Dans un marché électrique concurrentiel, EDF a adapté ses outils de gestion de production pour permettre une gestion optimale de son portefeuille, particulièrement sur les horizons journaliers et infra-journaliers, derniers leviers pour une gestion optimisée de la production. Et plus l'horizon d'optimisation s'approche du temps réel, plus les décisions prises aux instants précédents deviennent structurantes voire limitantes en terme d'actions. Ces décisions sont aujourd'hui prises sans tenir compte du caractère aléatoire de certaines entrées du modèle. En effet, pour les décisions à court-terme, la finesse et la complexité des modèles déjà dans le cas déterministe ont souvent été un frein à des travaux sur des modèles tenant compte de l'incertitude. Pour se prémunir face à ces aléas, des techniques d'optimisation en contexte incertain ont fait l'objet des travaux de cette thèse. Nous avons ainsi proposé un modèle robuste de placement de la production tenant compte des incertitudes sur la demande en puissance. Nous avons construit pour cette fin un ensemble d'incertitude permettant une description fine de l'aléa sur les prévisions de demande en puissance. Le choix d'indicateurs fonctionnels et statistiques a permis d'écrire cet ensemble comme un polyèdre d'incertitude. L'approche robuste prend en compte la notion de coût d'ajustement face à l'aléa. Le modèle a pour objectif de minimiser les coûts de production et les pires coûts induits par l'incertitude. Ces coûts d'ajustement peuvent décrire différents contextes opérationnels. Une application du modèle robuste à deux contextes métier est menée avec un calcul du coût d'ajustement approprié à chaque contexte. Enfin, le présent travail de recherche se situe, à notre connaissance, comme l'un des premiers dans le domaine de la gestion optimisée de la production électrique à court terme avec prise en compte de l'incertitude. Les résultats sont par ailleurs susceptibles d'ouvrir la voie vers de nouvelles approches du problème. / Robust Optimization is an approach typically offered as a counterpoint to Stochastic Programming to deal with uncertainty, especially because it doesn't require any precise information on stochastic distributions of data. In the present work, we deal with challenging unit-commitment problem for the French daily electricity production under demand uncertainty. Our contributions concern both uncertainty modelling and original robust formulation of unit-commitment problem. We worked on a polyhedral set to describe demand uncertainty, using statistical tools and operational indicators. In terms of modelling, we proposed robust solutions that minimize production and worst adjustment costs due to uncertainty observation. We study robust solutions under two different operational contexts. Encouraging results to the convex unit-commitment problems under uncertainty are thus obtained, with intersting research topics for future work.

A ascensão de um grande número de pessoas em países em desenvolvimento para a classe média, no inicio do século XXI, aliado ao movimento político para transferência de base energética para os biocombustíveis vêm aumentando a pressão sobre os preços das commodities agrícolas e apresentando novas oportunidades e cenários administrativos para os produtores agrícolas dessas commodities, em especial aquelas que podem se diversificar em muitos subprodutos para atender diferentes mercados, como o de alimentos, químico, têxtil e de energia. Nesse novo ambiente os produtores podem se beneficiar dividindo adequadamente a produção entre os diferentes subprodutos, definindo o melhor momento para a comercialização através de estoques, e ainda controlar sua exposição ao risco através de posições no mercado de derivativos. A literatura atual pouco aborda o tema da diversificação e seu impacto nas decisões de produção e comercialização agrícola e portanto essa tese tem o objetivo de propor um modelo de decisão fundado na teoria de seleção de portfólios capaz de decidir a divisão da produção entre diversos subprodutos, as proporções a serem estocadas e o momento mais adequado para a comercialização e por fim as posições em contratos futuros para fins de proteção ou hedge. Adicionalmente essa tese busca propor que esse modelo seja capaz de lidar com incerteza em parâmetros, em especial parâmetros que provocam alto impacto nos resultados, como é o caso dos retornos previstos no futuro. Como uma terceira contribuição, esse trabalho busca ainda propor um modelo de previsão de preços mais sofisticado que possa ser aplicado a commodities agrícolas, em especial um modelo híbrido ou hierárquico, composto de dois modelos, um primeiro modelo fundado sob a teoria de processos estocásticos e do Filtro de Kalman e um segundo modelo, para refinar os resultados do primeiro modelo de previsão, baseado na teoria de redes neurais, com a finalidade de considerar variáveis exógenas. O modelo híbrido de previsão de preços foi testado com dados reais do mercado sucroalcooleiro brasileiro e indiano, gerando resultados promissores, enquanto o modelo de decisão de parâmetros de produção, comercialização, estocagem e hedge se mostrou