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Statisitcal models of the renewable energy market
Kozma, Petr ; Marek, Luboš (advisor) ; Coufal, Jan (referee) ; Hronová, Stanislava (referee) ; Janáček, Kamil (referee)
An efficient application and development of renewable energy sources is one of the most important contribution to the energetic balance of the human society. Anyhow, statistical model of the renewable energy market, which would fundamentally explain relevant economical rules related to these perspective energetic resources, is not clearly known up to now. Nevertheless, the relevant statistical data concerning application of solar energy (photovoltaic and thermo-solar heating) are available for the last twenty years. Based on the economic models, statistical data concerning sales of photovoltaic models and thermo-solar collectors sales have been analysed in this work. It has been shown that the model of constant elasticity predicts an exponential increase which will slow down when a certain level of annual cumulative sales was reached. The model of constant elasticity was found to be successful to interpret past sales data. In the approach of variable elasticity model the parameter of the elasticity has been modified as a function of variables such as market volume, price and time through the statistical evaluation. It enabled to calculate initial, saturation and competitive market conditions, as well. Whereas the constant elasticity demand model describes exponential growth of sales and installations, which was characteristic for the beginning of the application of these renewable resources of energy, the variable elasticity demand model describes a more realistic situation, where cumulative sales either increase or decrease and prices vary subsequently. Simple growth model of unlimited demand based on the growing sales is not realistic and could not be feasible in the long term. The market elasticity could be understood as a real economical parameter representing percentual market increase or decrease at a given time; in the variable demand elasticity model, the constant elasticity is replaced by a function of a market volume, price and time. In this case, we can estimate model parameters for the different market conditions: growth, saturation and decrease. The function representing the capital adequacy in the generalized market model has also been deliberated. Statistical models have been used to determine cumulative sales and market prices of photovoltaic modules and thermo-solar collectors. Moreover, model parameters have been used for the calculation of the realized photovoltaic and thermo solar projects' capital adequacy on the renewable energy market. By using model parameters, renewable energy market forecast up to 2020 has been estimated. We have used generalized market model to credibly estimate future renewable energy market until 2020; as well as extend model parameterization on other resources of renewable energy (water and wind, geothermal sources, biomass) and set prices of energy produced from these renewable sources. Potential energetic savings have been estimated for households (apartments and private houses), who can be relevant consumers of energy from renewable sources. We have performed statistical findings on randomly selected files, where we have reached a real energy consumption, to prove this. This research allowed us to perform a real estimate of a renewable energy contribution to the total energy balance. We have successfully proved that linearly growing capital adequacy function, with an annual growth between 2.5% and 3.0%, is reflecting the renewable energy market sufficiently and is fully in line with an average growth of the total energy consumption. Renewable energy share on the total energy balance will grow substantially to reach a level of 15% in 2015 on the world market and a level of 8% in the Czech Republic for the same period with a perspective to reach a level of 11% in 2020 respectively. Assuming this level of renewable energy on the total production will lead to a decrease of CO2 emissions by three million of tones in 2015 and by four million of tones in 2020. Final reach of this status quo is fully predicted by our statistical model for renewable energy market.
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