Bilevel optimization for joint scheduling of production and energy systems

• Published in: Optimization and engineering 2023-03, Vol.24 (1), p.499-537
• Main Authors: Leenders, Ludger, Hagedorn, Dörthe Franzisca, Djelassi, Hatim, Bardow, André, Mitsos, Alexander
• Format: Article
• Language: English
• Subjects: Algorithms; Case studies; Control; Coupling; Energy; Engineering; Environmental Management; Financial Engineering; Independent variables; Mathematics; Mathematics and Statistics; Microbalances; Mixed integer; Onsite; Operations Research/Decision Theory; Optimization; Production scheduling; Research Article; Schedules; Scheduling; Systems Theory
• ISSN: 1389-4420; 1573-2924
• DOI: 10.1007/s11081-021-09694-0

Energy-intensive production sites are often supplied with energy by on-site energy systems. Commonly, the scheduling of the systems is performed sequentially, starting with the scheduling of the production system. Often, the on-site energy system is operated by a different company than the production system. In consequence, the production and the energy system schedule their operation towards misaligned objectives leading in general to suboptimal schedules for both systems. To reflect the independent optimization with misaligned objectives, the scheduling problem of the production system can be formulated as a bilevel problem. We formulate the bilevel problem with mixed-integer decision variables in the upper and the lower level, and propose an algorithm to solve this bilevel problem based on the deterministic and global algorithm by Djelassi, Glass and Mitsos (J Glob Optim 75:341–392, 2019. https://doi.org/10.1007/s10898-019-00764-3) for bilevel problems with coupling equality constraints. The algorithm works by discretizing the independent lower-level variables. In the scheduling problem considered herein, the only coupling equality constraints are energy balances in the lower level. Since an intuitive distinction is missing between dependent and independent variables, we specialize the algorithm and add a procedure to identify independent variables to be discretized. Thereby, we preserve convergence guarantees. The performance of the algorithm is demonstrated in two case studies. In the case studies, the production system favors different technologies for the energy supply than the energy system. By solving the bilevel problem, the production system identifies an energy demand, which leads to minimal cost. Additionally, we demonstrate the benefits of solving the bilevel problem instead of solving the common integrated or sequential problem.