In dieser Arbeit wird ein Modell für die Simulation des Verbraucherverhaltens in Haushalten entwickelt. Das Ziel ist die Erstellung von Lastprofilen für den Strom- und Wasserverbrauch. Das Modell wird in einem Programm implementiert. Die Ergebnisse werden anschließend validiert und verschiedene Kenngrößen mit Literaturwerten verglichen. Abschließend wird eine Parameterstudie durchgeführt, um den Einfluss verschiedener Faktoren wie z.B. das Arbeitszeitmodell oder die Feiertagsmodellierung auf Lastprofile zu quantifizieren. Das Modell basiert auf einem Bedürfnismodell aus der Psychologie und ermöglicht den Verzicht auf die Errechnung von Aktivitäts-Wahrscheinlichkeitsverteilungen. / In this thesis a model for the simulation of the behaviour of people in residential households is introduced. The goal is to generate load profiles for residential electricity and water consumption. The model is implemented as a Windows program. The results are validated and various metrics are compared with literature values. A parameter study is performed to quantify the influence of various factors such as the working hours or the influence of holidays on the load profile. The model is based on a desire model from the field of psychology and makes it possible to avoid calculating any probabilty distributions.

The first part of this dissertation presents an innovative method for the assessment of generation system reliability. In this method, genetic algorithm (GA) is used as a search tool to truncate the probability state space and to track the most probable failure states. GA stores system states, in which there is generation deficiency to supply system maximum load, in a state array. The given load pattern is then convoluted with the state array to obtain adequacy indices. In the second part of the dissertation, a GA based method for state sampling of composite generation-transmission power systems is introduced. Binary encoded GA is used as a state sampling tool for the composite power system network states. A linearized optimization load flow model is used for evaluation of sampled states. The developed approach has been extended to evaluate adequacy indices of composite power systems while considering chronological load at buses. Hourly load is represented by cluster load vectors using the k-means clustering technique. Two different approaches have been developed which are GA parallel sampling and GA sampling for maximum cluster load vector with series state revaluation. The developed GA based method is used for the assessment of annual frequency and duration indices of composite system. The conditional probability based method is used to calculate the contribution of sampled failure states to system failure frequency using different component transition rates. The developed GA based method is also used for evaluating reliability worth indices of composite power systems. The developed GA approach has been generalized to recognize multi-state components such as generation units with derated states. It also considers common mode failure for transmission lines. Finally, a new method for composite system state evaluation using real numbers encoded GA is developed. The objective of GA is to minimize load curtailment for each sampled state. Minimization is based on the dc load flow model. System constraints are represented by fuzzy membership functions. The GA fitness function is a combination of these membership values. The proposed method has the advantage of allowing sophisticated load curtailment strategies, which lead to more realistic load point indices.

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2017-12-21T17:39:10Z No. of bitstreams: 1 marianabrinatialtomar.pdf: 5565205 bytes, checksum: aed1136ebbc66049baea19e7b7da34fb (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-12-22T12:17:48Z (GMT) No. of bitstreams: 1 marianabrinatialtomar.pdf: 5565205 bytes, checksum: aed1136ebbc66049baea19e7b7da34fb (MD5) / Made available in DSpace on 2017-12-22T12:17:48Z (GMT). No. of bitstreams: 1 marianabrinatialtomar.pdf: 5565205 bytes, checksum: aed1136ebbc66049baea19e7b7da34fb (MD5) Previous issue date: 2017-09-01 / A inserção de geração distribuída em sistemas de potência do mundo todo vem crescendo em ritmo acelerado nos últimos anos. Grandes investimentos