In this thesis we study the problem of road taxation. This problem consists in finding the toll on the roads belonging to the government or a private company in order to maximize the revenue. An optimal taxation policy consists in determining level of tolls low enough to favor the use of toll arcs, and high enough to get important revenues. Since there are twolevels of decision, the problem is formulated as a bilevel bilinear program.

In response to growing concerns about the negative impact of GHG emissions, several countries such as the European Union have adopted a cap-and-trade policy to limit the overall emissions levels. Alternatively, other countries including Argentina, Canada, the United Kingdom, and United States have proposed an intensity-based cap-and-trade system that targets emission intensities, measured in emissions per dollars or unit of output. Arguably,intensity regulations can accommodate future economic growth, reduce cost uncertainty, engage developing countries in international efforts to mitigate climate change, and provide incentives to improve energy efficiency and to use less carbon-intensive fuels. This work models and studies a carbon tax scheme where policy makers set a target emission factor, which is used as an intensity measure, for a specific industry and tax firms if they exceed that limit. The policy aims to promote energy efficiency, alleviate the impact on low emitters, and allow high emitters some flexibility to comply. We examine the effectiveness of the policy in reducing the emission factor due to manufacturing and transportation. The major objective of this research is to provide policy makers with a decision support tool that can aid in investigating the impact of an intensity-based carbon tax on regulated sectors and in finding the tax rate that achieves a target reduction. Therefore, we first propose a social-welfare maximizing model that can serve as a tool to evaluate the economic and environmental impacts of the policy. We compare the outcomes of the intensity-based tax and other existing environmental policies; namely, carbon tax imposed on overall emissions, cap-and-trade systems, and mandatory caps using case studies that are built within the context of the cement industry. The effectiveness of the policy is measured by achieving a balance between the target emission factor and the social welfare. To find the optimal tax rate that achieves a target reduction, we propose a bilevel programming model where at the upper level, the government sets a target emission factor for the industry and taxes firms if they exceed that target, and at the lower level, the industry sets output levels that maximize social welfare. In the design of the policy, the government takes into account the decisions of the producers regarding fuel types and production quantities as well as the decisions of the market regarding demand. To evaluate the effectiveness of the policy, we build case studies in the context of cement industry. The policy is found to be effective in reducing the CO2 emissions by opting for a less carbon-intensive fuel with a little impact on social welfare. To examine the effectiveness of the intensity-based carbon tax on reducing CO2 emissions from transportation, which is a major supply chain activity, we finally propose a bilevel program where at the upper level the government decides on the tax rate and at the lower level firms decide on the design of their supply chain and truck types. The policy is found to be effective in inducing firms to reduce their emission factors and consequently reducing the overall emissions.

In response to growing concerns about the negative impact of GHG emissions, several countries such as the European Union have adopted a cap-and-trade policy to limit the overall emissions levels. Alternatively, other countries including Argentina, Canada, the United Kingdom, and United States have proposed an intensity-based cap-and-trade system that targets emission intensities, measured in emissions per dollars or unit of output. Arguably,intensity regulations can accommodate future economic growth, reduce cost uncertainty, engage developing countries in international efforts to mitigate climate change, and provide incentives to improve energy efficiency and to use less carbon-intensive fuels. This work models and studies a carbon tax scheme where policy makers set a target emission factor, which is used as an intensity measure, for a specific industry and tax firms if they exceed that limit. The policy aims to promote energy efficiency, alleviate the impact on low emitters, and allow high emitters some flexibility to comply. We examine the effectiveness of the policy in reducing the emission factor due to manufacturing and transportation. The major objective of this research is to provide policy makers with a decision support tool that can aid in investigating the impact of an intensity-based carbon tax on regulated sectors and in finding the tax rate that achieves a target reduction. Therefore, we first propose a social-welfare maximizing model that can serve as a tool to evaluate the economic and environmental impacts of the policy. We compare the outcomes of the intensity-based tax and other existing environmental policies; namely, carbon tax imposed on overall emissions, cap-and-trade systems, and mandatory caps using case studies that are built within the context of the cement industry. The effectiveness of the policy is measured by achieving a balance between the target emission factor and the social welfare. To find the optimal tax rate that achieves a target reduction, we propose a bilevel programming model where at the upper level, the government sets a target emission factor for the industry and taxes firms if they exceed that target, and at the lower level, the industry sets output levels that maximize social welfare. In the design of the policy, the government takes into account the decisions of the producers regarding fuel types and production quantities as well as the decisions of the market regarding demand. To evaluate the effectiveness of the policy, we build case studies in the context of cement industry. The policy is found to be effective in reducing the CO2 emissions by opting for a less carbon-intensive fuel with a little impact on social welfare. To examine the effectiveness of the intensity-based carbon tax on reducing CO2 emissions from transportation, which is a major supply chain activity, we finally propose a bilevel program where at the upper level the government decides on the tax rate and at the lower level firms decide on the design of their supply chain and truck types. The policy is found to be effective in inducing firms to reduce their emission factors and consequently reducing the overall emissions.

