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Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources

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Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. / Khan, Inam Ullah; Javaid, Nadeem; Akurugoda Gamage, Kelum et al.
In: IEEE Access, Vol. 8, 14.08.2020, p. 148622-148643.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Khan IU, Javaid N, Akurugoda Gamage K, Taylor CJ, Baig S, Ma X. Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. IEEE Access. 2020 Aug 14;8:148622-148643. doi: 10.1109/ACCESS.2020.3015473

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Khan, Inam Ullah ; Javaid, Nadeem ; Akurugoda Gamage, Kelum et al. / Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. In: IEEE Access. 2020 ; Vol. 8. pp. 148622-148643.

Bibtex

@article{be301618bd234fac99c36b61b24e2879,
title = "Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources",
abstract = "Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction. ",
keywords = "Optimal Power Flow, Renewable Energy Sources, Carbon Emission, Meta-heuristic Techniques, Grey Wolf Optimisation",
author = "Khan, {Inam Ullah} and Nadeem Javaid and {Akurugoda Gamage}, Kelum and Taylor, {C. James} and Sobia Baig and Xiandong Ma",
year = "2020",
month = aug,
day = "14",
doi = "10.1109/ACCESS.2020.3015473",
language = "English",
volume = "8",
pages = "148622--148643",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources

AU - Khan, Inam Ullah

AU - Javaid, Nadeem

AU - Akurugoda Gamage, Kelum

AU - Taylor, C. James

AU - Baig, Sobia

AU - Ma, Xiandong

PY - 2020/8/14

Y1 - 2020/8/14

N2 - Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

AB - Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

KW - Optimal Power Flow

KW - Renewable Energy Sources

KW - Carbon Emission

KW - Meta-heuristic Techniques

KW - Grey Wolf Optimisation

U2 - 10.1109/ACCESS.2020.3015473

DO - 10.1109/ACCESS.2020.3015473

M3 - Journal article

VL - 8

SP - 148622

EP - 148643

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -