Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Anna Eingartner, Steffi Naumann, Philipp Schmitz and Karl Worthmann ()
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Anna Eingartner: Fraunhofer IOSB-AST: Cognitive Energy Systems, 98693 Ilmenau, Germany
Steffi Naumann: Fraunhofer IOSB-AST: Cognitive Energy Systems, 98693 Ilmenau, Germany
Philipp Schmitz: Optimization-Based Control Group, Institute of Mathematics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
Karl Worthmann: Optimization-Based Control Group, Institute of Mathematics, Technische Universität Ilmenau, 98693 Ilmenau, Germany

Energies, 2024, vol. 17, issue 8, 1-14

Abstract: In this paper, the application of the method of affinely adjustable robust optimization to a planning model of an energy system under uncertain parameters is presented, and the total scheduling costs in comparison with the deterministic model are evaluated. First, the basics of optimization under uncertain data are recapped, and it is described how these methods can be used in different applications for energy systems. This is followed by the methodology of adjustable robust optimization by defining the affinely adjustable robust counterpart. Finally, a numerical case study is conducted to compare the adjustable robust method with a rolling deterministic scheduling method. Both are implemented on a model of an energy system and compared with each other by simulation using real-world data. By calculating the total operating costs for both methods, it can be concluded that the adjustable robust optimization provides a significantly more cost-effective solution to the scheduling problem.

Keywords: adjustable robust optimization; uncertainties; renewable energy; imbalance energy; energy system operation; day-ahead planning (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: 2024
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