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Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty

Author

Listed:
  • Keivan Shariatmadar

    (M-Group Campus Bruges, KU Leuven, B-8200 Bruges, Belgium)

  • Adriano Arrigo

    (Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium)

  • François Vallée

    (Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium)

  • Hans Hallez

    (DistriNet Campus Bruges, KU Leuven, B-8200 Bruges, Belgium)

  • Lieven Vandevelde

    (Department of Electromechanical, Systems and Metal Engineering, Ghent University, B-9052 Ghent, Belgium
    FlandersMake@UGent—Corelab EEDT-DC, Flanders Make, B-9052 Ghent, Belgium)

  • David Moens

    (LMSD Campus De Nayer, KU Leuven, B-2860 Sint-Katelijne-Waver, Belgium)

Abstract

The current energy transition and the underlying growth in variable and uncertain renewable-based energy generation challenge the proper operation of power systems. Classical probabilistic uncertainty models, e.g., stochastic programming or robust optimisation, have been used widely to solve problems such as the day-ahead energy and reserve dispatch problem to enhance the day-ahead decisions with a probabilistic insight of renewable energy generation in real-time. By doing so, the scheduling of the power system becomes, production and consumption of electric power, more reliable (i.e., more robust because of potential deviations) while minimising the social costs given potential balancing actions. Nevertheless, these classical models are not valid when the uncertainty is imprecise, meaning that the system operator may not rely on a unique distribution function to describe the uncertainty. Given the Distributionally Robust Optimisation method, our approach can be implemented for any non-probabilistic, e.g., interval models rather than only sets of distribution functions (ambiguity set of probability distributions). In this paper, the aim is to apply two advanced non-probabilistic uncertainty models: Interval and ϵ -contamination, where the imprecision and in-determinism in the uncertainty (uncertain parameters) are considered. We propose two kinds of theoretical solutions under two decision criteria—Maximinity and Maximality. For an illustration of our solutions, we apply our proposed approach to a case study inspired by the 24-node IEEE reliability test system.

Suggested Citation

  • Keivan Shariatmadar & Adriano Arrigo & François Vallée & Hans Hallez & Lieven Vandevelde & David Moens, 2021. "Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty," Energies, MDPI, vol. 14(4), pages 1-19, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1016-:d:499860
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    References listed on IDEAS

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    3. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    4. Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2007. "Probabilities in Economic Modeling," PIER Working Paper Archive 07-023, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Ramsey, Frank P., 1926. "Truth and Probability," Histoy of Economic Thought Chapters, in: Braithwaite, R. B. (ed.),The Foundations of Mathematics and other Logical Essays, chapter 7, pages 156-198, McMaster University Archive for the History of Economic Thought.
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    1. Diego Larrahondo & Ricardo Moreno & Harold R. Chamorro & Francisco Gonzalez-Longatt, 2021. "Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power," Energies, MDPI, vol. 14(15), pages 1-15, July.
    2. Shubo Hu & Zhengnan Gao & Jing Wu & Yangyang Ge & Jiajue Li & Lianyong Zhang & Jinsong Liu & Hui Sun, 2022. "Time-Interval-Varying Optimal Power Dispatch Strategy Based on Net Load Time-Series Characteristics," Energies, MDPI, vol. 15(4), pages 1-23, February.

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