Q40 - Energy: GeneralReturn

Renewable power generation, especially wind power and solar power, is experiencing a strong expansion worldwide and especially in Germany. With high shares of these methods of power generation, energy storage is needed to enable a demand-oriented power supply even with weather-related fluctuations in generation. Against the background of a power supply based entirely on wind and solar power, the question arises as to what total costs arise with the inclusion of storage systems, which is the subject of this article. The calculation model uses hourly resolved real data of German electricity generation from the years 2012 to 2018 to determine the required storage capacities. The electricity generation costs used range between 0.02 and 0.10 EUR/kW/h. The costs for the considered energy storages are calculated based on the Levelised Cost of Storage (LCOS) metric. It is concluded that in an electricity supply system based on wind and solar power, it is not the electricity generation that causes the greatest costs, but the storage. With electricity generation costs of 0.06 EUR/kW/h, the total system costs are in a range of 0.19 to 0.28 EUR/kW/h. This means that, in terms of costs, energy storage is more significant than electricity generation.

This article evaluates the production possibilities of the electricity sector in selected EU countries. The estimates for production functions are based on the financial data of individual companies in the selected sector. The analysis was based on a linearized version of the two-factor Cobb-Douglas production function, which was subsequently modified to compare productivity results by company size and country. The countries were selected based on the results of a cluster analysis. The cluster analysis was performed using aggregated data on the shares of energy sources in production in the electricity sector. The results show that companies from countries with a high share of renewables (such as Denmark) perform the worst in terms of total productivity. Furthermore, it was found that large companies have significantly higher productivity when compared to their smaller competitors.

This research investigates factors influencing domestic energy demand among rural households. Data were collected from 260 randomly sampled household heads in the study area. Descriptive statistics, ANOVA, and Tobit regression were used for the analysis. Tobit regression results revealed that gender, household size, income, occupation, farm size, and per capita expenditure were significant in influencing the use of fuelwood; age, gender, household size, occupation, education, and per capita expenditure for charcoal, while age, marital status, income, education and per capita expenditure were significant determinants in the use of kerosene. Also, income, occupation, and per capita expenditure were the factors influencing the use of electricity among rural households in the study area. The study concluded that apart from income, other household variables were significant in determining energy usage. The study, therefore, recommended that government and stakeholders should develop policies that will promote the use of safe, reliable, and clean energy sources in order to reduce the negative environmental consequences while also enhancing human life quality.