A Hybrid Genetic Algorithm Applied to the Transmission Network Expansion Planning Considering Non-conventional Solution Candidates

Jaime Andrés López  López; Jesús María López-Lezama; Nicolás  Muñoz-Galeano

doi:10.6180/jase.201909_22(3).0018

A Hybrid Genetic Algorithm Applied to the Transmission Network Expansion Planning Considering Non-conventional Solution Candidates

Computer Science and Information Engineering

Jaime Andrés López López¹, Jesús María López-Lezama This email address is being protected from spambots. You need JavaScript enabled to view it.² and Nicolás Muñoz-Galeano²

¹XM S.A. E.S.P., Calle 12 Sur No. 18 – 168, Medellín, Colombia
²Grupo de Investigación en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 67 No 53-108, Medellín, Colombia

Received: February 20, 2019
Accepted: May 2, 2019
Publication Date: September 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201909_22(3).0018

ABSTRACT

This paper presents a metaheuristic approach to solve the transmission network expansion planning (TNEP) problem considering non-conventional solution candidates. The TNEP consists on finding the set of new elements required in a power system to meet a given future demand at a minimum cost. The TNEP traditionally considers as candidate solutions the addition of new lines and transformers. The main contribution of this work is the inclusion of non-conventional solution candidates. Such non-conventional solution candidates are namely: repowering of existing circuits and reactive shunt compensation. Also, an AC modeling of the network that allows obtaining more realistic results than the traditional DC model has been considered. The TNEP is represented by means of a non linear mixed integer programming problem which is solved through a hybrid genetic algorithm (HGA). Several tests were performed on two benchmark power systems to show the applicability and effectiveness of the proposed approach.

Keywords: Transmission Network Expansion Planning, Non-conventional Solution Candidates, Genetic Algorithms, Greedy Randomized Search Procedure

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