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

Keivan Shariatmadar, Adriano Arrigo, François Vallée, Hans Hallez, Lieven Vandevelde and David Moens
Additional contact information
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
David Moens: LMSD Campus De Nayer, KU Leuven, B-2860 Sint-Katelijne-Waver, Belgium

Energies, 2021, vol. 14, issue 4, 1-19

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.

Keywords: energy and reserve dispatch; imprecise uncertainty; maximinity and maximality; optimal decision (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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