Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

Walter Gil-González, Oscar Danilo Montoya, Luis Fernando Grisales-Noreña, Alberto-Jesus Perea-Moreno and Quetzalcoatl Hernandez-Escobedo
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Walter Gil-González: Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia
Oscar Danilo Montoya: Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia
Luis Fernando Grisales-Noreña: Grupo GIIEN, Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, Colombia
Alberto-Jesus Perea-Moreno: Departamento de Física Aplicada, Universidad de Córdoba, ceiA3, Campus de Rabanales, 14071 Córdoba, Spain
Quetzalcoatl Hernandez-Escobedo: Escuela Nacional de Estudios Superiores, Campus Juriquilla, UNAM, Queretaro 3001, Mexico

Sustainability, 2020, vol. 12, issue 7, 1-20

Abstract: This paper presents an optimization model for the optimal placement and sizing of wind turbines, considering their reactive power capacity, wind speed, and demand curves. The optimization model is nonlinear and is focused on minimizing power losses in AC distribution networks. Also, paired wind turbine and power conversion systems are treated via chargeability factor η at the peak hour. This factor represents the percentage of usage of the power conversion system in the nominal wind speed conditions, and allows to support reactive power dynamically during all periods of the day as a function of the distribution system requirements. In addition, an artificial neural network is used for short-term forecasting to deal with uncertainties in wind power generation. We assume that the number of wind power distributed generators could be from zero to three generators integrated into the system, considering unit power factors and reactive power injections to follow up the effect of reactive power compensation in the daily operation. The General Algebraic Modeling System (GAMS) is employed to solve the proposed optimization model.

Keywords: wind power generation; artificial neural networks; chargeability factor; reactive power capacity; wind speed and demand curves (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:7:p:2983-:d:343006

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