The deployment of Renewable Energy (RE)-based generation has experienced a sustained global growth in the recent decades, driven by many countries' interest in reducing greenhouse gas emissions and dependence on fossil fuel for electricity generation. This trend is also observed in remote off-grid systems (isolated microgrids), where local communities, in an attempt to reduce fossil fuel dependency and associated economic and environmental costs, and to increase availability of electricity, are favouring the installation of RE-based generation. This practice has posed several challenges to the operation of such systems, due to the intermittent and hard-to-predict nature of RE sources. In particular, this thesis addresses the problem of reliable and economic dispatch of isolated microgrids, also known as the energy management problem, considering the uncertain nature of those RE sources, as well as loads. Isolated microgrids feature characteristics similar to those of distribution systems, in terms of unbalanced power flows, significant voltage drops and high power losses. For this reason, detailed three-phase mathematical models of the microgrid system and components are presented here, in order to account for the impact of unbalanced system conditions on the optimal operation of the microgrid. Also, simplified three-phase models of Distributed Energy Resources (DERs) are developed to reduce the level of complexity in small units that have limited impact on the optimal operation of the system, thus reducing the number of equations and variables of the problem. The proposed mathematical models are then used to formulate a novel energy management problem for isolated microgrids, as a deterministic, multi-period, Mixed-Integer Nonlinear Programming (MINLP) problem. The multi-period formulation allows for a proper management of energy storage resources and multi-period constraints associated with the commitment decisions of DERs. In order to obtain solutions of the energy management problem in reasonable computational times for real-time, realistic applications, and to address the uncertainty issues, the proposed MINLP formulation is decomposed into a Mixed-Integer Linear Programming (MILP) problem, and a Nonlinear programming (NLP) problem, in the context of a Model Predictive Control (MPC) approach. The MILP formulation determines the unit commitment decisions of DERs using a simplified model of the network, whereas the NLP formulation calculates the detailed three-phase dispatch of the units, knowing the commitment status. A feedback signal is generated by the NLP if additional units are required to correct reactive power problems in the microgrid, triggering a new calculation MINLP problem. The proposed decomposition and calculation routines are used to design a new deterministic Energy Management System (EMS) based on the MPC approach to handle uncertainties; hence, the proposed deterministic EMS is able to handle multi-period constraints, and account for the impact of future system conditions in the current operation of the microgrid. In the proposed methodology, uncertainty associated with the load and RE-based generation is indirectly considered in the EMS by continuously updating the optimal dispatch solution (with a given time-step), based on the most updated information available from suitable forecasting systems. For a more direct modelling of uncertainty in the problem formulation, the MILP part of the energy management problem is re-formulated as a two-stage Stochastic Programming (SP) problem. The proposed novel SP formulation considers that uncertainty can be properly modelled using a finite set of scenarios, which are generated using both a statistical ensembles scenario generation technique and historical data. Using the proposed SP formulation of the MILP problem, the deterministic EMS design is adjusted to produce a novel stochastic EMS. The proposed EMS design is tested in a large, realistic, medium-voltage isolated microgrid test system. For the deterministic case, the results demonstrate the important connection between the microgrid's imbalance, reactive power requirements and optimal dispatch, justifying the need for detailed three-phase models for EMS applications in isolated microgrids. For the stochastic studies, the results show the advantages of using a stochastic MILP formulation to account for uncertainties associated with RE sources, and optimally accommodate system reserves. The computational times in all simulated cases show the feasibility of applying the proposed techniques to real-time, autonomous dispatch of isolated microgrids with variable RE sources.

