Miranda, Felipe L., Oliveira, Leonardo W., Oliveira, Edimar J., Nepomuceno, Erivelton and Dias, Bruno H. (2023) Multi-objective transmission expansion planning based on Pareto dominance and neural networks. Electric Power Systems Research, 214. p. 108864. ISSN 03787796
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Abstract
This paper presents an algorithm to solve the multi-objective transmission expansion planning (TEP) problem including the investment and reliability criteria. The reliability is considered by using the Expected energy not supplied (EENS) index. The main contribution consists on handling the reliability criterion in the optimization process, which tends to provide solutions with better trade-off between the mentioned criteria. For that purpose, a novel probabilistic algorithm called non-dominated Monte Carlo simulation (ND-MCS) is proposed to allow solving the multi-objective TEP problem with suitable computational effort and efficacy even considering the probabilistic feature of reliability in the optimization. In addition, a Support Vector Machine (SVM) network is applied embedded within the ND-MCS. The proposed methodology integrates the Pareto dominance method as a convergence criterion to MCS and a fuzzy criterion to support the decision making. The effectiveness of the proposed approach is tested in three systems, including a practical Brazilian network.
Item Type: | Article |
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Keywords: | Transmission expansion planning; Multi-objective optimization; Reliability; Monte Carlo simulation; Neural network; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 20258 |
Identification Number: | 10.1016/j.epsr.2022.108864 |
Depositing User: | Erivelton Nepomuceno |
Date Deposited: | 14 Jul 2025 15:36 |
Journal or Publication Title: | Electric Power Systems Research |
Publisher: | Elsevier |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/20258 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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