<p>Prior knowledge about the possible characteristics of demand and supply is vital in the planning and operation of economically sustainable isolated rural power systems. System modelling and simulation is one of the tools that can be used in planning and assessing the performance of these systems. This thesis is presenting a Monte Carlo simulation methodology for modelling, simulating and analysing the performance of isolated rural electricity markets applicable in developing countries. The definitions of possible power system operators managing these markets are introduced based on different economic objectives of operating the systems. The two system operators considered in the thesis are: altruistic and profit maximising operators. The concept used to define types of isolated rural electricity markets is combining the definitions of operators and the possible combinations of power supply options (purely thermal or hybrid system). It is anticipated that the rural electricity markets under consideration comprise of uncertainties in demand and supply (both demand and generation are modelled as random variables from assumed or estimated probability distributions).</p><p>Demand is price sensitive and modelled as a product of two random variables, relative demand and peak demand. The price sensitivity of demand is shown by representing peak demand using an economic price-demand function. The parameters (price sensitivity and demand factor) of this function are modelled as random variables which reflect the randomness of consumers’ preferences.</p><p>The simulation algorithm is based on the theory of correlated sampling, in order to compare the performance of systems under different operators. The thesis introduces the concept of nested Monte Carlo simulation to be able manage the simulation of different operators subjected to the same market conditions. The performance of electricity markets is assessed by analysing three parameters (tariffs, profit and reliability), which are random variables presented using probability distributions in form of duration curves.</p><p>The methodology is tested on a theoretical case study system using load data obtained from a rural community in Africa.  The case study illustrates how to use the model, preparation of the input variables and how to use the output to estimate and assess the possible performance of isolated rural power systems under different power system operators. It is anticipated that the proposed methodology can be used by researchers, planners and academia as a tool for planning, estimating and assessing the performance of rural power systems in isolated areas of developing countries</p>

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This thesis presents the development of a new method for incorporating station related outages in composite or bulk system reliability analysis. Station related failures can cause multiple component outages that can propagate to other parts of the network resulting in severe damages. In order to minimize the effects of station related outages on the composite system performance it is necessary for the designer to assess their effects. This task can be achieved by including station related outages in the composite system evaluation. Monte Carlo simulation is used in this research to assess composite system reliability. The new method described in this thesis is used to include station related outages in the reliability evaluation of two composite test systems. This new method is relatively simple and can be used to consider multiple component outages due to station related failures in composite system reliability evaluation. In this approach, the effects of station related outages are combined with the connected terminal failure parameters. Reliability studies conducted on the two composite test systems demonstrates that station failures significantly affect the system performance. The system reliability can be improved by selecting appropriate station configurations. This is illustrated by application to the two composite test systems.

This thesis presents the development of a new method for incorporating station related outages in composite or bulk system reliability analysis. Station related failures can cause multiple component outages that can propagate to other parts of the network resulting in severe damages. In order to minimize the effects of station related outages on the composite system performance it is necessary for the designer to assess their effects. This task can be achieved by including station related outages in the composite system evaluation. Monte Carlo simulation is used in this research to assess composite system reliability. The new method described in this thesis is used to include station related outages in the reliability evaluation of two composite test systems. This new method is relatively simple and can be used to consider multiple component outages due to station related failures in composite system reliability evaluation. In this approach, the effects of station related outages are combined with the connected terminal failure parameters. Reliability studies conducted on the two composite test systems demonstrates that station failures significantly affect the system performance. The system reliability can be improved by selecting appropriate station configurations. This is illustrated by application to the two composite test systems.

Se presenta el trabajo realizado sobre el paquete de Subrrutinas Penélope. Este código permite la simulación MC del transporte de fotones y electrones en la materia con geometrías complejas. Los aspectos considerados son:A) mejora del algoritmo de SCATTERING de la radiación primaria y de los algoritmos que dan cuenta de las secundarias.B) simplificación del algoritmo de SCATTERING mixto par electrones empleado anteriormente. C) incorporación de secciones eficaces diferenciales. D) un paquete de subrutinas geométricas, pengeom, ha sido desarrollado. Permite geometría combinatoria con superficies cuadricas.e) presentación de un marco teórico para aplicar técnicas de reducción de varianza.F) comparación con resultados experimentales y presentación de 4 aplicaciones reales que emplean pengeom y reducción de varianza. En su estado actual Penélope permite que usuarios externos no especializados puedan abordar problemas en el campo de la ingeniería de radiaciones, de la física médica, etc.