em fontes limpas e renováveis, especialmente a eólica e solar, estão sendo feitos pelos países com o objetivo de minimizar os impactos ambientais causados pela geração de energia a partir de combustíveis fósseis. A conexão de uma geração distribuída utilizando fontes intermitentes pode trazer algumas vantagens para o funcionamento do sistema de distribuição, como melhora dos níveis de tensão, aumento da confiabilidade e redução de perdas elétricas. Porém, do ponto de vista do sistema de transmissão, a conexão desse tipo de geração em algumas áreas do sistema pode acabar tendo um impacto negativo em sua operação, causando variações de tensão que podem afetar alguns equipamentos da rede e aumentar a complexidade de sua operação. Além disso, deve ser avaliado seu impacto no controle de frequência de carga e controle de tensão entre outros aspectos. Desta forma, pode-se verificar que esse tipo de conexão deve ser estudado com um maior nível de profundidade. A partir deste contexto, neste trabalho serão analisados os impactos sistêmicos da conexão de uma geração solar em sistemas elétricos de potência, avaliando os níveis de perdas elétricas e variações nos níveis de tensão. Duas metodologias de solução do fluxo de potência na presença de incertezas são avaliadas. As incertezas consideradas estão nos dados de geração e também de carga do sistema. A primeira metodologia é baseada na solução do fluxo de carga determinístico sequencial ao longo da curva de carga. Para esta metodologia são consideradas curvas de carga e de geração, obtidas através do Sistema Teste de Confiabilidade IEEE24 barras e do Laboratório Solar da UFJF, respectivamente. São analisados os impactos nos níveis de perdas elétricas, assim como a variação dos níveis de tensão nas barras para diferentes situações de geração. A segunda metodologia avaliada é baseada na utilização de aritmética affine para determinação dos impactos destas incertezas. Os resultados de perdas obtidos serão tratados considerando-se o ponto médio dos intervalos para fins de comparação com outras metodologias. Os resultados das duas metodologias são comparados aos obtidos pelas Simulações de Monte Carlo, com o objetivo de serem validados. / The insertion of distributed generation into worldwide power systems has been increasing at a rapid pace in the last few years. Large investments in clean and renewable sources, especially wind and solar, are being made by countries to minimize the environmental impacts caused by fossil fuel power generation. Connecting a distributed generation using intermittent sources can bring some benefits for the operation of the distribution system, such as improved voltage levels, increased reliability and reduction of electrical losses. However, from the point of view of the transmission system, the connection of this type of generation in some areas of the system can end up having a negative impact on its operation, causing voltage variations which can affect network equipments and increase the operation complexity. In addition, impacts on the load frequency control and voltage control among other aspects must be evaluated. Thus, this type of connection must be studied with greater level of depth. In this context, the systemic impacts of the connection of a solar plant to electrical power systems, evaluating the levels of electrical losses and variations in voltage levels are analysed. Two methodologies for solving the power flow in the presence of uncertainties are evaluated. The uncertainties considered are the generation and system load data. The first methodology is based on sequential Newton power flow solutions along the load curve, in which load and generation curves obtained from the IEEE24 Bus Test System and from the UFJF Solar Laboratory, respectively. The impacts on the electrical losses are analyzed, as well as the variation on bus voltage levels in the bars for various generation conditions. The second methodology is based on the use of affine arithmetic to determine the impacts of these uncertainties. The results of losses obtained will be treated considering the average point of the intervals for comparisons with other methodologies. The results obtained from the two methods are compared with those obtained by the Monte Carlo Simulations, in order to validate them.

