Decision-support tool for assessing future nuclear reactor generation portfolios

Capital costs, fuel, operation and maintenance (O&M) costs, and electricity prices play a key role in the economics of nuclear power plants. Often standardized reactor designs are required to be locally adapted, which often impacts the project plans and the supply chain. It then becomes difficult to ascertain how these changes will eventually reflect in costs,which makes the capital costs component of nuclear power plants uncertain. Different nuclear reactor types compete economically by having either lower and less uncertain construction costs, increased efficiencies, lower and less uncertain fuel cycles and O&M costs etc. The decision making process related to nuclear power plants requires a holistic approach that takes into account the key economic factors and their uncertainties. We here present a decision-support tool that satisfactorily takes into account the major uncertainties in the cost elements of a nuclear power plant, to provide an optimal portfolio of nuclear reactors. The portfolio so obtained, under our model assumptions and the constraints considered, maximizes the combined returns for a given level of risk or uncertainty. These decisions are made using a combination of real option theory and mean\xe2\x80\x93variance portfolio optimization

Long range dependence in discharge time series and its relationship to external drivers

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheThe long term analysis of hydrological variables, such as discharge, is important given the recent interest in climate change effects on the water balance of catchments. The aim of this thesis is to a gain deeper understanding of the long term behaviour of discharge and its possible dependencies on various climate and storage related drivers from a long term perspective. There are several criteria that can be considered, when analysing time series from a long term perspective. Long range dependence, measured by the Hurst coefficient, gives information about the autocorrelation for high time lags. This phenomenon is investigated in Chapter 2, where the Hurst coefficient of 39 series of mean daily discharges of European rivers is estimated using different methods. The existence of long range dependence is identified in all time series. Furthermore, the correlations between the Hurst coefficient and several discharge related characteristics are investigated. Various significant correlations are found including a positive correlation between the Hurst coefficient and catchment area and air temperature. Another approach of analysing hydrological variables from a long term perspective are wavelet and cross-wavelet spectra. This methodology is used in Chapter 3 to analyse monthly time series from the Danube River in order to find long cycles. The correlations between the spectra are examined for discharge, air temperature and precipitation monthly data sets. Long cycles with over a decade long return periods are found in all discharge time series. Long cycles in selected precipitation time series are found as well. However, no long cycles can be identified in the air temperature time series. The cross-wavelet analysis shows strong correlations between the discharge and precipitation spectra, especially for low frequencies. The two approaches mentioned above are combined in Chapter 4, where a method for deseasonalisation of time series using discrete wavelet transformation is proposed. Long range dependence of the time series is taken into consideration by using an ARFIMA (autoregressive fractionally integrated moving average) model. Wavelet deseasonalisation is compared to a standard moving average deseasonalisation approach, using forecasting performance as a comparison criterion. The results show that, considering one to ten days ahead forecasting performance, the wavelet deseasonalisation approach improves the forecasting performance for longer forecasting horizons compared to the standard approach. The findings of this thesis give new insights into discharge and discharge related processes from the long term perspective. They form a basis for more accurate multivariate modelling, using discharge as dependent and possibly air temperature and precipitation as explanatory variables. The results of this thesis suggest that there are significant cycles with multidecadal time periods in European rivers. Furthermore, the results highlight the need to approach time series modelling on a case-by-case base, considering the specific periodic behaviour of each data set separately, emphasizing the need for future improvements of stochastic modelling of discharge processes.7

Diagnosis of Selected pre-Concepts Towards Technical and Media Education of 4th and 5th Grade Pupils of Primary School

This thesis deals with the diagnosis and eventually evaluating certain preconcepts in the area of technical and media education of 4th and 5th grade pupils of primary school. The theoretical part is dedicated to the issue, related to the topic. Constructivism and its didactics is included, also the role of teacher in this type of education, concept of student curriculum and concepts of teaching and learning. We also mention technical and media education and its concepts in educational framework. The practical part is focused on a particular preconcepts and their subjective perception of pupils at primary school. For the research questionnaires were used to detect the cognitive and affective level. For better understanding of cognitive level mental maps were used. The pupils of primary schools in ČB took part in the research, epsecially the pupils of the primary schools Kubatova, Dukelská and Rožnov

