Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm

This study introduces a multi-criteria optimization approach using the Grey Wolf Optimizer (GWO) algorithm to determine the optimal capacity, locations of DG units and capacitor banks, and network reconfiguration in distribution systems. The objective function incorporates six key performance metrics: real losses, imaginary losses, voltage variation, voltage stability index, section load ability, and current balancing index. The proposed GWO technique was evaluated on IEEE 33- and 69-bus systems and benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results demonstrate that GWO effectively optimizes the placement and sizing of DGs and capacitor banks under diverse conditions, enhancing system performance and reducing active power losses.

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Publication Details

Journal
International Journal of Electrical and Electronics Research
Published
2024-12-30
DOI
https://doi.org/10.37391/ijeer.120445
Primary Topic
Microgrid Control and Optimization
Type
article
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article

Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm

P. Sujatha, S. Suresh Reddy, D. Mahesh Kumar
International Journal of Electrical and Electronics Research
Microgrid Control and Optimization
article

Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm

P. Sujatha, S. Suresh Reddy, D. Mahesh Kumar
article en

Abstract

This study introduces a multi-criteria optimization approach using the Grey Wolf Optimizer (GWO) algorithm to determine the optimal capacity, locations of DG units and capacitor banks, and network reconfiguration in distribution systems. The objective function incorporates six key performance metrics: real losses, imaginary losses, voltage variation, voltage stability index, section load ability, and current balancing index. The proposed GWO technique was evaluated on IEEE 33- and 69-bus systems and benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results demonstrate that GWO effectively optimizes the placement and sizing of DGs and capacitor banks under diverse conditions, enhancing system performance and reducing active power losses.

International Journal of Electrical and Electronics ResearchVol. 12(4)
Narayana Dental College and Hospital (IN), Jawaharlal Nehru Technological University Anantapur (IN)
Affordable and clean energy
Openalex Percentile: Top 70%
Microgrid Control and Optimization
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Optimal DG and Capacitor Allocation Along with Network Reconfiguration Using Grey Wolf Optimizer Algorithm — P. Sujatha, S. Suresh Reddy, et al. · International Journal of Electrical and Electronics Research (2024) | TGRS Research Map | TGRS