uma ferramenta útil para suporte a decisão após ser testado com dados reais do mercado sucroalcooleiro brasileiro e do mercado de milho, etanol e biodiesel norte-americano. / The rise of a large number of people in developing countries for the middle class at the beginning of the century, combined with the political movement to transfer the energy base for biofuels has been increasing pressure on prices of agricultural commodities and presenting new opportunities and administrative scenarios for agricultural producers of these commodities, especially those who may diversify into many products to meet different markets such as food, chemicals, textiles and energy. In this new environment producers can achieve benefits properly dividing production between different products, setting the best time to market through inventories, and still control their risk exposure through positions in the derivatives market. The literature poorly addresses the issue of diversification and its impact on agricultural production and commercialization decisions and therefore this thesis aims to propose a decision model based on the theory of portfolio selection able to decide the division of production between different products, the proportions to be stored and timing for marketing and finally the positions in futures contracts to hedge. Additionally this thesis attempts to propose that this model is capable of dealing with uncertainty in parameters, especially parameters that cause high impact on the results, as is the case of expected returns in the future. As a third contribution this paper seeks to also propose a model more sophisticated to forecast prices that can be applied to agricultural commodities, especially a hybrid or hierarchical model, composed of two models, a first one based on the theory of stochastic processes and Kalman filter and a second one to refine the results of the first prediction model, based on the theory of neural networks in order to consider the exogenous variables. The hybrid model for forecasting prices has been tested with real data from the Brazilian and Indian sugar ethanol market, generating promising results, while the decision model parameters of production, commercialization, storage and hedge proved a useful tool for decision support after being tested with real data from Brazilian sugar ethanol market and the corn, ethanol and biodiesel market in U.S.A.

Orientador: Alberto Luiz Francato / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-23T22:42:20Z (GMT). No. of bitstreams: 1 Deantoni_VictordeBarros_M.pdf: 2817178 bytes, checksum: 1c05a50aa818606a82c052b88f62c14d (MD5) Previous issue date: 2013 / Resumo: O planejamento da operação do setor elétrico brasileiro é realizado com base em modelos que por meio de otimização determinam a geração de energia de fontes térmicas e hidráulicas. Utilizando a técnica de otimização estocástica robusta possibilita-se a análise com um conjunto de cenários históricos, com o objetivo de determinar a operação do primeiro intervalo de tempo do horizonte de planejamento, e assim obter uma solução que não necessariamente seja a melhor para um determinado cenário, mas sim uma solução que seja interessante para qualquer um dos cenários que possam ocorrer. O objetivo deste trabalho foi criar uma nova versão do modelo SolverSIN com um módulo de otimização estocástica robusta, chamado de SolverSINR, esse novo modelo permite o planejamento da operação considerando um conjunto de cenários históricos. As séries históricas são obtidas do relatório do programa mensal de operação, que é um arquivo de saída do NEWAVE. Para organização dessas séries foi desenvolvido um aplicativo chamado SHENA. O novo modelo apresentou resultados coerentes em comparação com o modelo SolverSIN determinístico e mostrou valores viáveis para a operação de reservatórios. A ferramenta permite a otimização assumindo a hipótese de repetição de uma série histórica. O modelo SolverSINR vem a contribuir como mais uma alternativa de avaliação crítica às políticas operacionais do SIN implementadas pelos modelos oficiais do SEB. Durante a avaliação de resultados notou-se que uma restrição de geração hidráulica mínima para o subsistema Nordeste causava infactibilidade em alguns cenários, adotou-se uma variável de folga para solucionar esse problema. Destaca-se que houve factibilidade nos procedimentos de operação em tempo de processamento compatível com otimização estocástica de processos em equipamentos usais para tal tarefa / Abstract: The operation planning of the interconnected power generation Brazilian system is carried out based on models through deterministic optimization power generation from thermal and hydraulic sources. Using the robust stochastic optimization technique enables the analysis grounded in a set of historical scenarios, in order to determine the operation of the first time interval of the planning horizon, and thus obtain a solution that is not necessarily the best for a selected scenario, but a solution that is interesting for any of the scenarios that might happen. The objective of this work was to create a new version of the model SolverSIN