroužek & Tomáš Apeltauer & Václav Stupka & Tomáš Pitner, 2021. "Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    2. Khorshidi, Reza & Shabaninia, Faridon & Niknam, Taher, 2016. "A new smart approach for state estimation of distribution grids considering renewable energy sources," Energy, Elsevier, vol. 94(C), pages 29-37.
    3. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    4. Kavousi-Fard, Abdollah & Abunasri, Alireza & Zare, Alireza & Hoseinzadeh, Rasool, 2014. "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, Elsevier, vol. 78(C), pages 904-915.
    5. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
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