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Optimal power flow solution with uncertain RES using augmented grey wolf optimization

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Optimal power flow solution with uncertain RES using augmented grey wolf optimization. / Khan, Inam Ullah; Javaid, Nadeem; Akurugoda Gamage, Kelum; Taylor, C. James; Ma, Xiandong.

2020 IEEE International Conference on Power Systems Technology (POWERCON). IEEE, 2020. (IEEE International Conference on Power Systems Technology (POWERCON)).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Khan, IU, Javaid, N, Akurugoda Gamage, K, Taylor, CJ & Ma, X 2020, Optimal power flow solution with uncertain RES using augmented grey wolf optimization. in 2020 IEEE International Conference on Power Systems Technology (POWERCON). IEEE International Conference on Power Systems Technology (POWERCON), IEEE. https://doi.org/10.1109/POWERCON48463.2020.9230659

APA

Khan, I. U., Javaid, N., Akurugoda Gamage, K., Taylor, C. J., & Ma, X. (2020). Optimal power flow solution with uncertain RES using augmented grey wolf optimization. In 2020 IEEE International Conference on Power Systems Technology (POWERCON) (IEEE International Conference on Power Systems Technology (POWERCON)). IEEE. https://doi.org/10.1109/POWERCON48463.2020.9230659

Vancouver

Khan IU, Javaid N, Akurugoda Gamage K, Taylor CJ, Ma X. Optimal power flow solution with uncertain RES using augmented grey wolf optimization. In 2020 IEEE International Conference on Power Systems Technology (POWERCON). IEEE. 2020. (IEEE International Conference on Power Systems Technology (POWERCON)). https://doi.org/10.1109/POWERCON48463.2020.9230659

Author

Khan, Inam Ullah ; Javaid, Nadeem ; Akurugoda Gamage, Kelum ; Taylor, C. James ; Ma, Xiandong. / Optimal power flow solution with uncertain RES using augmented grey wolf optimization. 2020 IEEE International Conference on Power Systems Technology (POWERCON). IEEE, 2020. (IEEE International Conference on Power Systems Technology (POWERCON)).

Bibtex

@inproceedings{b38edb6f8fa24968a085c6e59c6d7b66,
title = "Optimal power flow solution with uncertain RES using augmented grey wolf optimization",
abstract = "This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.",
keywords = "Optimal power flow, Renewable energy sources, Carbon emission, Meta-heuristic techniques, Grey wolf optimization",
author = "Khan, {Inam Ullah} and Nadeem Javaid and {Akurugoda Gamage}, Kelum and Taylor, {C. James} and Xiandong Ma",
note = "{\textcopyright}2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2020",
month = nov,
day = "2",
doi = "10.1109/POWERCON48463.2020.9230659",
language = "English",
isbn = "9781728163512",
series = "IEEE International Conference on Power Systems Technology (POWERCON)",
publisher = "IEEE",
booktitle = "2020 IEEE International Conference on Power Systems Technology (POWERCON)",

}

RIS

TY - GEN

T1 - Optimal power flow solution with uncertain RES using augmented grey wolf optimization

AU - Khan, Inam Ullah

AU - Javaid, Nadeem

AU - Akurugoda Gamage, Kelum

AU - Taylor, C. James

AU - Ma, Xiandong

N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2020/11/2

Y1 - 2020/11/2

N2 - This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.

AB - This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.

KW - Optimal power flow

KW - Renewable energy sources

KW - Carbon emission

KW - Meta-heuristic techniques

KW - Grey wolf optimization

U2 - 10.1109/POWERCON48463.2020.9230659

DO - 10.1109/POWERCON48463.2020.9230659

M3 - Conference contribution/Paper

SN - 9781728163512

T3 - IEEE International Conference on Power Systems Technology (POWERCON)

BT - 2020 IEEE International Conference on Power Systems Technology (POWERCON)

PB - IEEE

ER -