The impacts of synthetic correlated generation of weather variables on adequacy analyses

Reducing carbon dioxide emissions caused by the production of electrical energy has made the decommissioning of coal-fired power plants a priority. The decommissioned fossil-fuelled power plants are therefore being replaced by renewable energy plants (RES) and other technologies useful for the energy transition, such as storage power plants. However, since RES generation depends on the randomness of weather variables, an even greater proliferation of RES increases the intermittency of electricity supply and thus jeopardises the coverage of electricity demand. This problem affects the adequacy of a power system. In this paper, an adequacy analysis for a one-bus power system is conducted and validated. Since power generation from renewable and conventional power plants depends on the combined and simultaneous effects of different weather variables, pairs of hourly values of solar radiation, air temperature, precipitation and wind speed are synthetically generated in a correlated manner using Monte Carlo simulations. The correlated weather variables are the inputs of photovoltaic (PV), wind, thermoelectric and hydropower generation models. These models, together with an electricity demand model, allow the calculation of adequacy indices needed to quantify the adequacy level of a given electricity system. This method is then repeatedby synthetically generating the weather variables without taking into account the correlation between them; thus, the adequacy indices are re-evaluated. Finally, the results of the adequacy indices obtained with the two different methods are compared. The Sicily market zone is used as a reference for the installed capacity and demand data. All simulations are carried out in the MATLAB environment (Copyright 2018 The MathWorks, Inc.) and in the PowerWorld simulator©.