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Analytical uncertainty propagation for multi-period stochastic optimal power flow

Bauer, Rebecca 1; Mühlpfordt, Tillmann 2; Ludwig, Nicole; Hagenmeyer, Veit 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract:

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000153716
Veröffentlicht am 20.12.2022
Originalveröffentlichung
DOI: 10.1016/j.segan.2022.100969
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 2352-4677
KITopen-ID: 1000153716
HGF-Programm 37.12.02 (POF IV, LK 01) Design,Operation & Digitalization of the Future Energy Grids
Erschienen in Sustainable Energy, Grids and Networks
Verlag Elsevier
Band 33
Seiten Art.-Nr.: 100969
Vorab online veröffentlicht am 26.11.2022
Schlagwörter Optimal power flow, Gaussian uncertainty, Distributed storage, Affine policies, Transmission network
Nachgewiesen in Web of Science
Dimensions
Scopus
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