Embedding quasi-static time series within a genetic algorithm for stochastic optimization: the case of reactive power compensation on distribution systems

Lujano-Rojas, J.M. (Universidad de Zaragoza) ; Zubi, G. ; Dufo-Lopez, R. (Universidad de Zaragoza) ; Bernal-Agustin, J.L. (Universidad de Zaragoza) ; Atencio-Guerra, J.L. ; Catalao, J.P.S.

Resumen: This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required.

Idioma: Inglés

DOI: 10.1093/jcde/qwaa016

Año: 2020

Publicado en: Journal of computational design and engineering 7, 2 (2020), 177-194
ISSN: 2288-4300

Factor impacto JCR: 5.86 (2020)

Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 9 / 90 = 0.1 (2020) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 16 / 111 = 0.144 (2020) - Q1 - T1

Factor impacto SCIMAGO: 0.764 - Computational Mathematics (Q1) - Computational Mechanics (Q1) - Modeling and Simulation (Q1) - Engineering (miscellaneous) (Q1) - Human-Computer Interaction (Q1) - Computer Graphics and Computer-Aided Design (Q1)

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)