C53 - Forecasting Models; Simulation MethodsReturn

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.

They are available many modeling and decision making systems. Some of them are based on statistical methods like time series analysis. The general problem of these systems is that they cannot correctly react to the changes of modeled systems and their environment. This paper presents an approach based on the fuzzy expert system application, which is able to represent the expert knowledge about the modeled system behavior. This approach combines the statistical methods with expert knowledge and is able to give appropriate information about the system behavior and help with the decision making process. The presented paper describes general principles of this system and its application for waste production modeling as a part of the decision making of the company for waste treatment. This company is able to optimize its resources and warehouse stock management to minimize the production costs.

We apply standard Merton and enhanced Merton-D-Vine copula model for the measurement of credit risk on the basis of accounting and stock market data for 4 companies from Prague Stock Exchange, in the midterm horizon of 4 years. Basic Merton structural credit model is based on assumption that firm equity is European option on company assets. Consequently enhanced Merton model take in account market data, dependence between daily returns and its volatility and helps to evaluate and project the credit quality of selected companies, i.e. correlation between assets trajectories through copulas. From our and previous results it is obvious that basic Merton model significantly underestimates actual level, i.e. offers low probabilities of default. Enhanced model support us with higher simulated probability rates which mean that capturing of market risk and transferring it to credit risk estimates is probably a good way or basic step in enhancing Merton methodology.