Transmission network expansion planning considering weighted transmission loading relief nodal indexes

  • Sergio D. Saldarriaga-Zuluaga Institución Universitaria Pascual Bravo
  • Jesús M. López-Lezama Universidad de Antioquia
  • Fernando Villada-Duque Universidad de Antioquia
Keywords: Genetic algorithms, security constraints, transmission network expansion planning

Abstract

This paper presents a model and a solution approach for the transmission network expansion planning (TNEP) problem that integrates security constraints given by weighted transmission loading relief (WTLR) indexes. Such indexes integrate shift and power distribution factors and allow to measure the severity of overloads in normal conditions and under any single contingency. Furthermore, the inclusion of small-scale generation was considered as complementary to TNEP solutions. The proposed model was solved by means of the metaheuristic NSGA-II (Non-dominated Sorting Genetic Algorithm II), which enabled to find a set of solutions that represent a trade-off between the cost of the expansion plan and its security level. Several tests were performed on the 6-bus Garver system and the IEEE 24-bus reliability test system, thus showing the applicability of the proposed approach. It was found that the inclusion of small-scale generation in strategic nodes allows to reduce the cost of expansion plans and increases their level of security for single contingencies.

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Author Biographies

Sergio D. Saldarriaga-Zuluaga, Institución Universitaria Pascual Bravo

MSc. in Engineering, Facultad de Ingeniería

Jesús M. López-Lezama, Universidad de Antioquia

PhD. in Electrical Engineering, Departamento de Ingeniería Eléctrica

Fernando Villada-Duque, Universidad de Antioquia

PhD. in Electrical Engineering , Departamento de Ingeniería Eléctrica

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How to Cite
Saldarriaga-Zuluaga, S., López-Lezama, J., & Villada-Duque, F. (2018, May 14). Transmission network expansion planning considering weighted transmission loading relief nodal indexes. TecnoLógicas, 21(42), 31-50. https://doi.org/10.22430/22565337.777
Published
2018-05-14
Section
Research Papers