Adaption of Marketing Mix for Selected Country

Using the SWOT analysis, it was found that Kofola Plc. is in a sufficiently strong position to enter the foreign market, specifically the Austrian market, and the competition in the Austrian market was further analysed. It showed that there are three major competitors in the market. Using the PEST analysis, information concerning the exchange of goods and exports, or the financial situation of Austrian citizens was found. The company’s marketing mix on the Czech market and its subsequent adaptation to the Austrian market were described. The greatest need for adaptation was identified in the marketing communication and also in the product itself, specifically packaging, which will require a language mutation in the composition. Also, some change in appearance to match the marketing communication will be needed. The marketing communication will focus mainly on the unique taste of Kofola, which could be an attraction for potential customers, mainly because Austrians already like the herbal drink made by Almdudler, which is also one of the company’s competitors, together with Coca Cola Austria and PepciCo

Fuel price and technological uncertainty in a real options model for electricity planning

Electricity generation is an important source of total CO2 emissions, which in turn have been found to relate to an acceleration of global warming. Given that many OECD countries have to replace substantial portions of their electricity-generating capacity over the next 10-20 years, investment decisions today will determine the CO2-intensity of the future energy mix. But by what type of power plants will old (mostly fossil-fuel-fired) capacity be replaced? Given that modern, less carbon-intensive technologies are still expensive but can be expected to undergo improvements due to technical change in the near future, they may become more attractive, especially if fossil fuel price volatility makes traditional technologies more risky. At the same time, technological progress is an inherently uncertain process itself. In this paper, we use a real options model with stochastic technical change and stochastic fossil fuel prices in order to investigate their impact on replacement investment decisions in the electricity sector. We find that the uncertainty associated with the technological progress of renewable energy technologies leads to a postponement of investment. Even the simultaneous inclusion of stochastic fossil fuel prices in the same model does not make renewable energy competitive compared to fossil-fuel-fired technology in the short run based on the data used. This implies that policymakers have to intervene if renewable energy is supposed to get diffused more quickly. Otherwise, old fossil-fuel-fired equipment will be refurbished or replaced by fossil-fuel-fired capacity again, which enforces the lock-in of the current system into unsustainable electricity generation.Real options Energy policy Fossil fuel price uncertainty Technical change

Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence

Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA) model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization

Evaluation of power investment decisions under uncertain carbon policy: A case study for converting coal fired steam turbine to combined cycle gas turbine plants in Australia

Greenhouse gas (GHG) intensive fuels are currently a major input into the Australian electricity sector. Accordingly, climate change mitigation policies represent a systematic risk to investment in electricity generation assets. Although the Australian government introduced carbon pricing in 2012 and announced a commitment to the continuation of the Kyoto protocol beyond 2012, the opposition at the time signalled that should they be provided the opportunity they would repeal these policies. This paper uses a real options analysis (ROA) framework to investigate the optimal timing of one potential business response to carbon pricing: investment in the conversion of coal plant to lower emission CCGT plant. An American-style option valuation method is used for this purpose. The viewpoint is from that of a private investor assessing three available options for an existing coal plant: (1) to invest in its conversion to CCGT; (2) to abandon it, or; (3) to take no immediate action. The method provides a decision criterion that informs the investor whether or not to delay the investment. The effect of market and political uncertainty is studied for both the Clean Energy Act 2011 (CEA) and high carbon price (HCP) policy scenarios. The results of the modelling suggest that political uncertainty after the implementation of carbon pricing impedes the decision to switch to cleaner technologies. However, this effect can be mitigated by implementing higher expected carbon prices

Freely available mean daily discharge series from Czechia: what can be inferred from them?

Most hydrometeorological data from Czechia are still provided for a fee. This especially applies to time series with a finer step than monthly. The fact that the data must be paid with respect to an expert’s appraisement and that the proper licence agreement ratification has to be performed causes a considerable delay in the data transfer to potential customers, unfortunately including scientists as well. Naturally, this time-consuming process is unpleasant to the experts on both sides. Due to a substantial rise of university students’ requests for data, the Czech Hydrometeorological Institute’s hydrologists launched a website from which long mean daily discharge series representing ten selected water-gauging stations can be downloaded. Besides other assessments, the series may play an important role when studying climate change impacts on water resources in Czechia. Therefore, the objective here was to extract from these series some preliminary information on long-term changes such as abrupt and gradual trends caused either by the construction of reservoirs or by climate variability itself. The main tool used was nonparametric trend analysis. Mainly different months were of interest so as to determine if there have been recorded some changes in seasonality. The results may be easily expanded by students