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This research study proposes a methodology based on data science to estimate the technical feasibility of implementing hybrid solar-wind power systems by calculating the plant factor, also known as capacity factor (CF). Data is gathered from the NOVALYNX 110-WS-18 weather station, between the years 2018 and 2020, to determine the energetic potential available for the Candelaria campus at the La Salle University (Colombia). A k-means data clustering algorithm, developed in Matlab, is applied over the following descriptors: time, solar radiation, wind speed, and temperature. The results show that the photovoltaic and wind power systems have a CF of 36.69% and 2.15%, respectively. It is concluded that it is technically feasible to implement a photovoltaic system at the La Salle University, but not a wind power system as it does not meet the minimum CF required. [ABSTRACT FROM AUTHOR]

This paper describes a linear programming model and Monte Carlo simulation which solve a middle-termed hydrothermal dispatch problem for three different scenarios of wind power penetration. Transmission line effects are neglected. The fuel cost in thermal plants varies in each time period. The test system has two thermal and three hydroelectric plants that can continuously operate during the twelve-months planning term. Wind generation is modeled by using Weibull Distribution Functions particular to the Colombian system, while hydro-generation is modeled by Normal Distribution Functions. Results show costs reduction for the three scenarios of wind power penetration. The main contribution of this paper is to simultaneously introduce the stochastic effect of wind and hydrology in the hydrothermal dispatch. [ABSTRACT FROM AUTHOR]