with a robust stochastic optimization module, called SolverSINR , this new model allows for the operation planning considering a set of historical scenarios . The time series is obtained from the monthly report called "pmo.dat" that is an output file from NEWAVE model. For organizing these series was developed an application called SHENA. The new model showed consistent results in comparison with the deterministic model (SolverSIN) and showed feasible values for reservoir operation. The tool allows the optimization under the assumption of repetition of a historical series. The model SolverSINR comes to contribute as an alternative assessment to critical operational policies implemented by SIN official models of SEB. During the evaluation of results was noted that a restriction of minimum hydraulic generation on Northeast subsystem caused infeasible answer in some scenarios, we adopted a penalty variable to solve this problem. It is noteworthy that there was feasibility in operating procedures in processing time compatible with stochastic optimization of processes in usual equipment for this task / Mestrado / Recursos Hidricos, Energeticos e Ambientais / Mestre em Engenharia Civil

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Transportation system is one of the key functioning components of the modern society and plays an important role in the circulation of commodity and growth of economy. Transportation system is not only the major influencing factor of the efficiency of large-scale complex industrial logistics, but also closely related to everyone’s daily life. The goals of an ideal transportation system are focused on improving mobility, accessibility, safety, enhancing the coordination of different transportation modals and reducing the impact on the environment, all these activities require sophisticated design and plan that consider different factors, balance tradeoffs and maintaining efficiency. Hence, the design and planning of transportation system are strongly considered to be the most critical problems in transportation research. Transportation system planning and design is a sequential procedure which generally contains two levels: strategic and operational. This dissertation conducts extensive research covering both levels, on the strategic planning level, two network design problems are studied and on the operational level, routing and scheduling problems are analyzed. The main objective of this study is utilizing operations research techniques to generate and provide managerial decision supports in designing reliable and efficient transportation system. Specifically, three practical problems in transportation system design and operations are explored. First, we collaborate with a public transit company to study the bus scheduling problem for a bus fleet with multiples types of vehicles. By considering different cost characteristics, we develop integer program and exact algorithm to efficiently solve the problem. Next, we examine the network design problem in emergency medical service and develop a novel two stage robust optimization framework to deal with uncertainty, then propose an approximate algorithm which is fast and efficient in solving practical instance. Finally, we investigate the major drawback of vehicle sharing program network design problem in previous research and provide a counterintuitive finding that could result in unrealistic solution. A new pessimistic model as well as a customized computational scheme are then introduced. We benchmark the performance of new model with existing model on several prototypical network structures. The results show that our proposed models and solution methods offer powerful decision support tools for decision makers to design, build and maintain efficient and reliable transportation systems.

Hydro power producers are faced with the task of releasing water from the reservoirs in the right time. To do this there are tools using stochastic optimization that aims at maximizing the income of that producer. The existing methods have a high computing time and grow exponentially with the size of the problem. A new approach that uses linear decision rules is investigated in this thesis to see if it is possible to maintain the same quality of the solutions and in the same time decrease run times. With this method the hydro power producer receives policies as an affine function of the realization of the uncertainty variables in inflow and price. This thesis presents a deterministic model and then converts it into an linear decision rules, LDR, model. It also presents a way to model the uncertainty in both inflow to the reservoir and the spot price. The result is that the LDR approach generates reasonable policies with low run times but loses a lot of optimality compared to solutions that are used today. Therefore it is concluded that this approach needs further development before commercial use. The work described in this thesis has been done in cooperation with three master students at NTNU. The approach of using linear decision rules are the same in the two projects but the differences are the models evaluated.

