The introduction of large amounts of wind power into power systems will increase the production uncertainties due to unforeseen wind power production variations. This will have a significant impact on the required balance management quantities. The most suitable power source to balance fast production or consumption variations is hydropower because of its flexibility and low operational costs. This thesis addresses the problem of trading of electricity on the daily marketfrom a hydropower producer perspective in a system with large amounts of wind power. The overall aim is to present models that can be used in the trading decision process. This thesis describes models within three different areas:1. Modeling of the demand for balancing power by using deterministic andstochastic models. The stochastic models are based on stochastic differentialequations.2. Modeling of prices on the day-ahead and real-time markets using deterministic and stochastic models. The stochastic models are based on time series modeling.3. Short-term hydropower scheduling of trading decisions. These problems areformulated as stochastic optimization problems where the market prices arerandom variables. The first two can be used to simulate the impact of wind power on various market prices, while the third simulates how the hydropower producer responds to market prices. Thereby, the thesis presents the necessary models for short-term scheduling of hydropower for a future system with significant amounts of wind power. This thesis concludes that the proposed price models are sufficient to reflect the relevant price properties, and that the proposed short-term hydropower scheduling models can be used to simulate the actions taken by the hydropower producer in a system with significant amounts of wind power. This is also supported by the case studies in the appended publications. / QC 20100804

