Leakage Detection And Localization Method Based On A Hybrid Inverse/Direct Modelling Approach Suitable For Handling Multiple-Leak Scenarios

When an accurate hydraulic network model is available, direct modeling techniques are very straightforward and reliable for on-line leakage detection and localization applied to large class of water distribution networks. In general, this type of techniques based on analytical models can be seen as an application of the well-known fault detection and isolation theory for complex industrial systems. Nonetheless, the assumption of single leak scenarios is usually made considering a certain leak size pattern which may not hold in real applications. Upgrading a leak detection and localization method based on a direct modeling approach to handle multiple-leak scenarios can be, on one hand, quite straightforward but, on the other hand, highly computational demanding for large class of water distribution networks given the huge number of potential water loss hotspots. This paper presents a leakage detection and localization method suitable for multiple-leak scenarios and large class of water distribution networks. This method can be seen as an upgrade of the above mentioned method based on a direct modeling approach in which a global search method based on genetic algorithms has been integrated in order to estimate those network water loss hotspots and the size of the leaks. This is an inverse / direct modeling method which tries to take benefit from both approaches: on one hand, the exploration capability of genetic algorithms to estimate network water loss hotspots and the size of the leaks and on the other hand, the straightforwardness and reliability offered by the availability of an accurate hydraulic model to assess those close network areas around the estimated hotspots. The application of the resulting method in a DMA of the Barcelona water distribution network is provided and discussed. The obtained results show that leakage detection and localization under multiple-leak scenarios may be performed efficiently following an easy procedure.

Transmission network expansion planning considering multiple generation scenarios

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering open access. The methodology finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with multiples generation scenarios. The model presented is solved using a specialized genetic algorithm. The methodology is tested in a system from the literature. ©2008 IEEE.

Transmission Expansion Planning Via a Constructive Heuristic Algorithm in Restructured Electricity Industry

The transmission expansion planning problem in modern power systems is a large-scale, mixed-integer, nonlinear and non-convex problem. this paper presents a new mathematical model and a constructive heuristic algorithm (CHA) for solving transmission expansion planning problem under new environment of electricity restructuring. CHA finds an acceptable solution in an iterative process, where in each step a circuit is chosen using a sensitivity index and added to the system. The proposed model consider multiple generation scenarios therefore the methodology finds high quality solution in which it allows the power system operate adequacy in an environment with multiple generators scenarios. Case studies and simulation results using test systems show possibility of using Constructive heuristic algorithm in an open access system.

Risk/investment-driven transmission expansion planning with multiple scenarios

This study presents a new methodology based on risk/investment to solve transmission network expansion planning (TNEP) problem with multiple future scenarios. Three mathematical models related to TNEP problems considering multiple future generation and load scenarios are also presented. These models will provide planners with a meaningful risk assessment that enable them to determine the necessary funding for transmission lines at a permissible risk level. The results using test and real systems show that the proposed method presents better solutions compared with scenario analysis method. ©The Institution of Engineering and Technology 2013.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Modeling the biophysical impacts of global change in mountain biosphere reserves

Mountains and mountain societies provide a wide range of goods and services to humanity, but they are particularly sensitive to the effects of global environmental change. Thus, the definition of appropriate management regimes that maintain the multiple functions of mountain regions in a time of greatly changing climatic, economic, and societal drivers constitutes a significant challenge. Management decisions must be based on a sound understanding of the future dynamics of these systems. The present article reviews the elements required for an integrated effort to project the impacts of global change on mountain regions, and recommends tools that can be used at 3 scientific levels (essential, improved, and optimum). The proposed strategy is evaluated with respect to UNESCO's network of Mountain Biosphere Reserves (MBRs), with the intention of implementing it in other mountain regions as well. First, methods for generating scenarios of key drivers of global change are reviewed, including land use/land cover and climate change. This is followed by a brief review of the models available for projecting the impacts of these scenarios on (1) cryospheric systems, (2) ecosystem structure and diversity, and (3) ecosystem functions such as carbon and water relations. Finally, the cross-cutting role of remote sensing techniques is evaluated with respect to both monitoring and modeling efforts. We conclude that a broad range of techniques is available for both scenario generation and impact assessments, many of which can be implemented without much capacity building across many or even most MBRs. However, to foster implementation of the proposed strategy, further efforts are required to establish partnerships between scientists and resource managers in mountain areas.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Aparato para experimentação remota do lançamento de projéteis

Num sistema de ensino cada vez mais exigente, a experimentação assume um papel fundamental na aquisição e validação do conhecimento. No ensino da Física, a necessidade de compreender a influência do meio num dado conceito teórico leva a que a experimentação tenha um carácter obrigatório. Neste contexto, surgem três cenários capazes de suportar a aprendizagem dos conceitos teóricos adquiridos. A simulação que faz uso da velocidade e capacidades de cálculo do computador para obter o resultado de uma experiência, a experimentação tradicional em laboratório, na qual o aluno executa, presencialmente, a sua experiência e por último a experimentação remota, que permite a execução de uma experiência real sem a presença física do aluno. Esta dissertação apresenta o projeto de um aparato para experimentação remota do “Lançamento de projéteis”. De forma a providenciar um meio de ensino de Física mais flexível, o aparato desenvolvido permite, aos alunos, a determinação da aceleração da gravidade e o estudo da dependência do movimento de um projétil num conjunto de parâmetros. Este aparato, operado remotamente, é acedido via web, onde primeiramente é reservado um intervalo de tempo. O conjunto de parâmetros (“Bola”, “Altura de lançamento” e “Ângulo de lançamento”) da máquina permite suportar vários cenários de ensino da Física, com diferentes complexidades.