This dissertation demonstrates novel holistic approaches on how to link policy, clean energy innovations, and robust energy modeling techniques to help build more resilient and cost-effective energy systems for the Canadian Arctic region and remote communities in general. In spite of the diversity among Arctic jurisdictions, various energy issues and challenges are shared pan-territorially in the North. For instance, 53 out of 80 remote communities in the Northern territories rely exclusively on diesel-based infrastructures to generate electricity, with heating oil as their primary source of heat. This critical dependence on fossil fuels exposes the Indigenous peoples and other Canadians living in the North to high energy costs and environmental vulnerabilities which is exacerbated by the local and global catastrophic effects of climate change in the Arctic. Aside from being strong point sources of greenhouse gases and other airborne pollutants, this reliance on carbon-intensive sources of energy elevates risk of oils spills during fuel transport and storage. Further, conventional transportation mode via ice roads is now increasingly unreliable because of the rising Arctic temperatures which is twice the global average rate. As a result, most fuels are being transported by small planes which contribute to high energy costs and fuel poverty rates, or via boats which also increases the risk of oil spills in the Arctic waters. Methodologically, this thesis presents a multi-domain perspective on how to accelerate energy transitions among Northern remote communities. In particular, a multi-objective optimization energy model was developed in order to capture complex trade-offs in designing integrated electrical and thermal energy systems. In comparison with traditional single-objective optimization approach, this technique offers diversity of solutions to represent multiple energy solution philosophies from various stakeholders and practitioners in the North. A case study in the Northernmost community of the Northwest Territories demonstrates the applicability of this framework - from modeling a range of energy solutions (supply and demand side aspects) to exploring insights and recommendations while taking into account uncertainties. Overall, this dissertation makes a set of contributions, including: (i) Development of a robust energy modeling framework that integrates complex trade-offs and multiple overlapping uncertainties in designing energy systems for the Arctic and remote communities in general; (ii) Extension of previous Arctic studies - where focused has solely been on the electricity sector - by integrating heating technology options in the proposed modeling framework in conjunction with methods on obtaining `high performance' buildings in the North; (iii) Overall energy system performance evaluation when integrating heat and electricity sectors, as well as the role of battery storage systems and diesel generator on facilitating variable renewable energy generation among isolated communities; (iv) Formulation of a community-scale energy trilemma index model which helps design policies that are accelerating (or hindering) energy transitions among remote communities by assessing quantitatively challenges relating to energy security, affordability, and environmental sustainability; (v) Synthesized holistic insights and recommendations on how to create opportunities for Indigenous peoples-led energy projects while discussing interwoven links between energy system operations, relationship building and stakeholders engagement, policy design, and research (energy modeling and analysis). Collectively, the new methods and recommendations demonstrated herein offer evidence-based decision making and innovative solutions for policy makers, utility companies, Indigenous peoples, and other stakeholders in the Arctic and beyond. / Graduate

In this thesis we study some storage loading problems motivated from several practical contexts, under different types of uncertainty on the items’ data. To have robust stacking solutions against the data uncertainty, we apply the concepts of strict and adjustable robustness. We first give complexity results for various storage loading problems with stacking constraints, and point out some interesting settings in which the adjustable robust problems can be solved more efficiently than the strict ones. Then we propose different solution algorithms for the robust storage loading problems, and figure out which algorithm performs best for which data setting. We also propose a robust optimization framework dealing with storage loading problems under stochastic uncertainty. In this framework, we offer several rule-based ways of scenario generation to derive different uncertainty sets, and analyze the trade-off between cost and robustness of the robust stacking solutions. Additionally, we introduce a novel approach in dealing with stability issues of stacking configurations. Our key idea is to impose a limited payload on each item depending on its weight. We then study a storage loading problem with the interaction of stacking and payload constraints, as well as uncertainty on the weights of items, and propose different solution approaches for the robust problems.