Pas de résumé en anglais

In the recent years, governments have encouraged the utilization of renewable energy by providing incentives to investors, and enhancing traditional practices in the sector. For example, in Ontario, Canada, local distribution companies can now legally own and operate up to 10 MW generating plant per location as long as it is from a renewable source. Although this trend results in some operational benefits for the host networks, it also creates specific technical challenges and economic problems. New modeling approaches are needed to account for the main features of power produced by these facilities, namely, the uncertainty and uncontrollability. The uncertainty of power produced by weather-based generating facilities affects the decisions of different activities related to the operation of distribution systems. Examples of these tasks include power procurement decisions, the assessment of voltage magnitude variation, and reactive power management. If not properly included, uncertainty could result in non optimal outcome of operational activities of a distribution system operator. Based on different optimization techniques, the thesis introduces several models that capture the uncertain behavior of renewable resources. Two operational tasks were selected for application using the enhanced models: economical operation of distribution system and impact assessment on voltage magnitude. The power procurement problem is an operational challenge to acquire the correct economic mix of power purchases to supply the demand of a local distribution company. Three models have been presented to formulate the power procurement problem with a consideration of the stochastic nature of renewable generation. These models select the optimal quantities of bilateral contracts under uncertain renewable generation and give the option to decision makers to recalculate the powers from other sources. In one of these proposed models, the mean-variance theory is utilized to evaluate the risk associated with the variation of renewable power output on the financial efficiency of a local distribution company. Unlike previous studies, in which renewable power production is identified as a decision variable, in this work the generation from these units is represented as a parameter to model their feature of uncontrollability. Comparison of results obtained from using the proposed models showed that the degree of uncertainty plays an important role in selecting the proper mix. In general, stochastic based algorithms are superior to deterministic approaches when increasing contributions from renewable resources are considered. A major technical problem that may be caused by the uncertain generation of renewable units is the increase of voltage variation. The second part of the thesis introduces a methodology based on a Monte-Carlo technique to assess new installation depending on its impact on the quality of supply voltage. Two different standard measures for supply voltage quality are applied in this approach to provide the decision maker a tool that can be used to authorize new connections of renewable generation. The consistency of results obtained by the two indices applied in the proposed methodology encourages adopting the proposed approach for evaluating the impact of new connections of renewable resources. The models proposed in the thesis contribute to promote safer integration of renewable resources in distribution systems by modeling two main features: uncertainty and non-controllability.

[pt] Estudamos aproximações amostrais de problemas com restrições probabilísticas através da aproximação pela média amostral (SAA) e demonstramos as propriedades de convergência relacionadas. Utilizamos SAA para obter bons candidatos à solução e cotas estatísticas para o valor ótimo do problema original. Para ajustar corretamente parâmetros, aplicamos o método a dois problemas com restrições probabilísticas. O primeiro é um problema de seleção de portfolio linear com retornos seguindo uma distribuição lognormal multivariada. O segundo é uma versão com restrições probabilísticas conjuntas de um problema da mistura simplificado. Concluímos com uma aplicação mais exigente ao problema de se determinar a provisão mínima que um agente econômico deve ter de forma a satisfazer uma série de obrigações futuras com probabilidade suficientemente alta. / [en] We study sample approximations of chance constrained problems through the sample average approximation (SAA) approach and prove the related convergence properties. We discuss how to use the SAA method to obtain good candidate solutions and bounds for the optimal value of the original problem. In order to tune the parameters of SAA, we apply the method to two chance constrained problems. The first is a linear portfolio selection problem with returns following a multivariate lognormal distribution. The second is a joint chance constrained version of a simple blending problem. We conclude with a more demanding application of SAA methodology to the determination of the minimum provision an economic agent must have in order to meet a series of future payment obligations with sufficiently high probability.