Dinamisme, capacitat de resposta i flexibilitat són característiques essencials en el desenvolupament de la societat actual. Les noves tendències de globalització i els avenços en tecnologies de la informació i comunicació fan que s'evolucioni en un entorn altament dinàmic i incert. La incertesa present en tot procés esdevé un factor crític a l'hora de prendre decisions, així com un repte altament reconegut en l'àrea d'Enginyeria de Sistemes de Procés (PSE). En el context de programació de les operacions, els models de suport a la decisió proposats fins ara, així com també software comercial de planificació i programació d'operacions avançada, es basen generalment en dades estimades, assumint implícitament que el programa d'operacions s'executarà sense desviacions. La reacció davant els efectes de la incertesa en temps d'execució és una pràctica habitual, però no sempre resulta efectiva o factible. L'alternativa és considerar la incertesa de forma proactiva, és a dir, en el moment de prendre decisions, explotant el coneixement disponible en el propi sistema de modelització.Davant aquesta situació es plantegen les següents preguntes: què s'entén per incertesa? Com es pot considerar la incertesa en el problema de programació d'operacions? Què s'entén per robustesa i flexibilitat d'un programa d'operacions? Com es pot millorar aquesta robustesa? Quins beneficis comporta? Aquesta tesi respon a aquestes preguntes en el marc d'anàlisis operacionals en l'àrea de PSE. La incertesa es considera no de la forma reactiva tradicional, sinó amb el desenvolupament de sistemes proactius de suport a la decisió amb l'objectiu d'identificar programes d'operació robustos que serveixin com a referència pel nivell inferior de control de planta, així com també per altres centres en un entorn de cadenes de subministrament. Aquest treball de recerca estableix les bases per formalitzar el concepte de robustesa d'un programa d'operacions de forma sistemàtica. Segons aquest formalisme, els temps d'operació i les ruptures d'equip són considerats inicialment com a principals fonts d'incertesa presents a nivell de programació de la producció. El problema es modelitza mitjançant programació estocàstica, desenvolupant-se finalment un entorn d'optimització basat en simulació que captura les múltiples fonts d'incertesa, així com també estratègies de programació d'operacions reactiva, de forma proactiva. La metodologia desenvolupada en el context de programació de la producció s'estén posteriorment per incloure les operacions de transport en sistemes de múltiples entitats i incertesa en els temps de distribució. Amb aquesta perspectiva més àmplia del nivell d'operació s'estudia la coordinació de les activitats de producció i transport, fins ara centrada en nivells estratègic o tàctic. L'estudi final considera l'efecte de la incertesa en la demanda en les decisions de programació de la producció a curt termini. El problema s'analitza des del punt de vista de gestió del risc, i s'avaluen diferents mesures per controlar l'eficiència del sistema en un entorn incert.En general, la tesi posa de manifest els avantatges en reconèixer i modelitzar la incertesa, amb la identificació de programes d'operació robustos capaços d'adaptar-se a un ampli rang de situacions possibles, enlloc de programes d'operació òptims per un escenari hipotètic. La metodologia proposada a nivell d'operació es pot considerar com un pas inicial per estendre's a nivells de decisió estratègics i tàctics. Alhora, la visió proactiva del problema permet reduir el buit existent entre la teoria i la pràctica industrial, i resulta en un major coneixement del procés, visibilitat per planificar activitats futures, així com també millora l'efectivitat de les tècniques reactives i de tot el sistema en general, característiques altament desitjables per mantenir-se actiu davant la globalitat, competitivitat i dinàmica que envolten un procés. / Dynamism, responsiveness, and flexibility are essential features in the development of the current society. Globalization trends and fast advances in communication and information technologies make all evolve in a highly dynamic and uncertain environment. The uncertainty involved in a process system becomes a critical problem in decision making, as well as a recognized challenge in the area of Process Systems Engineering (PSE). In the context of scheduling, decision-support models developed up to this point, as well as commercial advanced planning and scheduling systems, rely generally on estimated input information, implicitly assuming that a schedule will be executed without deviations. The reaction to the effects of the uncertainty at execution time becomes a common practice, but it is not always effective or even possible. The alternative is to address the uncertainty proactively, i.e., at the time of reasoning, exploiting the available knowledge in the modeling procedure itself. In view of this situation, the following questions arise: what do we understand for uncertainty? How can uncertainty be considered within scheduling modeling systems? What is understood for schedule robustness and flexibility? How can schedule robustness be improved? What are the benefits? This thesis answers these questions in the context of operational analysis in PSE. Uncertainty is managed not from the traditional reactive viewpoint, but with the development of proactive decision-support systems aimed at identifying robust schedules that serve as a useful guidance for the lower control level, as well as for dependent entities in a supply chain environment. A basis to formalize the concept of schedule robustness is established. Based on this formalism, variable operation times and equipment breakdowns are first considered as the main uncertainties in short-term production scheduling. The problem is initially modeled using stochastic programming, and a simulation-based stochastic optimization framework is finally developed, which captures the multiple sources of uncertainty, as well as rescheduling strategies, proactively. The procedure-oriented system developed in the context of production scheduling is next extended to involve transport scheduling in multi-site systems with uncertain travel times. With this broader operational perspective, the coordination of production and transport activities, considered so far mainly in strategic and tactical analysis, is assessed. The final research point focuses on the effect of demands uncertainty in short-term scheduling decisions. The problem is analyzed from a risk management viewpoint, and alternative measures are assessed and compared to control the performance of the system in the uncertain environment.Overall, this research work reveals the advantages of recognizing and modeling uncertainty, with the identification of more robust schedules able to adapt to a wide range of possible situations, rather than optimal schedules for a hypothetical scenario. The management of uncertainty proposed from an operational perspective can be considered as a first step towards its extension to tactical and strategic levels of decision. The proactive perspective of the problem results in a more realistic view of the process system, and it is a promising way to reduce the gap between theory and industrial practices. Besides, it provides valuable insight on the process, visibility for future activities, as well as it improves the efficiency of reactive techniques and of the overall system, all highly desirable features to remain alive in the global, competitive, and dynamic process environment.

