Flujos de potencia probabilísticos considerando centrales fotovoltaicas empleando el método de montecarlo

Abstract

Environmental pollution and fossil fuel depletion worldwide has led to motivation in several countries around the world for the production and research of renewable energies such as wind, solar thermal, solar light, tidal, wave power, geothermal and others.

Solar irradiance is a very variable resource that requires statistical analysis in order to predict its behaviour. The parameters as mean, standard deviation and kurtosis will be obtained using the descriptive statistic then the maximum likelihood estimator will be using the probability density function that best fits the solar irradiance data. In this work, fourteen months values are taken from the Secretary of the Environment of “Distrito Metropolitano de Quito”. The model of the photovoltaic power plant connected to the network is implemented for the IEEE test system New England, the model consists of three fundamental parts: DC modelling of the photovoltaic panel, after this by means of the power inverter we convert the voltage and dc current into alternating, the power inverter is modelled by the pulse width modulation and the phase angle, finally, the modelling of the power transformer, whose function is to connect the photovoltaic system with the network. Then, from the IEEE test system New England, the demand data from all points in the system is extracted and the load probabilistically is modelled using a normal probability density function with mean equal to the extracted value, and a standard deviation of 5% then by using MATLAB’s package "MATPOWER", power flows are solved using the Newton-Raphson method. Finally, through a graphical interface made in MATLAB Guide are obtained the results of the probabilistic power flows by the Monte Carlo method.