In dieser Arbeit wird ein Modell für die Simulation des Verbraucherverhaltens in Haushalten entwickelt. Das Ziel ist die Erstellung von Lastprofilen für den Strom- und Wasserverbrauch. Das Modell wird in einem Programm implementiert. Die Ergebnisse werden anschließend validiert und verschiedene Kenngrößen mit Literaturwerten verglichen. Abschließend wird eine Parameterstudie durchgeführt, um den Einfluss verschiedener Faktoren wie z.B. das Arbeitszeitmodell oder die Feiertagsmodellierung auf Lastprofile zu quantifizieren. Das Modell basiert auf einem Bedürfnismodell aus der Psychologie und ermöglicht den Verzicht auf die Errechnung von Aktivitäts-Wahrscheinlichkeitsverteilungen.:Inhaltsverzeichnis 1 Einleitung 1.1 Motivation 1.2 Ziel der Arbeit 2 Einordnung 3 Wissensstand 3.1 Lastprofile 3.1.1 VDEW-Standard-Lastprofile 3.1.2 Referenzlastprofile von Ein- und Mehrfamilienhäusern für den Einsatz von KWK-Anlagen (VDI 4655) 3.1.3 BDEW-Standardlastprofile Gas 3.1.4 IEA Annex 42 Lastkurven 3.2 Lastprofilgeneratoren 3.2.1 Methoden 3.2.2 Auswahl der Beispiele 3.2.3 Lastprofilgenerator nach Stokes 3.2.4 Lastprofilgenerator nach IEA Annex 42 3.2.5 Lastprofilgenerator nach Jordan 3.2.6 Lastprofilgenerator nach NREL 3.2.7 Lastprofilgenerator nach Walker und Pokoski 3.2.8 Lastprofilgenerator nach Capasso 3.2.9 Lastprofilgenerator nach Widen et al. 3.2.10 Lastprofilgenerator nach Richardson 3.2.11 Lastprofilgenerator nach Metz 3.2.12 Lastprofilgenerator nach Fischer 3.2.13 Zusammenfassung der Lastprofilgeneratoren 3.3 Verhaltenssimulation 3.3.1 Rational Choice Model 3.3.2 Verhaltensmodell nach D. Dörner 3.4 Hausinfrastrukturmodelle 3.4.1 Heizung und Kühlung 3.4.2 Modellierung in TRNSYS 4 Das Modell des bLPG 4.1 Bedürfnismodell 4.2 Modellierung eines einzelnen Haushalts 4.2.1 Desires 4.2.2 Person 4.2.3 Load Types 4.2.4 Devices 4.2.5 Time Profile 4.2.6 Time Limits 4.2.7 Affordances 4.2.8 Berechnungsbeispiel Aktivitätenauswahl 4.2.9 Zusammenfassung der Modellierung eines Haushalts 4.3 Verbesserung der Modellqualität 4.3.1 Locations 4.3.2 Holidays 4.3.3 Geographic Locations 4.3.4 Subaffordances 4.3.5 Temperature Profiles und Date Based Profiles 4.3.6 Vacations 4.3.7 Autonome Geräte 4.4 Houses und Settlements 4.4.1 House Types 4.5 Abstraktion der Geräte 4.6 Abstraktion Haushaltsdefinition 4.7 Elemente für Auswertungen 4.8 Zusammenfassung des Modells des bLPG 5 Implementierung 5.1 Allgemeines 5.2 Historie 5.3 Features 5.4 Struktur 5.5 User Interface 5.6 Database 5.7 CalcController 5.8 Calculation 5.8.1 Aktivitätswahl 5.8.2 Protokollierung 5.8.3 House Infrastructure 5.9 ChartCreator 5.10 SimulationEngine.Exe 5.11 Verwendete Bibliotheken 5.12 Zusammenfassung der Implementierung 6 Modellierung der vordefinierten Haushalte 6.1 Datenbasis und Modellierung 6.2 Vordefininierte Elemente 6.3 Namensschema 6.4 Erfahrungen bei der Erstellung der vordefinierten Haushalte 6.5 Zusammenfassung 7 Validierung 7.1 Einzelner Haushalt 7.1.1 Aktivitäten - Rasterdiagramme 7.1.2 Aktivitäten - Zeit pro Affordanz 7.1.3 Summe des Stromverbrauchs 7.1.4 Verlauf des Lastprofils 7.1.5 Wasserverbrauch 7.1.6 Integration von Photovoltaik 7.1.7 Lichtbedarf 7.1.8 Zusammenfassung CHR03 7.2 Vordefinierte Haushalte 7.2.1 Stromverbrauch 7.2.2 Verhaltensgesteuerter Anteil am Stromverbrauch 7.2.3 Zeitverbrauch der Aktivitäten 7.2.4 Eigenverbrauchsquote mit einer Photovoltaik-Anlage 7.2.5 Jahresdauerlinien 7.3 Validierung einer Siedlung 7.3.1 Gleichzeitigkeitsfaktor des Stromverbrauchs 7.3.2 Vergleich einer Siedlung mit dem H0-Profil 7.4 Fazit 8 Anwendungsmöglichkeiten und Ergebnisse 8.1 Integration von Photovoltaik und Batterien 8.2 Parameterstudie 8.2.1 Vergleichskriterien 8.2.2 Einfluss von Brückentagen 8.2.3 Einfluss von Urlaubsreisen 8.2.4 Einfluss des Rentneranteils 8.2.5 Einfluss von Schichtarbeitern 8.2.6 Einfluss von Arbeitslosigkeit 8.2.7 Einfluss