Abstract
The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.
Highlights
A recently developed metaheuristic technique has been investigated in solving the optimal reactive power dispatch in transmission systems, namely the salp swarm algorithm (SSA)
Two objectives have been considered in solving the optimal reactive power dispatch (ORPD) problem, the power loss minimization and the voltage deviations reduction
The ORPD problem is approached as both single- and multi-objective
Summary
Considering the continuous and discrete variables that define the reactive power planning, highly performant algorithms are needed in order to solve the resulting nonlinear optimization problem [3]. In order to deal with the numerous decision variables that define the ORPD problem, the implementation of new advanced methods is required In this regard, recent studies propose the approach of meta-heuristic algorithms, such as genetic algorithms [8], the gravitational search algorithm [9], the differential search algorithm [10] or the moth-flame optimization [11]. The salp swarm algorithm (SSA) is a novel nature-inspired optimizer recommended for solving single and multiple objectives, defined by high convergence and coverage [16] Considering these features, recently, this new metaheuristic technique finds its use in the power systems sector.
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