Optimal Control of a Dispatchable Energy Source for Wind Energy Management

Guglielmo D’Amico 1 , Filippo Petroni 2 ,  and Robert Adam Sobolewski 3
  • 1 Department of Pharmacy, University “G. D’Annunzio” of Chieti-Pescara, 66013, Chieti, Italy
  • 2 Department of Management, Università Politecnica delle Marche, 60121, Ancona, Italy
  • 3 Department of Power Engineering, Photonics and Lighting Technology, Bialystok University of Technology, Bialystok, Poland
Guglielmo D’Amico, Filippo Petroni
  • Corresponding author
  • Department of Management, Università Politecnica delle Marche, 60121, Ancona, Italy
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and Robert Adam Sobolewski
  • Department of Power Engineering, Photonics and Lighting Technology, Bialystok University of Technology, Bialystok, Poland
  • Email
  • Search for other articles:
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Abstract

The major drawback of wind energy relies in its variability in time, which necessitates specific strategies to be settled. One such strategy can be the coordination of wind power production with a co-located power generation of dispatchable energy source (DES), e.g., thermal power station, combined heat and power plant, gas turbine or compressed air energy storage. In this paper, we consider an energy producer that generates power by means of a wind park and of a DES and sells the produced energy to an isolated grid. We determine the optimal quantity of energy produced by a DES, given the unit cost of this energy, that a power producer should buy and use to hedge against the risk inherent in the production of energy through wind turbines. We determine the optimal quantity by solving a static optimization problem taking into account the possible dependence between the amount of energy produced by wind turbines and electricity prices by using a copula function. Several particular cases are studied that allow the determination of the optimal solution in an analytical closed form. Finally, a numerical example concerning a real 48 MW wind farm located in Poland and Polish Power Exchange shows the possibility of implementing the model in real-life problems.

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