Abstract
The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.
Highlights
Optimal reactive power dispatch (ORPD) is an important problem in power system operation.The economy of grid operation has two main aspects to consider: active (Watt) and reactive (Var) power control problems
The significant contribution of this paper is to develop a new hybridization of two naturally-inspired metaheuristics techniques, particle swarm optimization (PSO) and artificial physics optimization (APO), solve and optimize the complexity and nonlinearity of the ORPD problem and test it on various IEEE test systems to evaluate its search capacity
The hybrid APOPSO algorithm developed in this work aims to individually and simultaneously minimize three main objectives in the ORPD problem subjected to equality and inequality constraints, namely, minimizing total active power loss, reducing voltage deviations, and improving voltage stability index
Summary
Optimal reactive power dispatch (ORPD) is an important problem in power system operation. ORPD is considered a pivotal problem in this manner, which aims to solve highly constrained, nonconvex, and nonlinear optimization problems that possess both discrete and continuous control variables to achieve important goals, such as minimizing active power losses and voltage deviations, while improving the voltage stability index of the grid These types of operational issues emerge due to the complexity arising in grid modernization. Metaheuristic approaches allow abstract-level description that provide non-specificity, which is useful for solving a wide range of problems that are presided over by the metaheuristics’ upper-level strategies which influence greater search capabilities They are based on utilizing search capabilities, mainly embodied as a form of memory, to be re-evaluated by successive iterations to steer their search process.
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