In this dissertation, an important homeland security problem is studied. With the focus on wildfire and pyro-terrorism management. We begin the dissertation by studying the vulnerability of landscapes to pyro-terrorism. We develop a maximal covering based optimization model to investigate the impact of a pyro-terror attack on landscapes based on the ignition locations of fires. We use three test case landscapes for experimentation. We compare the impact of a pyro-terror wildfire with the impacts of naturally-caused wildfires with randomly located ignition points. Our results indicate that a pyro-terror attack, on average, has more than twice the impact on landscapes than wildfires with randomly located ignition points. In the next chapter, we develop a Stackelberg game model, a min-max network interdiction framework that identifies a fuel management schedule that, with limited budget, maximally mitigates the impact of a pyro-terror attack. We develop a decomposition algorithm called MinMaxDA to solve the model for three test case landscapes, located in Western U.S. Our results indicate that fuel management, even when conducted on a small scale (when 2% of a landscape is treated), can mitigate a pyro-terror attack by 14%, on average, comparing to doing nothing. For a fuel management plan with 5%, and 10% budget, it can reduce the damage by 27% and 43% on average. Finally, we extend our study to the problem of suppression response after a pyro-terror attack. We develop a max-min model to identify the vulnerability of initial attack resources when used to fight a pyro-terror attack. We use a test case landscape for experimentation and develop a decomposition algorithm called Bounded Decomposition Algorithm (BDA) to solve the problem since the model has bilevel max-min structure with binary variables in the lower level and therefore not solvable by conventional methods. Our results indicate that although pyro-terror attacks with one ignition point can be controlled with an initial attack, pyro-terror attacks with two and more ignition points may not be controlled by initial attack. Also, a faster response is more promising in controlling pyro-terror fires.

Nowadays, electricity markets are becoming more deregulated, especially development of smart grid and introduction of renewable energy promote regulations of energy markets. On the other hand, the uncertainties of new energy sources and market participants’ bidding bring more challenges to power system operation and transmission system planning. These problems motivate us to study spot price (also called locational marginal pricing) of electricity markets, the strategic bidding of wind power producer as an independent power producer into power market, transmission expansion planning considering wind power investment, and analysis of the maximum loadability of a power grid. The work on probabilistic spot pricing for a utility grid includes renewable wind power generation in a deregulated environment, taking into account both the uncertainty of load forecasting and the randomness of wind speed. Based on the forecasted normal-distributed load and Weibull-distributed wind speed, probabilistic optimal power flow is formulated by including spinning reserve cost associated with wind power plants and emission cost in addition to conventional thermal power plant cost model. Simulations show that the integration of wind power can effectively decrease spot price, also increase the risk of overvoltage. Based on the concept of loacational marginal pricing which is determined by a marketclearing algorithm, further research is conducted on optimal offering strategies for wind power producers participating in a day-ahead market employing a stochastic market-clearing algoivrithm. The proposed procedure to drive strategic offers relies on a stochastic bilevel model: the upper level problem represents the profit maximization of the strategic wind power producer, while the lower level one represents the marketing clearing and the corresponding price formulation aiming to co-optimize both energy and reserve. Thirdly, to improve wind power integration, we propose a bilevel problem incorporating two-stage stochastic programming for transmission