Multiobjective security-constrained optimal reactive dispatch with enhanced sinusoidal discretization

Optimal Reactive Dispatch (ORD) with security constraints is a nonlinear optimization problem used to assess the impacts of predefined sets of contingencies in power systems. Its resolution helps operators identify secure and stable operating conditions. Additionally, the problem involves determining the optimal settings of control components to achieve an improved system configuration. When more complex modeling is considered – incorporating multiple objectives and discrete decision variables – most existing approaches struggle to find good feasible solutions, particularly as the number of contingencies increases. Our contribution to this multi-objective mixed-integer nonlinear programming (MINLP) problem is a solution method based on Evolutionary Particle Swarm Optimization (EPSO), enhanced with a sinusoidal discretization function. With classical PSO-based methods, the consideration of discrete variables usually implies adapting scalar operations to operate with discrete variables, which degrades the original PSO algorithm. However, the proposed application of the sinusoidal function allows for accurate modeling of discrete variables used to coordinate reactive power sources, while simultaneously minimizing active power losses and generator reactive power outputs (i.e., increasing reactive margins) without losing accuracy and convergence speed. We conducted exhaustive tests on the IEEE-118 and IEEE-300 bus systems. These included a sensitivity analysis on the most influential parameters to demonstrate the effectiveness and robustness of the proposed multi-objective model in delivering reliable trade-off solutions that satisfy both technical and safety requirements across different objectives. Comparisons with other known metaheuristic algorithms have also been done, which confirmed the good performance of the proposed method in terms of resolution time and solution quality.