published_or_final_version / Mathematics / Master / Master of Philosophy

Infrastructure systems are critical to a country’s prosperity. It is extremely important to manage the infrastructure systems efficiently in order to avoid wastage and to maximize benefits. Deterioration of infrastructure systems is one of the primary issues in civil engineering today. This problem has been widely acknowledged by engineering community in numerous studies. We need to evolve efficient strategies to tackle the problem of infrastructure deterioration and to efficiently operate infrastructure. In this research, we propose stochastic models to predict the process of deterioration in engineering systems and to perform life-cycle analysis (LCA) of deteriorating engineering systems. LCA has been recognized, over the years, as a highly informative tool for helping the decision making process in infrastructure management. In this research, we propose a stochastic model, SSA, to accurately predict the effect of deterioration processes in engineering systems. The SSA model addresses some of the important and ignored areas in the existing models such as the effect of deterioration on both capacity and demands of systems and accounting for different types of failures in assessing the life-span of a deteriorating system. Furthermore, this research proposes RTLCA, a renewal theory based LCA model, to predict the life-cycle performance of deteriorating systems taking into account not only the life-time reliability but also the costs associated with operating a system. In addition, this research investigates the effect of seismic degradation on the reliability of reinforced concrete (RC) bridges. For this purpose, we model the seismic degradation process in the RC bridge columns which are the primary lateral load resisting system in a bridge. Thereafter, the RTLCA model along with SSA model is used to study the life-cycle of an example RC bridge located in seismic regions accounting for seismic degradation. It is expected that the models proposed in this research will be helpful in better managing our infrastructure systems.

Biological transport is an essential phenomenon for the living systems. A mechanistic investigation of biological transport processes is highly important for the characterization of physiological and cellular events, the design and functioning of several biomedical devices and the development of new therapies. To investigate the physical-chemical details of this phenomenon, concerted efforts of both experiments and theory are necessary. Motor proteins constitute a major portion of the active transport in the living cell. However, the actual mechanism of how chemical energy is converted into their directed motion has still remained obscure. Recent experiments on motor proteins have been producing exciting results that have motivated theoretical studies. In order to provide deep insight onto motor protein's mechanochemical coupling we have used stochastic modeling based on discrete-state chemical kinetic model. Such models enable us to (1) resolve the contradiction between experimental observations on heterodimeric kinesins and highly popular hand-over-hand mechanism, (2) take into account the free energy landscape modification of individual motor domains due to interdomain interaction, (3) recognize the effect of spatial fluctuations on biochemical properties of molecular motors, and (4) calculate the dynamical properties such as velocities, dispersions of complex biochemical pathways. We have also initiated the investigation of the dynamics of coupled motor assemblies using stochastic modeling. Furthermore, an extensive Monte Carlo lattice simulation based study on facilitated search process of DNA-binding proteins is presented. This simulation shows that the accelerated search compared to pure Smoluchowski limit can be achieved even in the case where the one-dimensional diffusion is order of magnitude slower than the three-dimensional diffusion. We also show that facilitated search is not only the manifestation of dimensionality reduction but correlation times play a crucial role in the overall search times. Finally, a more general field of stochastic processes, namely first-passage time process is investigated. Explicit expressions of important properties, such as splitting probailities and mean first-passage times, that are relevant to (but not limited to) biological transport, are derived for several complex systems.

We address the problem of learning discrete hidden Markov models from very long sequences of observations. Incremental versions of the Baum-Welch algorithm that approximate the beta-values used in the backward procedure are commonly used for this problem since their memory complexity is independent of the sequence length. However, traditional approaches have two main disadvantages: the approximation of the beta-values deviates far from the real values, and the learning algorithm requires previous knowledge of the topology of the model. This dissertation describes a new incremental Baum-Welch algorithm with a novel backward procedure that improves the approximation of the â-values based on a one-step lookahead in the training sequence and investigates heuristics to prune unnecessary states from an initial complex model. Two new approaches for pruning, greedy and controlled, are introduced and a novel method for identification of ill-conditioned models is presented. Incremental learning of multiple independent observations is also investigated. We justify the new approaches analytically and report empirical results that show they converge faster than the traditional Baum-Welch algorithm using fewer computer resources. Furthermore, we demonstrate that the new learning algorithms converge faster than the previous incremental approaches and can be used to perform online learning of high-quality models useful for classification tasks. Finally, this dissertation explores the use of the new algorithms for anomaly detection in computer systems, that improve our previous research work on detectors based on hidden Markov models integrated into real-world monitoring systems of high-performance computers.

No description available.

