MURAL - Maynooth University Research Archive Library



    Multi-objective transmission expansion planning based on Pareto dominance and neural networks


    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

    [thumbnail of EN_multi.pdf]
    Preview
    Text
    EN_multi.pdf
    Available under License Creative Commons Attribution Non-commercial Share Alike.

    Download (728kB) | Preview

    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
    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

    Repository Staff Only (login required)

    Item control page
    Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads