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

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Razi University

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.

Details

Original languageEnglish
Article number105460
Number of pages22
JournalInternational Journal of Electrical Power and Energy Systems
Volume115
Publication statusPublished - 1 Feb 2020
MoE publication typeA1 Journal article-refereed

    Research areas

  • Monte-Carlo simulation (MCS), Renewable energy resources (RERs), Self-adaptive probabilistic mutation operator (SAPMO), Transmission expansion planning (TEP)

ID: 36428217