Journal Article

FZJ-2020-02285

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices

Schäfer, P.RWTH* ; Schweidtmann, A. M.RWTH* ; Lenz, P. H. A.RWTH* ; Markgraf, H. M. C.RWTH* ; Mitsos, A. (Corresponding author)FZJ*RWTH*

2020
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/25071  doi:10.1016/j.compchemeng.2019.106598

Abstract: Using nonlinear process models in discrete-time scheduling typically prohibits long planning horizons with fine temporal discretizations. Therefore, we propose an adaptive grid algorithm tailored for scheduling subject to time-variable electricity prices. The scheduling problem is formulated in a reduced space. In the algorithm, the number of degrees of freedom is reduced by linearly mapping one degree of freedom to multiple intervals with similar electricity prices. The mapping is iteratively refined using a wavelet-based analysis of the previous solution. We apply the algorithm to the scheduling of a compressed air energy storage. We model the efficiency characteristics of the turbo machinery using artificial neural networks. Using our in-house global solver MAiNGO, the algorithm identifies a feasible near-optimal solution with  < 1% deviation in the objective value within  < 5% of the computational time compared to a solution considering the full dimensionality.

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Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 899 - ohne Topic (POF3-899) (POF3-899)

Appears in the scientific report 2020

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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