der Energieintensitätseinstellung 8.2.8 Einflussgröße Beleuchtung 8.3 Zusammenfassung der Parameterstudie 9 Ausblick 9.1 Verbesserungspotenziale der Implementierung 9.2 Verbesserungspotenziale der Datenbasis 9.3 Zusammenfassung des Ausblicks 10 Zusammenfassung Anhänge Anhang A Website Anhang B LoadProfileGenerator Manual Literaturverzeichnis / In this thesis a model for the simulation of the behaviour of people in residential households is introduced. The goal is to generate load profiles for residential electricity and water consumption. The model is implemented as a Windows program. The results are validated and various metrics are compared with literature values. A parameter study is performed to quantify the influence of various factors such as the working hours or the influence of holidays on the load profile. The model is based on a desire model from the field of psychology and makes it possible to avoid calculating any probabilty distributions.:Inhaltsverzeichnis 1 Einleitung 1.1 Motivation 1.2 Ziel der Arbeit 2 Einordnung 3 Wissensstand 3.1 Lastprofile 3.1.1 VDEW-Standard-Lastprofile 3.1.2 Referenzlastprofile von Ein- und Mehrfamilienhäusern für den Einsatz von KWK-Anlagen (VDI 4655) 3.1.3 BDEW-Standardlastprofile Gas 3.1.4 IEA Annex 42 Lastkurven 3.2 Lastprofilgeneratoren 3.2.1 Methoden 3.2.2 Auswahl der Beispiele 3.2.3 Lastprofilgenerator nach Stokes 3.2.4 Lastprofilgenerator nach IEA Annex 42 3.2.5 Lastprofilgenerator nach Jordan 3.2.6 Lastprofilgenerator nach NREL 3.2.7 Lastprofilgenerator nach Walker und Pokoski 3.2.8 Lastprofilgenerator nach Capasso 3.2.9 Lastprofilgenerator nach Widen et al. 3.2.10 Lastprofilgenerator nach Richardson 3.2.11 Lastprofilgenerator nach Metz 3.2.12 Lastprofilgenerator nach Fischer 3.2.13 Zusammenfassung der Lastprofilgeneratoren 3.3 Verhaltenssimulation 3.3.1 Rational Choice Model 3.3.2 Verhaltensmodell nach D. Dörner 3.4 Hausinfrastrukturmodelle 3.4.1 Heizung und Kühlung 3.4.2 Modellierung in TRNSYS 4 Das Modell des bLPG 4.1 Bedürfnismodell 4.2 Modellierung eines einzelnen Haushalts 4.2.1 Desires 4.2.2 Person 4.2.3 Load Types 4.2.4 Devices 4.2.5 Time Profile 4.2.6 Time Limits 4.2.7 Affordances 4.2.8 Berechnungsbeispiel Aktivitätenauswahl 4.2.9 Zusammenfassung der Modellierung eines Haushalts 4.3 Verbesserung der Modellqualität 4.3.1 Locations 4.3.2 Holidays 4.3.3 Geographic Locations 4.3.4 Subaffordances 4.3.5 Temperature Profiles und Date Based Profiles 4.3.6 Vacations 4.3.7 Autonome Geräte 4.4 Houses und Settlements 4.4.1 House Types 4.5 Abstraktion der Geräte 4.6 Abstraktion Haushaltsdefinition 4.7 Elemente für Auswertungen 4.8 Zusammenfassung des Modells des bLPG 5 Implementierung 5.1 Allgemeines 5.2 Historie 5.3 Features 5.4 Struktur 5.5 User Interface 5.6 Database 5.7 CalcController 5.8 Calculation 5.8.1 Aktivitätswahl 5.8.2 Protokollierung 5.8.3 House Infrastructure 5.9 ChartCreator 5.10 SimulationEngine.Exe 5.11 Verwendete Bibliotheken 5.12 Zusammenfassung der Implementierung 6 Modellierung der vordefinierten Haushalte 6.1 Datenbasis und Modellierung 6.2 Vordefininierte Elemente 6.3 Namensschema 6.4 Erfahrungen bei der Erstellung der vordefinierten Haushalte 6.5 Zusammenfassung 7 Validierung 7.1 Einzelner Haushalt 7.1.1 Aktivitäten - Rasterdiagramme 7.1.2 Aktivitäten - Zeit pro Affordanz 7.1.3 Summe des Stromverbrauchs 7.1.4 Verlauf des Lastprofils 7.1.5 Wasserverbrauch 7.1.6 Integration von Photovoltaik 7.1.7 Lichtbedarf 7.1.8 Zusammenfassung CHR03 7.2 Vordefinierte Haushalte 7.2.1 Stromverbrauch 7.2.2 Verhaltensgesteuerter Anteil am Stromverbrauch 7.2.3 Zeitverbrauch der Aktivitäten 7.2.4 Eigenverbrauchsquote mit einer Photovoltaik-Anlage 7.2.5 Jahresdauerlinien 7.3 Validierung einer Siedlung 7.3.1 Gleichzeitigkeitsfaktor des Stromverbrauchs 7.3.2 Vergleich einer Siedlung mit dem H0-Profil 7.4 Fazit 8 Anwendungsmöglichkeiten und Ergebnisse 