Transmission Expansion Planning - A broad comparison between static and dynamic approaches

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019-09-17

Autores

João Tomé Saraiva (Autor) FEUP; Luiz Eduardo Oliveira (Autor) FEUP; Phillipe Vilaça (Autor) FEUP

Ata de Conferência Internacional

Título: 16th IEEE International Conference on the European Energy Market

Páginas: 1-5

Indexação

ISI Web of Science

Scopus

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrotécnica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

DOI: 10.1109/EEM.2019.8916359

Abstract (EN): The Transmission Expansion Planning (TEP) problem aims at identifying a list of equipment, among transmission lines, cables and transformers, that will be installed on the grid over a predefined planning horizon. TEP must also identify the respective periods of installations of the selected pieces-of-equipment to expand the grid. TEP is a mixed-integer non-linear and non-convex problem that requires a huge computational effort to be solved and, for this reason, many authors have been proposing relaxed TEP versions to reduce the mentioned computational burden. In this direction, this paper presents a broad comparison between the relaxed static TEP approach and the complete dynamic TEP. Numerical simulations are conducted using the Garver-6-bus and the RTS-24-bus test systems. The problem is handled by a hybrid tool composed by the Evolutionary Particle Swarm optimization (EPSO) algorithm and a version of Hill-Climbing (HC), besides an AC-optimal power flow model is used to get more realistic operation conditions of the network. Even though dynamic TEP approaches present a higher computational effort, the results show that with this solution approach it is possible to obtain relevant investment savings when compared with the static TEP approach. © 2019 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5