Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)

Ahmed Alshahir; Richard Molyet

doi:doi:10.11648/j.epes.20211001.12

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Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)

Ahmed Alshahir, Richard Molyet

Published in American Journal of Electrical Power and Energy Systems (Volume 10, Issue 1)

Received: 25 January 2021 Accepted: 2 February 2021 Published: 10 February 2021

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Abstract

Renewable Energy Sources (RESs) have been growing continuously until they become the second source of electricity after coal. However, most of RESs have intermittent nature of electricity production due to the high dependency on some external conditions like weather which changes seasonally. This intermittent nature has a negative impact on security and stability, voltage profile, and increasing the power losses in radial distribution power networks which contain uncertain power sources. Therefore, this paper presents a novel technique based on Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO). The goal of utilizing the GA is to track the maximum power point of uncertain power sources such as Solar/Photovoltaic (PV) and Wind Turbine (WT). Then, PSO starts its execution to determine the optimum configuration of power networks in order to minimize the power losses, maintain voltage profile, and increase the overall system stability and security. Different test cases are considered for testing different operation conditions. The simulation work has implemented by using MATLAB 2016b software. The results are tested on standard IEEE 33 bus systems and validated with other conventional method to verify the correctness of the proposed technique. Results show a significant improvement in voltage profile, reduction in the power losses, and hence increment in the overall system stability and security.

Published in	American Journal of Electrical Power and Energy Systems (Volume 10, Issue 1)
DOI	10.11648/j.epes.20211001.12
Page(s)	6-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Conventional Energy Sources (CESs), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Radial Distribution Systems (RDSs), Network Reconfiguration

References

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APA Style

Ahmed Alshahir, Richard Molyet. (2021). Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). American Journal of Electrical Power and Energy Systems, 10(1), 6-14. https://doi.org/10.11648/j.epes.20211001.12

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ACS Style

Ahmed Alshahir; Richard Molyet. Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). Am. J. Electr. Power Energy Syst. 2021, 10(1), 6-14. doi: 10.11648/j.epes.20211001.12

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AMA Style

Ahmed Alshahir, Richard Molyet. Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). Am J Electr Power Energy Syst. 2021;10(1):6-14. doi: 10.11648/j.epes.20211001.12

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@article{10.11648/j.epes.20211001.12,
  author = {Ahmed Alshahir and Richard Molyet},
  title = {Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {10},
  number = {1},
  pages = {6-14},
  doi = {10.11648/j.epes.20211001.12},
  url = {https://doi.org/10.11648/j.epes.20211001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20211001.12},
  abstract = {Renewable Energy Sources (RESs) have been growing continuously until they become the second source of electricity after coal. However, most of RESs have intermittent nature of electricity production due to the high dependency on some external conditions like weather which changes seasonally. This intermittent nature has a negative impact on security and stability, voltage profile, and increasing the power losses in radial distribution power networks which contain uncertain power sources. Therefore, this paper presents a novel technique based on Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO). The goal of utilizing the GA is to track the maximum power point of uncertain power sources such as Solar/Photovoltaic (PV) and Wind Turbine (WT). Then, PSO starts its execution to determine the optimum configuration of power networks in order to minimize the power losses, maintain voltage profile, and increase the overall system stability and security. Different test cases are considered for testing different operation conditions. The simulation work has implemented by using MATLAB 2016b software. The results are tested on standard IEEE 33 bus systems and validated with other conventional method to verify the correctness of the proposed technique. Results show a significant improvement in voltage profile, reduction in the power losses, and hence increment in the overall system stability and security.},
 year = {2021}
}

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TY  - JOUR
T1  - Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)
AU  - Ahmed Alshahir
AU  - Richard Molyet
Y1  - 2021/02/10
PY  - 2021
N1  - https://doi.org/10.11648/j.epes.20211001.12
DO  - 10.11648/j.epes.20211001.12
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 6
EP  - 14
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20211001.12
AB  - Renewable Energy Sources (RESs) have been growing continuously until they become the second source of electricity after coal. However, most of RESs have intermittent nature of electricity production due to the high dependency on some external conditions like weather which changes seasonally. This intermittent nature has a negative impact on security and stability, voltage profile, and increasing the power losses in radial distribution power networks which contain uncertain power sources. Therefore, this paper presents a novel technique based on Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO). The goal of utilizing the GA is to track the maximum power point of uncertain power sources such as Solar/Photovoltaic (PV) and Wind Turbine (WT). Then, PSO starts its execution to determine the optimum configuration of power networks in order to minimize the power losses, maintain voltage profile, and increase the overall system stability and security. Different test cases are considered for testing different operation conditions. The simulation work has implemented by using MATLAB 2016b software. The results are tested on standard IEEE 33 bus systems and validated with other conventional method to verify the correctness of the proposed technique. Results show a significant improvement in voltage profile, reduction in the power losses, and hence increment in the overall system stability and security.
VL  - 10
IS  - 1
ER  -

