Stochastic Load Simulations Depending on Bias-corrected Climate Projections

The increasing variability and uncertainty of the power system is due to both growing penetration of renewable sources and modifications in the load absorption. This leads to the need of modelling the operation and planning of the power system with probabilistic methods. A typical approach is the Monte Carlo simulation, whose iterations require many realizations of time series of stochastic quantities such as load and renewable power. For this purpose, the procedure SPOPSI (Stochastic wind POwer Profile SImulator) was developed to generate future hourly series of wind and photovoltaic power, and load. The new module SPOPSI_load is described here. It starts from the analysis of historical series of load in Italian market zones and, considering the dependence of historical load on some meteorological variables deducted from the reanalysis dataset MERIDA HRES, synthesizes a model that is used to generate new plausible stochastic series in the future. The future load series are generated from the projections of ten Euro-CORDEX climate models in three different emission scenarios by 2100. The climate data were pre-processed through a bias-correction technique to make them consistent with the historical time series of MERIDA HRES. Moreover, the SPOPSI_load module can integrate several future electrification scenarios due to electric vehicles and heat pumps penetration. So, the new series preserve the statistical properties of the past but have a significant hourly variability, to simulate a wide range of plausible power system load absorption. The validation and one application of SPOPSI_load for the year 2050 are presented.