Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach

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@article{7656a06d970141938f6a9306a84dc9ff,
title = "Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach",
abstract = "This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.",
keywords = "Monte-Carlo simulation (MCS), Renewable energy resources (RERs), Self-adaptive probabilistic mutation operator (SAPMO), Transmission expansion planning (TEP)",
author = "Ehsan Naderi and Mahdi Pourakbari-Kasmaei and Matti Lehtonen",
year = "2020",
month = "2",
day = "1",
doi = "10.1016/j.ijepes.2019.105460",
language = "English",
volume = "115",
journal = "International Journal of Electrical Power and Energy Systems",
issn = "0142-0615",
publisher = "Elsevier Limited",

}

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TY - JOUR

T1 - Transmission expansion planning integrated with wind farms

T2 - A review, comparative study, and a novel profound search approach

AU - Naderi, Ehsan

AU - Pourakbari-Kasmaei, Mahdi

AU - Lehtonen, Matti

PY - 2020/2/1

Y1 - 2020/2/1

N2 - This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.

AB - This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.

KW - Monte-Carlo simulation (MCS)

KW - Renewable energy resources (RERs)

KW - Self-adaptive probabilistic mutation operator (SAPMO)

KW - Transmission expansion planning (TEP)

UR - http://www.scopus.com/inward/record.url?scp=85070216112&partnerID=8YFLogxK

U2 - 10.1016/j.ijepes.2019.105460

DO - 10.1016/j.ijepes.2019.105460

M3 - Article

VL - 115

JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

SN - 0142-0615

M1 - 105460

ER -

ID: 36428217