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Author Information

Ahmed Alshahir

Electrical Engineering and Computer Science Department, University of Toledo, Ohio, USA
Richard Molyet

Electrical Engineering and Computer Science Department, University of Toledo, Ohio, USA

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APA Style

Ahmed Alshahir, Richard Molyet. (2021). Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). American Journal of Electrical Power and Energy Systems, 10(1), 6-14. https://doi.org/10.11648/j.epes.20211001.12

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ACS Style

Ahmed Alshahir; Richard Molyet. Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). Am. J. Electr. Power Energy Syst. 2021, 10(1), 6-14. doi: 10.11648/j.epes.20211001.12

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AMA Style

Ahmed Alshahir, Richard Molyet. Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). Am J Electr Power Energy Syst. 2021;10(1):6-14. doi: 10.11648/j.epes.20211001.12

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@article{10.11648/j.epes.20211001.12,
  author = {Ahmed Alshahir and Richard Molyet},
  title = {Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {10},
  number = {1},
  pages = {6-14},
  doi = {10.11648/j.epes.20211001.12},
  url = {https://doi.org/10.11648/j.epes.20211001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20211001.12},
  abstract = {Renewable Energy Sources (RESs) have been growing continuously until they become the second source of electricity after coal. However, most of RESs have intermittent nature of electricity production due to the high dependency on some external conditions like weather which changes seasonally. This intermittent nature has a negative impact on security and stability, voltage profile, and increasing the power losses in radial distribution power networks which contain uncertain power sources. Therefore, this paper presents a novel technique based on Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO). The goal of utilizing the GA is to track the maximum power point of uncertain power sources such as Solar/Photovoltaic (PV) and Wind Turbine (WT). Then, PSO starts its execution to determine the optimum configuration of power networks in order to minimize the power losses, maintain voltage profile, and increase the overall system stability and security. Different test cases are considered for testing different operation conditions. The simulation work has implemented by using MATLAB 2016b software. The results are tested on standard IEEE 33 bus systems and validated with other conventional method to verify the correctness of the proposed technique. Results show a significant improvement in voltage profile, reduction in the power losses, and hence increment in the overall system stability and security.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Improving the Reconfiguration of Hybrid Power Networks by Combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO)
AU  - Ahmed Alshahir
AU  - Richard Molyet
Y1  - 2021/02/10
PY  - 2021
N1  - https://doi.org/10.11648/j.epes.20211001.12
DO  - 10.11648/j.epes.20211001.12
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 6
EP  - 14
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20211001.12
AB  - Renewable Energy Sources (RESs) have been growing continuously until they become the second source of electricity after coal. However, most of RESs have intermittent nature of electricity production due to the high dependency on some external conditions like weather which changes seasonally. This intermittent nature has a negative impact on security and stability, voltage profile, and increasing the power losses in radial distribution power networks which contain uncertain power sources. Therefore, this paper presents a novel technique based on Genetic Algorithm (GA) combined with Particle Swarm Optimization (PSO). The goal of utilizing the GA is to track the maximum power point of uncertain power sources such as Solar/Photovoltaic (PV) and Wind Turbine (WT). Then, PSO starts its execution to determine the optimum configuration of power networks in order to minimize the power losses, maintain voltage profile, and increase the overall system stability and security. Different test cases are considered for testing different operation conditions. The simulation work has implemented by using MATLAB 2016b software. The results are tested on standard IEEE 33 bus systems and validated with other conventional method to verify the correctness of the proposed technique. Results show a significant improvement in voltage profile, reduction in the power losses, and hence increment in the overall system stability and security.
VL  - 10
IS  - 1
ER  -

Copy | Download