American options are financial contracts that allow exercise at any time until ex- piration. While the pricing of standard American option contracts has been well researched, with a few exceptions no analytical solutions exist. Valuation of more in- volved American option contracts, which include multiple underlying assets or path- dependent payoff, is still to a high degree an uncharted area. Most numerical methods work badly for such options as their time complexity scales exponentially with the number of dimensions. In this Master’s thesis we study valuation methods based on Monte Carlo sim- ulations. Monte Carlo methods don’t suffer from exponential time complexity, but have been known to be difficult to use for American option pricing due to the forward nature of simulations and the backward nature of American option valuation. The studied methods are: Parametrization of exercise rule, Random Tree, Stochastic Mesh and Regression based method with a dual approach. These methods are evaluated and compared for the standard American put option and for the American maximum call option. Where applicable the values are compared with those from deterministic reference methods. The strengths and weaknesses of each method is discussed. The Regression based method essentially reduces the problem to one of selecting suitable basis functions. This choice is empirically evaluated for the following Amer- ican option contracts; standard put, maximum call, basket call, Asian call and Asian call on a basket. The set of basis functions considered include polynomials in the underlying assets, the payoff, the price of the corresponding European contract as well as certain analytic approximation of the latter. Results from the empirical studies show that the regression based method is the best choice when pricing exotic American options. Furthermore, using available analytical approximations for the corresponding European option values as a basis function seems to improve the performance of the method in most cases.

In the design of the food irradiation process, the knowledge of the radiation resistance of the target organism in a specific food commodity is required. The D10-value, the radiation dose needed to inactivate 90% of the microbial load in the food medium, is used to relate the amount of absorbed energy to the surviving bacterial population. Numerous experimental studies have been performed to determine the D10 values of several food-borne microorganisms irradiated under various conditions. Nevertheless, accurate predictions of D10 values of the pathogens in food products that have not been empirically examined cannot be made due to insufficient understanding of the biological response to radiation exposure. A theoretical model for the derivation of the D10-value has been proposed in this study to mechanistically assess the production of radiation-induced DNA damage by energetic electrons. The step-by-step Monte-Carlo simulation technique, which employs the detailed histories of the ionizing particles and the radiolytic species, was utilized. The effects of selected parameters including the genomic sequence, the type of DNA double strand break, the DNA damaging agents, the radical scavengers, the degree of dispersion of DNA molecules, and the number of genome equivalents were hypothetically investigated. The developed computational methodology as well as the results presented can be used as an analytical tool to evaluate the impact of ionizing radiation on cell survival.

Wind power generations have increased impacts on the electric utility power systems. When the wind power is placed into service in an electric system, it becomes a functioning part of the system, which may require other design changes to the system and special practices to integrate it to the system. The presence of the wind power generation units will directly affect voltage profiles along a feeder by changing the direction and magnitude of active/reactive power flows. A number of coordination issues including safety issue, protection, voltages and frequency control presently require study in order to understand technical limits to the penetration of wind power or distributed generation on a given system. The aim of this thesis is to investigate the impacts of wind generators connected to a distribution system. To take load uncertainty and wind power generation uncertainty due to wind speed variation in the analysis, Monte Carlo simulation technique is used. A number of cases are tested to assess the impacts of wind power generations in various scenarios for the studied network. Test results have shown that the when wind power generators are connected to distribution network, it would not only reduce the probability of occurrence of undervoltage but also decrease the feeder losses. The analytical models proposed in this thesis can provide the utility useful information in placing the wind power generators.

Zugl.: München, Univ., Diss., 2007 / Hergestellt on demand