Gil-González, WalterMontoya, Oscar DaniloGrisales-Noreña, Luis FernandoPerea-Moreno, Alberto-JesusHernandez-Escobedo, Quetzalcoatl2020-10-302020-10-302020-04-082020-10-28Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Perea-Moreno, A.-J.; Hernandez-Escobedo, Q. Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves. Sustainability 2020, 12, 2983.https://hdl.handle.net/20.500.12585/9521This 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.20 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/https://www.mdpi.com/2071-1050/12/7/2983info:eu-repo/semantics/article10.3390/su12072983Wind power generationArtificial neural networksChargeability factorReactive power capacityWind speed and demand curvesinfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de Bolívar