The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey

Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development

The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%).

Analysis of the Impact of the COVID-19 Pandemic on the Value of CO2 Emissions from Electricity Generation

The study analyzed the impact of the COVID-19 pandemic on the carbon dioxide emissions from electricity generation. Additionally, monthly seasonality was taken into account. It was assumed (research hypothesis) that both the COVID-19 pandemic (expressed in individual waves of infection cases) and the month have a significant impact on CO2 emissions. Analysis of variance (ANOVA) and non-parametric Kruskal–Wallis tests were used to evaluate the significance of the influence of individual explanatory variables on the CO2 emission. The identification of the studied series (CO2 emission) was first made by means of a linear regression model with binary variables and then by the ARMAX model. The analysis shows that in the consecutive months and periods of the COVID-19 pandemic, CO2 emissions differ significantly. The highest increase in emissions was recorded for the second wave of the pandemic, as well as in January and February. This is due to the overlapping of both the increase in infections (favoring stays at home) and the winter season. It can be concluded that working plants, schools and factories had the same demand for electricity, but sources of increased consumption were people staying at home and in hospitals as a result of deteriorated health, isolation or quarantine.