Propuesta para el desarrollo de un clúster frutícula en el de partamento de Cundinamarca

El departamento de Cundinamarca cuenta con una gran riqueza agrícola a lo largo de su extensión territorial. Su ubicación geográfica lo hace poseedor de múltiples escenarios óptimos para la siembra de un sinnúmero de frutas y hortalizas que juegan un impacto importante en la economía de la región. En este trabajo de grado se analiza específicamente la situación económica y social que rodea al cultivo del mango en las provincias del Tequendama y Alto Magdalena partiendo de la caracterización de este producto agrícola y de la región e implementando la guía del “manual de minicadenas productivas” de la ONUDI. El objetivo principal inicial del proyecto de investigación era realizar una propuesta para el desarrollo de un clúster frutícola en el Departamento de Cundinamarca, sin embargo, en la elaboración del documento principal de este proyecto fue posible determinar que aún no se cuentan con los factores necesarios para desarrollar un modelo asociativo de esta índole, los problemas sociales y de comercialización representan grandes obstáculos para su creación. Aquí se presentará un modelo de asociación basado en la estructura ejemplar de la Federación Nacional de Cafeteros de Colombia y un modelo de comercialización diseñado congruentemente con las necesidades y desafíos que se evidencian en la región, cuya implementación dependerá de la iniciativa de organismos gubernamentales, comerciales o educativos que logren ver en el proyecto, tal como nosotros lo hicimos, un gran potencial.

Uncertainty in the relationship between climate forcing and hydrological response in UK catchments

This paper assesses the relationship between amount of climate forcing – as indexed by global mean temperature change – and hydrological response in a sample of UK catchments. It constructs climate scenarios representing different changes in global mean temperature from an ensemble of 21 climate models assessed in the IPCC AR4. The results show a considerable range in impact between the 21 climate models, with – for example - change in summer runoff at a 2oC increase in global mean temperature varying between -40% and +20%. There is evidence of clustering in the results, particularly in projected changes in summer runoff and indicators of low flows, implying that the ensemble mean is not an appropriate generalised indicator of impact, and that the standard deviation of responses does not adequately characterise uncertainty. The uncertainty in hydrological impact is therefore best characterised by considering the shape of the distribution of responses across multiple climate scenarios. For some climate model patterns, and some catchments, there is also evidence that linear climate change forcings produce non-linear hydrological impacts. For most variables and catchments, the effects of climate change are apparent above the effects of natural multi-decadal variability with an increase in global mean temperature above 1oC, but there are differences between catchments. Based on the scenarios represented in the ensemble, the effect of climate change in northern upland catchments will be seen soonest in indicators of high flows, but in southern catchments effects will be apparent soonest in measures of summer and low flows. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation.

Transparent Fault Tolerance for Web Services based Architectures

Service-based architectures enable the development of new classes of Grid and distributed applications. One of the main capabilities provided by such systems is the dynamic and flexible integration of services, according to which services are allowed to be a part of more than one distributed system and simultaneously serve different applications. This increased flexibility in system composition makes it difficult to address classical distributed system issues such as fault-tolerance. While it is relatively easy to make an individual service fault-tolerant, improving fault-tolerance of services collaborating in multiple application scenarios is a challenging task. In this paper, we look at the issue of developing fault-tolerant service-based distributed systems, and propose an infrastructure to implement fault tolerance capabilities transparent to services.

Reactive power support pricing of distributed generators with primary energy source uncertainty

In this paper, a novel methodology to price the reactive power support ancillary service of Distributed Generators (DGs) with primary energy source uncertainty is shown. The proposed methodology provides the service pricing based on the Loss of Opportunity Costs (LOC) calculation. An algorithm is proposed to reduce the uncertainty present in these generators using Multiobjective Power Flows (MOPFs) implemented in multiple probabilistic scenarios through Monte Carlo Simulations (MCS), and modeling the time series associated with the generation of active power from DGs through Markov Chains (MC). © 2011 IEEE.

Pricing of reactive power support provided by distributed generators in transmission systems

Distributed Generation, microgrid technologies, two-way communication systems, and demand response programs are issues that are being studied in recent years within the concept of smart grids. At some level of enough penetration, the Distributed Generators (DGs) can provide benefits for sub-transmission and transmission systems through the so-called ancillary services. This work is focused on the ancillary service of reactive power support provided by DGs, specifically Wind Turbine Generators (WTGs), with high level of impact on transmission systems. The main objective of this work is to propose an optimization methodology to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). LOC occur when more reactive power is required than available, and the active power generation has to be reduced in order to increase the reactive power capacity. In the optimization process, three objectives are considered: active power generation costs of DGs, voltage stability margin of the system, and losses in the lines of the network. Uncertainties of WTGs are reduced solving multi-objective optimal power flows in multiple probabilistic scenarios constructed by Monte Carlo simulations, and modeling the time series associated with the active power generation of each WTG via Fuzzy Logic and Markov Chains. The proposed methodology was tested using the IEEE 14 bus test system with two WTGs installed. © 2011 IEEE.