Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • Universidade Estadual Paulista Júlio de Mesquita Filho


There is increasing evidence of the shortage of solver-based models for solving logically-constrained AC optimal power flow problem (LCOPF). Although in the literature the heuristic-based models have been widely used to handle the LCOPF problems with logical terms such as conditional statements, logical-and, logical-or, etc., their requirement of several trials and adjustments plagues finding a trustworthy solution. On the other hand, a well-defined solver-based model is of much interest in practice, due to rapidity and precision in finding an optimal solution. To remedy this shortcoming, in this paper we provide a solver-friendly procedure to recast the logical constraints to solver-based mixed-integer nonlinear programming (MINLP) terms. We specifically investigate the recasting of logical constraints into the terms of the objective function, so it facilitates the pre-solving and probing techniques of commercial solvers and consequently results in a higher computational efficiency. By applying this recast method to the problem, two sub-power- and sub-function-based MINLP models, namely SP-MINLP and SF-MINLP, respectively, are proposed. Results not only show the superiority of the proposed models in finding a better optimal solution, compared to the existing approaches in the literature, but also the effectiveness and computational tractability in solving large-scale power systems under different configurations.


Original languageEnglish
Pages (from-to)240-249
Number of pages10
JournalInternational Journal of Electrical Power and Energy Systems
Publication statusPublished - 1 Apr 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • FACTS devices, Logical constraint, Mixed-integer nonlinear programming, Non-smooth terms, Optimal power flow, Solver-based model

ID: 16968459