Certaines opérations de maintenance sur sites industriels nécessitent une planification à partir de modèles numériques 3D des scènes où se déroulent les interventions. Pour permettre la simulation de ces opérations, les modèles 3D utilisés doivent représenter fidèlement la réalité du terrain. Ces représentations virtuelles sont habituellement construites à partir de nuages de points relevés sur le site, constituant une description métrologique exacte de l’environnement sans toutefois fournir une description géométrique de haut niveau.Il existe une grande quantité de travaux abordant le problème de la reconstruction de modèles 3D à partir de nuages de points, mais peu sont en mesure de fournir des résultats suffisamment fiables dans un contexte industriel et cette tâche nécessite en pratique l’intervention d’opérateurs humains.Les travaux réalisés dans le cadre de cette thèse visent l’automatisation de la reconstruction,avec comme principal objectif la fiabilité des résultats obtenus à l’issu du processus. Au vu de la complexité de ce problème, nous proposons d’exploiter des connaissances et données a priori pour guider la reconstruction. Le premier a priori concerne la compositiondes modèles 3D : en Conception Assistée par Ordinateur (CAO), les scènes industrielles sont couramment décrites comme des assemblages de primitives géométriques simples telles que les plans, sphères, cylindres, cônes, tores, etc. Nous hiérarchisons l’analyse en traitant dans un premier temps les plans et les cylindres, comme un préalable à la détection de stores. On obtient ainsi une description fiable des principaux composants d’intérêt dans les environnements industriels. Nous proposons en outre d’exploiter un certain nombre de règles régissant la manière dont ces primitives s’assemblent en un modèle CAO, basées surdes connaissances ”métier” caractérisant les scènes industrielles que nous traitons. De plus,nous tirons parti d’un modèle CAO existant d´ecrivant une scène similaire à celle que nous souhaitons reconstruire, provenant typiquement de la reconstruction antérieure d’un site semblable au site d’intérêt. Bien que semblables en théorie, ces scènes peuvent présenterdes différences significatives qui s’accentuent au cours de leur exploitation.La méthode que nous développons se fonde sur une formulation Bayésienne du problème de reconstruction : il s’agit de retrouver le modèle CAO le plus probable vis à visdes différentes attentes portées par les données et les a priori sur le modèle à reconstruire. Les diverses sources d’a priori s’expriment naturellement dans cette formulation. Pour permettre la recherche du modèle CAO optimal, nous proposons une approche basée surdes tentatives d’insertion d’objets générés aléatoirement. L’acceptation ou le rejet de ces objets repose ensuite sur l’am´elioration systématique de la solution en cours de construction. Le modèle CAO se construit ainsi progressivement, par ajout et suppression d’objets, jusqu’à obtention d’une solution localement optimale. / 3D models are often used in order to plan the maintenance of industrial environments.When it comes to the simulation of maintenance interventions, these 3D models have todescribe accurately the actual state of the scenes they stand for. These representationsare usually built from 3D point clouds that are huge set of 3D measurements acquiredin industrial sites, which guarantees the accuracy of the resulting 3D model. Althoughthere exists many works addressing the reconstruction problem, there is no solution toour knowledge which can provide results that are reliable enough to be further used inindustrial applications. Therefore this task is in fact handled by human experts nowadays.This thesis aims at providing a solution automating the reconstruction of industrialsites from 3D point clouds and providing highly reliable results. For that purpose, ourapproach relies on some available a priori knowledge and data about the scene to beprocessed. First, we consider that the 3D models of industrial sites are made of simpleprimitive shapes. Indeed, in the Computer Aided Design (CAD) field, this kind of scenesare described as assemblies of shapes such as planes, spheres, cylinders, cones, tori, . . . Ourown work focuses on planes, cylinders and tori since these three kind of shapes allow thedescription of most of the main components in industrial environment. Furthermore, weset some a priori rules about the way shapes should be assembled in a CAD model standingfor an industrial facility, which are based on expert knowledge about these environments.Eventually, we suppose that a CAD model standing for a scene which is similar to theone to be processed is available. This a priori CAO model typically comes from the priorreconstruction of a scene which looks like the one we are interested in. Despite the factthat they are similar theoretically, there may be significant differences between the sitessince each one has its own life cycle.Our work first states the reconstruction task as a Bayesian problem in which we haveto find the most probable CAD Model with respect to both the point cloud and the a prioriexpectations. In order to reach the CAD model maximizing the target probability, wepropose an iterative approach which improves the solution under construction each time anew randomly generated shape is tried to be inserted in it. Thus, the CAD model is builtstep by step by adding and removing shapes, until the algorithm gets to a local maximumof the target probability.

