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)).

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.

Implementação de um sistema para simulação por Monte Carlo de fotões através do olho humano

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédica

Commissioning de um sistema de planimetria computorizado de Monte Carlo comercial para radioterapia

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Biomédica

Medição e cálculo, utilizando simulações Monte Carlo, das doses em exames de tomografia computorizada

Tese Mestrado Integrado em Engenharia Biomédica

Implementação de um sistema para simulação por Monte Carlo da passagem de fotões através do olho humano mediante a utilização da plataforma GEANT4 - I

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Implementação de um sistema para simulação por Monte Carlo da passagem de fotões através do olho humano mediante a utilização da plataforma GEANT4 – II

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Monte Carlo simulations for dosimetric verification in photon and electron beam radiotherapy

Dissertação para obtenção do Grau de Doutor em Engenharia Biomédica

Simulações Monte Carlo da redução da dose no cristalino e na tiroide em exames de tomografia computorizada utilizando proteções de bismuto

A presente dissertação foi desenvolvida com colaboração do Campus Tecnológico e Nuclear e do Hospital de São José

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.

Monte Carlo valuation of worst-of auto-callable equity swaps

This thesis proposes a Monte Carlo valuation method for Worst-of Auto-callable equity swaps. The valuation of this type of swap usually requires complex numerical methods which are implemented in “black-box” valuation systems. The method proposed is an alternative benchmark tool that is relatively simple to implement and customize. The performance of the method was evaluated according to the variance and bias of the output and to the accuracy when compared to a leading valuation system in the market.