Optimal power flow based upon genetic algorithm deploying optimum mutation and elitism

Aslam, Muhammad; Cheema, Muhammad Usman; Samran, Muhammad; Cheema, Muhammad Bilal

doi:10.1109/icitacee.2014.7065767

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Cited by 2 publications

References 13 publications

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Comparison of Genetic Algorithm and Particle Swarm Optimization for DC Optimal Power Flow

Azam,

Ahmad,

Altaf

et al. 2023

2023 25th International Multitopic Conference (INMIC)

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Comparison of Genetic Algorithm and Particle Swarm Optimization for DC Optimal Power Flow

Azam,

Ahmad,

Altaf

et al. 2023

2023 25th International Multitopic Conference (INMIC)

View full text Add to dashboard Cite

An intelligent alternating current-optimal power flow for reduction of pollutant gases with incorporation of variable generation resources

Lalljith

Swanson

Goudarzi

2020

J. energy South. Afr.

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Frequent escalations in fuel costs, environmental concerns, and the depletion of non-renewable fuel reserves have driven the power industry to significant utilisation of renewable energy resources. These resources cannot satisfy the entire system load demand because of the intermittent nature of variable generation resources (VGRs) such as wind and solar. Therefore, there is a need to optimally schedule the generating units (thermal and VGRs) to reduce the amount of fuel used and the level of emissions produced. In this study, an AC-power flow in conjunction with combined economic and environmental dispatch approach through the implementation of a modified constricted coefficient particle swarm optimisation was used to minimise the fuel cost and the level of emission gases produced. The approach was applied to the Institute of Electric and Electronic Engineers 30 bus test system through three different load conditions: base-load, increase-load and critical-load. The results showed the practicality of the proposed approach for the simultaneous reduction of the total generation cost and emission levels on a large electrical power grid while maintaining all the physical and operational constraints of the system.

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Optimal power flow based upon genetic algorithm deploying optimum mutation and elitism

Cited by 2 publications

References 13 publications

Comparison of Genetic Algorithm and Particle Swarm Optimization for DC Optimal Power Flow

Comparison of Genetic Algorithm and Particle Swarm Optimization for DC Optimal Power Flow

An intelligent alternating current-optimal power flow for reduction of pollutant gases with incorporation of variable generation resources

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