This thesis studies a classic single-period stochastic optimization problem called the newsvendor problem. A news-boy must decide how many items to order un- der the random demand. The simple model is extended in the following ways: endogenous demand in the additive and multiplicative manner, objective func- tion composed of the expected value and Conditional Value at Risk (CVaR) of profit, multicriteria objective with price-dependent demand, multiproduct exten- sion under dependent and independent demands, distributional robustness. In most cases, the optimal solution is provided. The thesis concludes with the nu- merical study that compares results of two models after applying the Sample Average Approximation (SAA) method. This study is conducted on the real data. 1

Orientador: Paulo Sergio Franco Barbosa / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-08-09T22:55:20Z (GMT). No. of bitstreams: 1 Zanfelice_FabioRogerio_M.pdf: 1622123 bytes, checksum: a97f37ded8557a9e73605dcce3e6b3ea (MD5) Previous issue date: 2007 / Resumo: A evolução deste trabalho de pesquisa foi pautada pelo desenvolvimento de uma metodologia que represente a atividade de comercialização de empresas de distribuição e comercialização de energia e indique o posicionamento destes agentes frente às alternativas e condições do mercado a fim de que seja atingida a estratégia ótima de comercialização que minimize o custo de aquisição de energia e os riscos associados ao negócio. Dessa forma, é necessário conhecer a legislação da indústria de energia elétrica brasileira, as características dos relacionamentos comerciais entre as empresas que constituem esta indústria e os riscos e oportunidades do mercado. Neste sentido, desenvolveu-se um modelo de otimização estocástica utilizando-se a técnica de programação mista onde foram representadas as restrições operacionais e regulatórias, intrínsecas ao processo de compra e venda de energia, bem como as várias alternativas de contratação futura das empresas distribuidoras, via leilões regulados, como determina a legislação vigente. Os resultados obtidos de alocação da energia contratada e indicação dos produtos que indicam a forma ótima de contratação foram analisados e apresentaram consistência mostrando-se coerentes para aplicação. / Abstract: This research work has been developed in order to propose a methodology to represent the trading activities of the distribution utilities and energy trading companies, and also to provide guidelines to these companies when facing market conditions aiming at achieve optimal trading strategies addressing cost and risk minimization. For this purpose, it is required to have an general understanding about regulatory issues of the Brazilian electricity industry, the trading relationships among companies running their businesses in this industry, risks and opportunities in this market. A sthocastic optimzation model has been developed by using a linear programming model where the main regulatory and operational constraints are included, all related to the energy buying and selling process, as well as those ones related to the future energy contracting needs through regulated auctions based on the present regulation. The optimal results defining contracted energy and options for future contracting alternatives along the planning horizon were reviewed, and they revealed coherent for real-world applications. The information generated by this research work is very important for companies or holding groups that run their businesses in the Brazilian power market, since it provides a decision support system to formulate energy buying and selling strategies based on optimal resource allocation. / Mestrado / Mestre em Planejamento de Sistemas Energéticos

L'approche de la biologie des systèmes vise à intégrer les méthodologies appliquées dans la conception et l'analyse des systèmes technologiques complexes, au sein de la biologie afin de comprendre les principes de fonctionnement globaux des systèmes biologiques. La thèse s'inscrit dans le cadre de la biologie des systèmes et en particulier dans la prolongation d'une méthode issue de ce cadre : la méthode Resource Blance Analysis (RBA). Nous visons dans cette thèse à augmenter le pouvoir prédictif de la méthode via un travail de modélisation tout en gardant un bon compromis entre représentativité des modèles issus de ce cadre et leur résolution numérique efficace. La thèse se décompose en deux grandes parties : la première vise à intégrer les aspects thermodynamiques et cinétiques inhérents aux réseaux métaboliques. La deuxième vise à comprendre l'impact de l'aspect stochastique de la production des enzymes sur le croissance de la bactérie. Des méthodes numériques ont été élaborées pour la résolution des modèles ainsi établis dans les deux cas déterministe et stochastique. / In order to understand the global functioning principals of biological systems, system bio- logy approach aims to integrate the methodologies used in the conception and the analysis of complex technological systems, within the biology. This PhD thesis fits into the system biology framework and in particular the extension of the already existing method Resource Balance Analysis (RBA). We aim in this PhD thesis to improve the predictive power of this method by introducing more complex model. However, this new model should respect a good trade-off between the representativity of the model and its efficient numerical computation. This PhD thesis is decomposed into two major parts. The first part aims the integration of the metabolic network inherent thermodynamical and kinetic aspects. The second part aims the comprehension of the impact of enzyme production stochastic aspect on the bacteria growth. Numerical methods are elaborated to solve the obtained models in both deterministic and stochastic cases.