8.1 Integration von Photovoltaik und Batterien 8.2 Parameterstudie 8.2.1 Vergleichskriterien 8.2.2 Einfluss von Brückentagen 8.2.3 Einfluss von Urlaubsreisen 8.2.4 Einfluss des Rentneranteils 8.2.5 Einfluss von Schichtarbeitern 8.2.6 Einfluss von Arbeitslosigkeit 8.2.7 Einfluss der Energieintensitätseinstellung 8.2.8 Einflussgröße Beleuchtung 8.3 Zusammenfassung der Parameterstudie 9 Ausblick 9.1 Verbesserungspotenziale der Implementierung 9.2 Verbesserungspotenziale der Datenbasis 9.3 Zusammenfassung des Ausblicks 10 Zusammenfassung Anhänge Anhang A Website Anhang B LoadProfileGenerator Manual Literaturverzeichnis

[pt] O Brasil possui um Sistema Interligado Nacional (SIN) que se baseia na geração de energia elétrica por meio de usinas hidrelétricas, térmicas, solares fotovoltaicas e eólicas. O planejamento e operação deste sistema é efetuado com base em previsões efetuadas em curto, médio e longo prazo a fim de evitar imprevistos que possam afetar o suprimento da demanda de energia elétrica em território nacional. Uma das informações consideradas fundamentais em cada uma das etapas do planejamento da operação é a carga, ou seja, a demanda por energia elétrica. Quando trabalhada em curto prazo, esta é importante para a programação diária da operação, garantindo um cenário ótimo para uso dos recursos disponíveis e, em cenário mais atual, determinação do Preço de Liquidação das Diferenças a cada hora. Quando trabalhada em médio prazo, esta funciona como base para manutenções de redes e negociações de contrato. Já em longo prazo, a previsão é importante para fornecer informações usadas como base para estratégias de expansão do Sistema. Normalmente a previsão em longo prazo é trabalhada de maneira a escalonar a curva histórica anual, mas as constantes alterações no hábito de consumo da população e a inserção de novas fontes ocasionam relevantes alterações no perfil da curva de carga diária em longo prazo, tornando necessário o planejamento não somente da expansão do sistema, mas também a forma com que este poderá ser programado. Assim, com o objetivo de propor uma ferramenta de suporte ao mercado brasileiro de energia, este trabalho propõe uma Metodologia para Geração de Cenários de Carga de Longo Prazo. O método proposto propõe uma abordagem bottom-up para previsão anual da demanda utilizando premissas de trabalhos acadêmicos recentes, propõe um método de geração de perfis específicos para suprir a escassez de dados horários detalhados no Brasil e propõe a utilização da Abordagem de Decomposição Parcial a fim de transformar as previsões anuais de demanda em curvas de carga horária. Finalizando a aplicação da Metodologia para Geração de Cenários de Longo Prazo, diferentes resultados gerados são utilizados para aplicação de simulação por Monte Carlo, sendo os intervalos de confianças gerados com base na resposta, possíveis cenários de comportamento da carga no futuro, transformando um método de previsão previamente determinístico em um previsor de cenários. Com o objetivo de demonstrar resultados da método, a Metodologia é aplicada para geração de cenários de longo prazo para a região sudeste brasileira até 2020 com base na curva histórica de 2016, apesar de ser capaz de gerar previsões para horizontes maiores, demonstrando verdadeiro potencial para se adaptar a possíveis alterações na curva de carga. / [en] Brazil has a National Interconnected System which produces and transmits electrical energy through a hydro-thermo-wind system. The planning and operation of this system is based on short, medium and long term on forecasts in order to avoid unforeseen that may affect the electricity supply in national territory. The short-term forecast is important for daily schedule of operation, certifying the resource use optimal scenario and, in a current scenario, the determination of Settlement Price for