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Transmission expansion planning integrated with wind farms: A review, comparative study, and a novel profound search approach
- Source :
- International Journal of Electrical Power & Energy Systems. 115:105460
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
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.
- Subjects :
- Mathematical optimization
Wind power
Optimization problem
Computer science
business.industry
Heuristic (computer science)
020209 energy
020208 electrical & electronic engineering
Evolutionary algorithm
Energy Engineering and Power Technology
Particle swarm optimization
02 engineering and technology
Hybrid algorithm
Electric power system
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 01420615
- Volume :
- 115
- Database :
- OpenAIRE
- Journal :
- International Journal of Electrical Power & Energy Systems
- Accession number :
- edsair.doi...........73bf445bd6dfd483efdf105c1cf1e114