Application of Multi‐Objective Evolutionary Optimization Algorithms to Reactive Power Planning Problem

Eghbal, Mehdi; Yorino, Naoto; Zoka, Yoshifumi; El-Araby, E.E.

doi:10.1002/tee.20455

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…Therefore, the optimization problem has evolved from a single-objective RPO problem to a multi-objective reactive power optimization (MORPO) problem [5,6]. The MORPO problem is essentially a nonlinear optimization problem with multiple constraints, variables, and objectives [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

Wang,

Li,

Xiao

et al. 2024

Energies

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With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

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