This thesis deals with the stochastic optimization problem of hydro reservoir manage- ment. External inflows and market electricity price are both considered as random inputs to the model, which is designed as joint chance constrained programming. The main goal of the optimization problem is to maximize the profit from hydro energy usage together with minimizing the cost of used water. The random component is modelled by suitable stochastic processes based on historical data and then approximated via scenarios. Sea- sonal deterministic model is another model that is presented in this thesis. This model helps appraise water stored in every each reservoir's compartment. The estimates of water values are based on dual variables. Finally, in the practical part the hydro reservoir ma- nagement problem is applied to the real hydro valley located on the Vltava river. This part also deals with an option of increasing the number of pumping stations in this particular hydro valley.

[pt] Atualmente, o mundo tem se voltado à promoção do desenvolvimento da energia proveniente de fontes renováveis, pois estas aparecem como uma alternativa para a redução do aquecimento global. No Brasil, as principais fontes de geração renováveis de energia são: geração de cogeração à bagaço de cana-deaçúcar (biomassa), eólica e pequenas centrais hidrelétricas (PCH). Atualmente, o grande desafio enfrentado por elas é comercialização de contratos lastreados em perfis de geração que, apesar de exibirem um baixo fator de emissões, são extremamente sazonais e incertos. Contudo, sabe-se que existe uma relevante complementaridade entre a disponibilidade dos seus recursos (colheita da cana, vento e hidrologia), que como consequência, promove a possibilidade de um ganho sinérgico com a formação de um portfolio contendo tais fontes. A sinergia entre os perfis de geração de uma biomassa e uma PCH foi recentemente estudada através de um modelo de otimização de portfolio com aversão a risco. Esta dissertação tem dois objetivos: (i) estender o modelo de comercialização integrada de fontes renováveis para considerar também a fonte de geração eólica, e (ii) utilizá-lo para definir a estratégia ótima de oferta (compra) da comercializadora em um leilão de compra de contratos de fontes renováveis no ACL. Dois formatos de leilão serão testados e comparados tanto em termos de benefício para a comercializadora, como em termos de participação final de cada fonte. Por fim, o objetivo (ii) preenche uma lacuna na literatura correspondente a ausência de modelos de oferta estratégica avessa a risco em leilões de contratos por parte da demanda. / [en] Nowadays, the world has turned to promoting the development of energy from renewable sources, because they appear as an alternative to reducing global warming. In Brazil, the main renewable energy sources are the thermoelectric cogeneration of sugarcane bagasse (biomass), wind power and small hydro resources. Besides, the major challenge faced by them is the contracts trade guaranteed by generation profiles that, despite exhibiting a low emission factor, are highly seasonal and uncertain. However, it is known that there is an important complementarity between the availability of resources (sugar cane harvesting, wind and hydrology), which as a consequence, promotes the ability to gain a synergistic effect with the formation of a portfolio containing such sources. The synergy between the resources availability profile of biomass and a small hydro was recently studied by a model of portfolio optimization with risk aversion. This work has two objectives: (i) extend the portfolio trade model of renewable sources to consider also the a wind generation power plant, and (ii) define an optimal strategic bidding (demand side) for a trading company on a contract auction for renewable sources. Two auction formats will be tested and compared in terms of benefit to the energy trading company. Finally, the objective (ii) fills a gap in the literature corresponding to the absence of risk-averse bidding models for contract auctions on the demand side.