expansion planning to accommodate large-scale wind power investments in electricity markets. The model integrates cooptimizations of energy and reserve to deal with uncertainties of wind power production. In the upper level problem, the objective of independent system operator (ISO) modelling transmission investments under uncertain environments is to minimize the transmission and wind power investment cost, and the expected load shedding cost. The lower level problem is composed of a two stage stochastic programming problem for energy schedule and reserve dispatch simultaneously. Case studies are carried out for illustrating the effectiveness of the proposed model. The above market-clearing or power system operation is based on direct current optimal power flow (DC-OPF) model which is a linear problem without reactive power constraints. Power system maximum loadability is a crucial index to determine voltage stability. The fourth work in this thesis proposes a Lagrange semi-definite programming (SDP) method to solve the non-linear and non-convex optimization alternating current (AC) problem of the maximum loadability of security constrained power system. Simulation results from the IEEE three-bus system and IEEE 24-bus Reliability Test System (RTS) show that the proposed method is able to obtain the global optimal solution for the maximum loadability problem. Lastly, we summarize the conclusions from studies on the above mentioned optimization problems of electric power market under modern grid, as well as the influence of wind power integration on power system reliability, and transmission expansion planning, as well as the operations of electricity markets. Meanwhile, we also present some open questions on the related research, such as non-convex constraints in the lower-level problem of a bilevel problem, and integrating N-1 security criterion of transmission planning. / Graduate / lei.ming296@gmail.com

This dissertation studies the competitive dynamics between two non-identical providers competing for customers seeking low-cost and quick service. Providers have generic delay functions where, asdemand received by each provider grows, so does delay in processing customers' requests. Given a pricing or capacity decision by each provider, customers determine the proportion of demand to send to each provider by minimizing generalized cost (monetary cost plus delaycost). This problem is formulated as a bilevel optimization, with providers competing at the upper level subject to the customers' decisions at the lower level. Occurrence of Nash equilibria between the providers is studied.First studied is the providers' problem of making decisions on capacities, while competing for a single customer. Conditions are derived for one provider to claim the entire market share, and for the occurrence of an equilibrium where both providers receive positivedemand. A numerical example in which no equilibrium exists is presented. Both the inelastic and elastic demand cases are studied for this scenario. In a second model, providers make pricing decisions with capacity fixed. Under some assumptions, it is shownthat a Nash equilibrium between providers always exists and a numerical example is presented. These models are then combined, in which providers make capacity decisions where prices equilibrate based on the results from the second model.Two competing customers with demand for a homogeneous product are then introduced, where providers choose prices as they compete for customers. This model is extended to an application along a highway corridor with a high-occupancy/toll (HOT) lane in parallel with a free road and transit line. A government agency chooses the transit service frequency while a private toll operator competes by choosing a toll to charge single-occupancy vehicles who wish to use the HOT lane.This scenario is also modeled as a bilevel program. For the lower level, a new dynamic equilibration process where homogeneous users make mode choice decisions based on previous generalized costs ofusing a particular mode is developed. Two numerical examples are presented showing a unique Nash equilibrium between the providers and an example in which multiple equilibria exist.

Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-02-16Bitstream added on 2014-06-13T19:19:29Z : No. of bitstreams: 1 lopezlezama_jm_dr_ilha.pdf: 1140558 bytes, checksum: 86f0f91a10800cda643c01c4a76fa063 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Nesta tese tem-se por objetivo a avaliação da alocação e preço de contrato de geração distribuida (GD) em ambientes competitivos. O trabalho apresentado é dividido em quatro temas principais. Na primeira parte deste trabalho é apresentado um modelo de e despacho de GD com base em um fluxo de potência ótimo AC multiperíodo. A principal vantagem deste modelo é a avaliação, de forma implícita, do impacto da GD na rede da concessionária. No modelo proposto considera-se um modelo de mercado no qual a a concessionária pode decidir comprar energia do mercado atacadista de energia através a e da subestação, ou alternativamente, das unidades de GD alocadas na sua rede, visando minimizar o pagamento no atendimento da demanda. Na segunda parte deste trabalho apresenta-se uma metodologia para calcular os preço de oferta de contrato da GD mediante programação binível. O modelo proposto considera a interação da concessionária e do proprietário da GD. De um lado, a concessionária procura minimizar o pagamento a a no atendimento da demanda. Para isto, utiliza-se um modelo de despacho simplificado similar ao apresentado na primeira parte deste trabalho. Por outro lado, o proprietário a da GD procura maximizar seus lucros mediante a venda de energia. Ambos os agentes devem cumprir, com certas restrições, por exemplo, a concessionária deve atender toda a demanda respeitando os limites técnicos da rede, enquanto o proprietário da GD deve e a fornecer sua energia considerando os limites mínimos e máximos das unidades de GD. Os a dois problemas de otimização são combinados em um problema de programação binível. A principal contribuição desta abordagem consiste na solução simultânea de dois problemas... / This work aims to the theoretical analysis and computational implementation of the operation and planning of distributed generation (DG) in a competitive framework. This report is divided in four main topics. In the first part of this work a DG dispatch model, based on a multi-period AC optimal power flow is presented. The main advantage of this model lies in the fact that it implicitly considers the impact of DG in the network of the distribution company. The proposed model considers a market model in which the distribution company can decide to purchase energy from the wholesale energy market, through the substation, or alternatively, from the DG units within its network, aiming to minimize the payments incurred in attending the demand. In the second part of this work a methodology to calculate the contract price offers of the DG by means of bilevel pro- gramming is presented. The proposed model considers the interaction of the utility and the owner of the DG. On one hand, the distribution company procures the minimization of the payments incurred in attending the demand. For this a simplified dispatch model, similar to the one presented in the first part of this work, is used. On the other hand, the DG owner procures the maximization of his profits by the selling of energy. Both agents must accomplish with certain constraints, for instance, the distribution company must attend all its demand considering the technical limits of the network, and the owner of the DG must supply his energy considering minimum and maximum limits of the gener- ation units. Both optimization problems are combined into a single bilevel optimization problem. The main contribution of this approach consists in the simultaneous solution of two optimization problems, providing contract prices that benefit both agents. In the third part of this work a Genetic... (Complete abstract click electronic access below)

Resumo: Nesta tese tem-se por objetivo a avaliação da alocação e preço de contrato de geração distribuida (GD) em ambientes competitivos. O trabalho apresentado é dividido em quatro temas principais. Na primeira parte deste trabalho é apresentado um modelo de e despacho de GD com base em um fluxo de potência ótimo AC multiperíodo. A principal vantagem deste modelo é a avaliação, de forma implícita, do impacto da GD na rede da concessionária. No modelo proposto considera-se um modelo de mercado no qual a a concessionária pode decidir comprar energia do mercado atacadista de energia através a e da subestação, ou alternativamente, das unidades de GD alocadas na sua rede, visando minimizar o pagamento no atendimento da demanda. Na segunda parte deste trabalho apresenta-se uma metodologia para calcular os preço de oferta de contrato da GD mediante programação binível. O modelo proposto considera a interação da concessionária e do proprietário da GD. De um lado, a concessionária procura minimizar o pagamento a a no atendimento da demanda. Para isto, utiliza-se um modelo de despacho simplificado similar ao apresentado na primeira parte deste trabalho. Por outro lado, o proprietário a da GD procura maximizar seus lucros mediante a venda de energia. Ambos os agentes devem cumprir, com certas restrições, por exemplo, a concessionária deve atender toda a demanda respeitando os limites técnicos da rede, enquanto o proprietário da GD deve e a fornecer sua energia considerando os limites mínimos e máximos das unidades de GD. Os a dois problemas de otimização são combinados em um problema de programação binível. A principal contribuição desta abordagem consiste na solução simultânea de dois problemas... