Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices

• Published in: Computers & chemical engineering 2020-01, Vol.132, p.106598, Article 106598
• Main Authors: Schäfer, Pascal, Schweidtmann, Artur M., Lenz, Philipp H.A., Markgraf, Hannah M.C., Mitsos, Alexander
• Format: Article
• Language: English
• Subjects: Adaptive refinement; Artificial neural networks; Discrete-time scheduling; Global optimization; Reduced-space
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2019.106598

•Generic reduced-space scheduling problem with time-variable electricity prices.•Linear mapping procedure assigning one degree of freedom to multiple time intervals.•Wavelet-based analysis of previous solution to iteratively refine the assignment.•Balance between improved objective values and reduced dimensionalities.•Feasible near-optimal solutions furnished within short time. [Display omitted] 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