Molecular cell biology has been an intensively studied interdisciplinary field with the rapid development of experimental techniques and fast upgrade of computational hardware and numerical tools. Recent technological developments have led to single-cell experiments which allow us to probe the role of stochasticity in cellular processes. Stochastic modeling of the corresponding processes is thus an essential ingredient for the understanding and interpretation of cellular systems of interest. In this thesis, we explore several nucleation-based stochastic cellular processes, i.e. Min protein oscillation in Escherichia coli, pausing phenomena in DNA transcription, and single-molecule enzyme kinetics. We focus on the key experimental results and build up stochastic models accordingly to provide quantitative insights to the underlying physical mechanisms for the corresponding biological processes. We utilize specific mathematical methods and computational algorithms to gain a better understanding and make predictions for further experimental explorations in the relevant fields. / Ph. D.

La modelación numérica del flujo de agua subterránea y del transporte de masa se está convirtiendo en un criterio de referencia en la actualidad para la evaluación de recursos hídricos y la protección del medio ambiente. Para que las predicciones de los modelos sean fiables, estos deben de estar lo más próximo a la realidad que sea posible. Esta proximidad se adquiere con los métodos inversos, que persiguen la integración de los parámetros medidos y de los estados del sistema observados en la caracterización del acuífero. Se han propuesto varios métodos para resolver el problema inverso en las últimas décadas que se discuten en la tesis. El punto principal de esta tesis es proponer dos métodos inversos estocásticos para la estimación de los parámetros del modelo, cuando estos no se puede describir con una distribución gausiana, por ejemplo, las conductividades hidráulicas mediante la integración de observaciones del estado del sistema, que, en general, tendrán una relación no lineal con los parámetros, por ejemplo, las alturas piezométricas. El primer método es el filtro de Kalman de conjuntos con transformación normal (NS-EnKF) construido sobre la base del filtro de Kalman de conjuntos estándar (EnKF). El EnKF es muy utilizado como una técnica de asimilación de datos en tiempo real debido a sus ventajas, como son la eficiencia y la capacidad de cómputo para evaluar la incertidumbre del modelo. Sin embargo, se sabe que este filtro sólo trabaja de manera óptima cuándo los parámetros del modelo y las variables de estado siguen distribuciones multigausianas. Para ampliar la aplicación del EnKF a vectores de estado no gausianos, tales como los de los acuíferos en formaciones fluvio-deltaicas, el NSEnKF propone aplicar una transformación gausiana univariada. El vector de estado aumentado formado por los parámetros del modelo y las variables de estado se transforman en variables con una distribución marginal gausiana. / Zhou ., H. (2011). Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12267

In two-location inventory systems, unidirectional transshipment policies are considered when an item is not routinely stocked at a location in the system. Unlike the past research in this area which has concentrated on the simple transshipment policies of complete pooling or no pooling, the research presented in this thesis endeavors to develop an understanding of a more general class of transshipment policy. The research considers two major approaches: a decomposition approach, in which the two-location system is decomposed into a system with independent locations, and Markov decision process approach. For the decomposition approach, the transshipment policy is restricted to the class of holdout transshipment policy. The first attempt to develop a decomposition approach assumes that transshipment between the locations occurs at a constant rate in order to decompose the system into two independent locations with constant demand rates. The second attempt modifies the assumption of constant rate of transshipment to take account of local inventory levels to decompose the system into two independent locations with non-constant demand rates. In the final attempt, the assumption of constant rate of transshipment is further modified to model more closely the location providing transshipments. Again the system is decomposed into two independent locations with non-constant demand rates. For each attempt, standard techniques are applied to derive explicit expressions for the average cost rate, and an iterative solution method is developed to find an optimal holdout transshipment policy. Computational results show that these approaches can provide some insights into the performance of the original system. A semi-Markov decision model of the system is developed under the assumption of exponential lead time rather than fixed lead time. This model is later extended to the case of phase-type distribution for lead time. The semi-Markov decision process allows more general transshipment policies, but is computationally more demanding. Implicit expressions for the average cost rate are derived from the optimality equation for dynamic programming models. Computational results illustrate insights into the management of the two-location system that can be gained from this approach.