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work aims to the theoretical analysis and computational implementation of the operation and planning of distributed generation (DG) in a competitive framework. This report is divided in four main topics. In the first part of this work a DG dispatch model, based on a multi-period AC optimal power flow is presented. The main advantage of this model lies in the fact that it implicitly considers the impact of DG in the network of the distribution company. The proposed model considers a market model in which the distribution company can decide to purchase energy from the wholesale energy market, through the substation, or alternatively, from the DG units within its network, aiming to minimize the payments incurred in attending the demand. In the second part of this work a methodology to calculate the contract price offers of the DG by means of bilevel pro- gramming is presented. The proposed model considers the interaction of the utility and the owner of the DG. On one hand, the distribution company procures the minimization of the payments incurred in attending the demand. For this a simplified dispatch model, similar to the one presented in the first part of this work, is used. On the other hand, the DG owner procures the maximization of his profits by the selling of energy. Both agents must accomplish with certain constraints, for instance, the distribution company must attend all its demand considering the technical limits of the network, and the owner of the DG must supply his energy considering minimum and maximum limits of the gener- ation units. Both optimization problems are combined into a single bilevel optimization problem. The main contribution of this approach consists in the simultaneous solution of two optimization problems, providing contract prices that benefit both agents. In the third part of this work a Genetic... (Complete abstract click electronic access below) / Orientador: Antonio Padilha Feltrin / Coorientador: Javier Contreras Sanz / Banca: Jose Roberto Sanches Mantovani / Banca: Carlos Roberto Minussi / Banca: Walmir de Freitas Filho / Banca: Flávio Antonio Becon Lemos / Doutor

Cette thèse est consacrée à une étude approfondie du Rank Pricing Problem (RPP) et de deux généralisations. Le RPP est un problème d'optimisation combinatoire qui vise à fixer le prix des produits d'une entreprise afin de maximiser son profit. Elle concerne les clients à la demande, c'est-à-dire les clients qui sont intéressés par plusieurs produits de l'entreprise, mais qui n'ont l'intention d'en acheter qu'un. Les clients disposent d'un budget fixe et classent les produits qui les intéressent du "meilleur" au "pire". Lorsque l'entreprise fixe les prix, chaque client achètera son produit préféré parmi ceux qu'il peut se permettre. Dans le RPP, nous supposons que les produits sont offerts en quantité illimitée, ce qui convient si l'on considère que l'entreprise a suffisamment de produits pour satisfaire la demande, ou lorsque les produits peuvent être fabriqués rapidement avec un coût négligeable (par exemple, les biens numériques).Cette thèse se compose de quatre chapitres. Le premier est un chapitre d'introduction au problème et aux concepts mathématiques présents dans la thèse, tandis que les trois chapitres suivants se concentrent sur chacun des problèmes étudiés :le RPP et deux généralisations. Ainsi, le troisième chapitre est consacré à l'étude du Rank Pricing Problem with Ties (RPPT). Dans cette extension du problème, nous supposons que les clients peuvent exprimer leur indifférence entre les produits qui les intéressent au moyen de liens dans leur liste de préférences. Enfin, le dernier chapitre de la thèse comprend l'étude du Capacitated Rank Pricing Problem (CRPP) avec envie. Dans cette extension, nous avons supposé des prix de réserve pour les clients qui reflètent ce qu'ils sont prêts à payer pour chaque produit, plutôt qu'un budget unique par consommateur. Cependant, la principale différence est que dans le cas du CRPP, l'entreprise dispose d'un nombre limité de produits et peut ne pas être en mesure de satisfaire la demande de tous les clients. L'objectif de la thèse est d'obtenir des formulations linéaires en nombres entiers mixtes pour les trois problèmes étudiés, et de les comparer sur le plan théorique et/ou computationnel. À cette fin, la méthodologie utilisée est basée sur la proposition de variables de décision et de contraintes appropriées qui modélisent le problème. L'objectif suivant est l'amélioration de ces formulations au moyen d'inégalités valides qui réduisent l’ensemble admissible de la relaxation du problème et permettent d'obtenir une meilleure borne de la relaxation linéaire. Et enfin, un troisième objectif est la proposition d'algorithmes de résolution pour chacun de ces modèles, et leur comparaison ultérieure au moyen d'études computationnelles et de résolution au moyen de logiciels d'optimisation commerciaux. / This doctorate is entirely devoted to an in-depth study of the Rank Pricing Problem (RPP) and two generalizations. The RPP is a combinatorial optimization problem which