State of art of data assimilation for variable renewable energies

The vast integration of intermittent variable renewable energy (VRE) sources in the electric grid poses a series of problems. The thesis will explain to readers with basic knowledge in electric circuits what are the main problems for achieving this task. Reducing the uncertainty linked to the production from variable renewable energy with forecasting methods is of great use for solving some of these problems. In this work the state of art of data assimilation techniques for integrating renewable source is analysed. Cause it is a quickly expanding field, five reviews were picked according to their date of publication and number of citations. The Kalman Filter has been successfully used in order to reduce the systematic error in wind power forecast. In photovoltaic power predictions no use of any Data Assimilation techniques was found. Anyway the most influential factor for photovoltaic forecasts is the solar irradiance that often is taken as an input from a Numerical Weather Prediction, where Data Assimilation techniques has been widely used. An interesting application for Data assimilation is the dynamic state estimation of the power grid because smart grids make power systems much less quasi-steady state. The availability of high quality phasor measurement units can represent the data to be assimilated.