Plusieurs problèmes d'ordonnancement cyclique ont été étudiés dans la littérature. Cependant, la plupart de ces travaux considèrent que les paramètres sont connus avec certitude et ne prennent pas en compte les différents aléas qui peuvent survenir. Par ailleurs, un ordonnancement optimal pour un problème déterministe peut très vite devenir le pire ordonnancement en présence d'incertitude. Parmi les incertitudes que nous pouvons rencontrer dans les problèmes d'ordonnancement, la variation des durées des tâches par rapport au valeurs estimées, pannes des machines, incorporation de nouvelles tâches qui ne sont pas considérées au départ, etc. Dans cette thèse, nous étudions des problèmes d'ordonnancement cyclique où les durées des tâches sont affectées par des incertitudes. Ces dernières sont décrites par un ensemble d'incertitude où les durées des tâches sont supposées appartenir à des intervalles et le nombre de déviations par rapport aux valeurs nominales est contrôlé par un paramètre appelé budget d'incertitude. Nous étudions deux problèmes en particulier. Le premier est le problème d'ordonnancement cyclique de base (BCSP). Nous formulons celui-ci comme un problème d'optimisation robuste bi-niveau et, à partir des propriétés de cette formulation, nous proposons différents algorithmes pour le résoudre. Le deuxième problème considéré est le problème du jobshop cyclique. De manière similaire au BSCP, nous proposons une formulation en termes de problème d'optimisation bi-niveau et, en exploitant les algorithmes développés pour le problème d'ordonnancement cyclique de base, nous développons un algorithme de Branch-and-Bound pour le résoudre. Afin d'évaluer l'efficacité de notre méthode nous l'avons comparé à des méthodes de décomposition qui existent dans la littérature pour ce type de problèmes. Enfin, nous avons étudié une version du problème du jobshop cyclique où les durées des tâches prennent des valeurs dans des intervalles d'une manière uniforme et dont l'objectif est de minimiser la valeur moyenne du temps de cycle. Pour résoudre ce problème nous avons adopté un algorithme de Branch-and-Bound où chaque sous-problème de l'arbre de recherche consiste à calculer le volume d'un polytope. Enfin, pour montrer l'efficacité de chacune de ses méthodes, des résultats numériques sont présentés. / Several studies on cyclic scheduling problems have been presented in the literature. However, most of them consider that the problem parameters are deterministic and do not consider possible uncertainties on these parameters. However, the best solution for a deterministic problem can quickly become the worst one in the presence of uncertainties, involving bad schedules or infeasibilities. Many sources of uncertainty can be encountered in scheduling problems, for example, activity durations can decrease or increase, machines can break down, new activities can be incorporated, etc. In this PhD thesis, we focus on scheduling problems that are cyclic and where activity durations are affected by uncertainties. More precisely, we consider an uncertainty set where each task duration belongs to an interval, and the number of parameters that can deviate from their nominal values is bounded by a parameter called budget of uncertainty. This parameter allows us to control the degree of conservatism of the resulting schedule. In particular, we study two cyclic scheduling problems. The first one is the basic cyclic scheduling problem (BCSP). We formulate the problem as a two-stage robust optimization problem and, using the properties of this formulation, we propose three algorithms to solve it. The second considered problem is the cyclic jobshop problem (CJSP). As for the BCSP, we formulate the problem as two-stage robust optimization problem and by exploiting the algorithms proposed for the robust BCSP we propose a Branch-and-Bound algorithm to solve it. In order to evaluate the efficiency of our method, we compared it with classical decomposition methods for two-stage robust optimization problems that exist in the literature. We also studied a version of the CJSP where each task duration takes uniformly values within an interval and where the objective is to minimize the mean value of the cycle time. In order to solve the problem, we adapted the Branch-and-Bound algorithm where in each node of the search tree, the problem to be solved is the computation of a volume of a polytope. Numerical experiments assess the efficiency of the proposed methods.

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