aims at setting the prices of a series of products of a company to maximize its revenue. This problem is specified by a set of unit-demand customers, that is, customers interested in a subset of the products offered by the company which intend to buy at most one of them. To do so, they count on a fixed budget, and they rank the products of their interest from the “best” to the “worst”. Once the prices are established by the company, they will purchase their highest-ranked product among the ones they can afford. In the RPP, it is assumed an unlimited supply of products, which is consistent with a company having enough copies of a product to satisfy the demand, or with a setting where the products can be produced quickly at negligible cost (e.g. digital goods). This dissertation consists of four chapters. The first chapter introduces the RPP problem and the mathematical concepts present in the work, whereas each of the next three chapters tackles the resolution of each of the problems of study: the RPP and two generalizations. Thus, Chapter 3 is dedicated to the Rank Pricing Problem with Ties (RPPT), an extension of the RPP where we consider that customers can express indifference among products in their preference list. And the last chapter of the thesis is devoted to a generalization of the problem that we have named the Capacitated Rank Pricing Problem (CRPP) with envy. For this generalization, we have considered reservation prices of customers for the different products that reflect their willingness to pay, instead of a single budget per customer. However, the main difference is that, in the CRPP, the company has a limited supply of products and might not be able to satisfy all the customers’ requests. This is a realistic assumption that we can find in many companies.The aim of this thesis is the proposal of mixed-integer linear formulations for the three problems of study, and their theoretical and/or computational comparison. The methodology used is based on the introduction of decision variables and adequate restrictions to model the problems. Another objective consists in strengthening the formulations by means of valid inequalities that reduce the feasible region of the relaxed problem and allow us to obtain better linear relaxation bounds. Finally, a third goal is to derive resolution algorithms for each of these models and compare them computationally, using commercial solvers. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Steelmaking industry, one of the most electricity-intensive industrial processes, is seeking for new approaches to improve its competitiveness in terms of energy savings by taking advantage of the volatile electricity prices. This fluctuation in the price is mainly caused by the increasing share of renewable energy sources, the liberalization of energy markets and the growing demand of the energy. Therefore, making the production scheduling of steelmaking processes with knowledge about the cost of the energy may lead to significant cost savings in the electricity bills. With this aim in mind, different models are developed in this project in order to improve the existing monolithic models (continuous-time based scheduling) to find an efficient formulation of accounting for electricity consumption and also to expand them with more detailed scheduling of Electric Arc Furnace stage in the production process. The optimization of the energy cost with multiple electricity sources and contracts and the production planning are usually done as stand-alone optimizers due to their complexity, therefore as a new approach in addition to the monolithic model an iterative framework is developed in this work. The idea to integrate the two models in an iterative manner has potential to be useful in the industry due to low effort for reformulation of existing models. The implemented framework uses multiparametric programming together with bilevel programming in order to direct the schedule to find a compromise between the production constraints and goals, and the energy cost. To ensure applicability heuristic approaches are also examined whenever full sized models are not meeting computational performance requirements. The results show that the monolithic model implemented has a considerable advantage in terms of computational time compared to the models in the literature and in some cases, the solution can be obtained in a few minutes instead of hours. In the contrary, the iterative framework shows a bad performance in terms of computational time when dealing with real world instances. For that matter a heuristic approach, which is easy to implement, is investigated based on coordination theory and the results show that it has a potential since it provides solutions close to the optimal solutions in a reasonable amount of time. Multiparametric programming is the main core of the iterative framework developed in this internship and it is not able to give the solutions for real world instances due to computational time limitations. This computational problem is related to the nature of the algorithm behind mixed integer multiparametric programming and its ability to handle the binary variables. Therefore, further work to this project is to develop new approaches to approximate multiparametric technique or develop some heuristics to approximate the mp-MILP solutions.