Differences at each hour. The medium-term forecast is used as a basis for network maintenance and contract negotiations. The long-term forecast is important to provide information used as basis for system expansion strategies. Usually, the long-term forecast is made staggering the annual load curve, however, the constant changes on people electrical consumption habits and insertion of new electrical generation sources cause relevant changes in daily load curve profile over the long term, making necessary not only the expansion planning, but also the way it can be programmed on long-term horizon. Thus, in order to propose a support tool to the Brazilian energy market, this work presents a Scenarios Generation Methodology. Such procedure proposes bottom-up approach as an annual demand projection provider, using assumptions of recent academic works, proposes a specific profile generation method as a way to overcome the lack of specific hourly data in Brazil. Not only that, the method also proposes Partial Decomposition Approach to adapt annual electricity demand into hourly load curves. Concluding the Scenarios Generation Methodology, future scenarios are developed by Monte Carlo simulation applied over different obtained results and confidence intervals calculated based on response are possible values of load behavior in the future, thus turning a deterministic forecasting method into a scenarios generation methodology. In order to demonstrate the Methodology application, it is used to generate long-term scenarios for the southeast Brazilian region by 2020 based on historical load curve from 2016, although it is capable of generating forecasts for larger horizons, proving true potential to adapt to possible changes on load curve.

The diploma thesis examines the influence of curve radius on continuous welded rail stability in a small radius curve. Attention is paid to curves with a radius less than 300 m. The first part of the thesis summarises the theory of continuous welded track and influence of track parameters on track stability. The second part of the thesis deals with constructing of the numerical model of a curved track with the observed behaviour of track under thermal loading with positive and negative temperature values. The variable values in the model are rail shapes and different radius. In the end, the shape of the track lateral deflection as a function of temperature are evaluated.

Diploma thesis is focused on the description of the parameters of nonlinear material models of concrete, which are implemented in a computational system LS-DYNA, interacting with performance of nonlinear test calculations in system LS-DYNA on selected problems, which are formed mainly by simulations of tests of mechanical and physical properties of concrete in uniaxial compressive and tensile on cylinders with applying different boundary conditions and by simulation of bending slab, with subsequent comparison of some results of test calculations with results of the experiment. The thesis includes creation of appropriate geometric models of selected problems, meshing of these geometric models, description of parameters and application of nonlinear material models of concrete on selected problems, application of loads and boundary conditions on selected problems and performance of nonlinear calculations in a computational system LS-DYNA. Evaluation of results is made on the basis of stress-strain diagrams and load-displacement diagrams based on nonlinear calculations taking into account strain rate effects and on the basis of hysteresis curves based on nonlinear calculations in case of application of cyclic loading on selected problems. Verification of nonlinear material models of concrete is made on the basis of comparison of some results of test calculations with results obtained from the experiment.