Gestão do risco em mercados de energia eléctrica recorrendo ao VaR por simulação de Monte Carlo

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia.

Fuzzy Monte Carlo model for transmission power systems reliability based decision making

This thesis presents the Fuzzy Monte Carlo Model for Transmission Power Systems Reliability based studies (FMC-TRel) methodology, which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states. This is followed by a remedial action algorithm, based on Optimal Power Flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. For the system states that cause load curtailment, an optimization approach is applied to reduce the probability of occurrence of these states while minimizing the costs to achieve that reduction. This methodology is of most importance for supporting the transmission system operator decision making, namely in the identification of critical components and in the planning of future investments in the transmission power system. A case study based on Reliability Test System (RTS) 1996 IEEE 24 Bus is presented to illustrate with detail the application of the proposed methodology.

Monte Carlo na Avaliação de Opções Americanas

A determinação do preço justo de um contrato de opções, trouxe enormes desa os a diversos ramos da Matemática procurando desenvolver e aperfeiçoar modelos e métodos que melhor representem os comportamentos dos mercados nanceiros. A avaliação de opções americanas apresenta uma di culdade acrescida, uma vez que é necessário determinar uma estratégia óptima ao seu exercício antecipado, já que estas opções podem ser exercidas em qualquer momento até à sua maturidade. Investigações recentes mostram que metodologias baseadas em técnicas de simulação podem ser usadas com sucesso neste tipo de opções (Duan and Simonato (2001), Longsta and Schwartz (2001), Stentoft (2005)). Neste trabalho, usamos métodos de Monte Carlo para avaliar opções Americanas, recorrendo à abordagem sugerida por Longsta and Schwartz (2001), combinando modelos GARCH para o subjacente com Filtered Historical Simulation (Barone, Engle and Mancini (2008)).

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method

This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment.

Aplicação de métodos estocásticos na gestão de custos de construção de condutas de saneamento

O trabalho apresentado centra-se na determinação dos custos de construção de condutas de pequenos e médios diâmetros em Polietileno de Alta Densidade (PEAD) para saneamento básico, tendo como base a metodologia descrita no livro Custos de Construção e Exploração – Volume 9 da série Gestão de Sistemas de Saneamento Básico, de Lencastre et al. (1994). Esta metodologia descrita no livro já referenciado, nos procedimentos de gestão de obra, e para tal foram estimados custos unitários de diversos conjuntos de trabalhos. Conforme Lencastre et al (1994), “esses conjuntos são referentes a movimentos de terras, tubagens, acessórios e respetivos órgãos de manobra, pavimentações e estaleiro, estando englobado na parte do estaleiro trabalhos acessórios correspondentes à obra.” Os custos foram obtidos analisando vários orçamentos de obras de saneamento, resultantes de concursos públicos de empreitadas recentemente realizados. Com vista a tornar a utilização desta metodologia numa ferramenta eficaz, foram organizadas folhas de cálculo que possibilitam obter estimativas realistas dos custos de execução de determinada obra em fases anteriores ao desenvolvimento do projeto, designadamente numa fase de preparação do plano diretor de um sistema ou numa fase de elaboração de estudos de viabilidade económico-financeiros, isto é, mesmo antes de existir qualquer pré-dimensionamento dos elementos do sistema. Outra técnica implementada para avaliar os dados de entrada foi a “Análise Robusta de Dados”, Pestana (1992). Esta metodologia permitiu analisar os dados mais detalhadamente antes de se formularem hipóteses para desenvolverem a análise de risco. A ideia principal é o exame bastante flexível dos dados, frequentemente antes mesmo de os comparar a um modelo probabilístico. Assim, e para um largo conjunto de dados, esta técnica possibilitou analisar a disparidade dos valores encontrados para os diversos trabalhos referenciados anteriormente. Com os dados recolhidos, e após o seu tratamento, passou-se à aplicação de uma metodologia de Análise de Risco, através da Simulação de Monte Carlo. Esta análise de risco é feita com recurso a uma ferramenta informática da Palisade, o @Risk, disponível no Departamento de Engenharia Civil. Esta técnica de análise quantitativa de risco permite traduzir a incerteza dos dados de entrada, representada através de distribuições probabilísticas que o software disponibiliza. Assim, para por em prática esta metodologia, recorreu-se às folhas de cálculo que foram realizadas seguindo a abordagem proposta em Lencastre et al (1994). A elaboração e a análise dessas estimativas poderão conduzir à tomada de decisões sobre a viabilidade da ou das obras a realizar, nomeadamente no que diz respeito aos aspetos económicos, permitindo uma análise de decisão fundamentada quanto à realização dos investimentos.

Análise estocástica com recurso a ferramentas informáticas no planeamento de empreitadas de construção

O presente trabalho, desenvolvido sob a orientação do Prof. Jaime Gabriel Silva, centra-se na procura e aplicação de metodologias de planeamento com apoio de ferramentas informáticas de análise de risco, que permitem realizar, em tempo útil, o cálculo dos prazos resultantes de inúmeras combinações possíveis associadas à incerteza das durações das atividades, recorrendo a modelos estocásticos. O trabalho aborda inicialmente o contexto da Gestão na Construção, com particular enfase na Gestão do Risco. Nessa fase inicial, fez-se também um pequeno inquérito a profissionais com diferentes níveis de responsabilidade organizacional e empresas do setor. A parte fundamental do trabalho, incide nos procedimentos a adotar na elaboração do planeamento de empreitadas. Nesta parte do trabalho, introduzem-se os conceitos da análise de risco com recurso a uma ferramenta informática de apoio, o @Risk, que permite a utilização do Método de Monte Carlo, para obtenção de resultados num contexto de uma tomada de decisão baseada no risco. Refira-se que houve vários contactos com o fornecedor do programa, que permitiram tirar partido de outro programa da Palisade, Evolver, direcionado para otimização matemática, podendo ser utilizado, por exemplo, na perspetiva da minimização dos custos, o que pode interessar pela relação destes com as opções adotadas na elaboração do planeamento de empreendimentos. Finalmente, toma-se um exemplo real do planeamento de uma empreitada em execução à data da realização deste trabalho, onde se aplicaram os conceitos desenvolvidos no trabalho, confrontando os resultados com o andamento da obra.

A influência dos parâmetros nas estimativas VAR: caso prático

Dissertação de Mestrado em Finanças Empresariais

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach.