Planning for Ontario’s future energy supply mix is a very challenging undertaking which requires consideration of various drivers and decision criteria. From the literature review conducted, no published work has been found addressing the multi-period energy planning problem with CO2 emission constraints and the option of carbon capture and storage (CCS). The objective of this project was to develop a novel multi-period mixed-integer non-linear programming (MINLP) model that is able to realize the optimal mix of energy supply sources which will meet current and future electricity demand, CO2 emission targets, and lower the overall cost of electricity. This model was implemented in GAMS (General Algebraic Modeling System). The model was formulated using an objective function that minimizes the net present value of the cost of electricity (COE) over a time horizon of 14 years. The formulation incorporates several time dependent parameters such as forecasted energy demand, fuel price variability, construction lead time, conservation initiatives, and increase in fixed operational and maintenance costs over time. The model was applied to two case studies in order to examine the economical, structural, and environmental effects that would result if Ontario’s electricity sector was required to reduce its CO2 emissions to a specific limit. The first case study examined a base case scenario in which no CO2 limits were imposed. The second case study examined a scenario in which Ontario’s electricity sector must comply with CO2 emission limits similar to the Kyoto target of 6% below 1990 levels. The results indicate that in order to meet the CO2 targets of 6% below 1990 levels, Nanticoke, Atikokan, and Thunder Bay coal-fired power plants must be fuel-switched, and Lambton coal-fired power plant must be retrofitted with a CCS system. Furthermore, a total CO2 reduction of approximately 32% was achieved when compared to the base case. The total cost associated with reducing the CO2 emissions to 6% below 1990 levels, per ton of CO2, was $48.79 / ton CO2 reduced. The total expenditure for Case Study II (CO2 limit of 6% below 1990 levels) was approximately 10.1% higher than for the base case. This model offers many potential benefits to Ontario’s energy sector. In addition to providing an optimal solution for meeting future electricity demand, it can help Ontario meet its emissions targets while minimizing the overall cost of electricity. Furthermore, although this project was aimed at Ontario’s future energy supply mix, it could also be readily applied to other regions or even countries as a whole.

Planning for Ontario’s future energy supply mix is a very challenging undertaking which requires consideration of various drivers and decision criteria. From the literature review conducted, no published work has been found addressing the multi-period energy planning problem with CO2 emission constraints and the option of carbon capture and storage (CCS). The objective of this project was to develop a novel multi-period mixed-integer non-linear programming (MINLP) model that is able to realize the optimal mix of energy supply sources which will meet current and future electricity demand, CO2 emission targets, and lower the overall cost of electricity. This model was implemented in GAMS (General Algebraic Modeling System). The model was formulated using an objective function that minimizes the net present value of the cost of electricity (COE) over a time horizon of 14 years. The formulation incorporates several time dependent parameters such as forecasted energy demand, fuel price variability, construction lead time, conservation initiatives, and increase in fixed operational and maintenance costs over time. The model was applied to two case studies in order to examine the economical, structural, and environmental effects that would result if Ontario’s electricity sector was required to reduce its CO2 emissions to a specific limit. The first case study examined a base case scenario in which no CO2 limits were imposed. The second case study examined a scenario in which Ontario’s electricity sector must comply with CO2 emission limits similar to the Kyoto target of 6% below 1990 levels. The results indicate that in order to meet the CO2 targets of 6% below 1990 levels, Nanticoke, Atikokan, and Thunder Bay coal-fired power plants must be fuel-switched, and Lambton coal-fired power plant must be retrofitted with a CCS system. Furthermore, a total CO2 reduction of approximately 32% was achieved when compared to the base case. The total cost associated with reducing the CO2 emissions to 6% below 1990 levels, per ton of CO2, was $48.79 / ton CO2 reduced. The total expenditure for Case Study II (CO2 limit of 6% below 1990 levels) was approximately 10.1% higher than for the base case. This model offers many potential benefits to Ontario’s energy sector. In addition to providing an optimal solution for meeting future electricity demand, it can help Ontario meet its emissions targets while minimizing the overall cost of electricity. Furthermore, although this project was aimed at Ontario’s future energy supply mix, it could also be readily applied to other regions or even countries as a whole.

Esta tese é dedicada ao estudo de modelos de otimização de carteiras de investimento multi-período. Daremos ênfase a um modelo com restrições intermediárias formulado como um problema de controle ótimo e resolvido utilizando técnicas de programação dinâmica. Serão tratados aspectos teóricos e práticos desta classe de problemas. Primeiramente faremos uma revisão das principais hipóteses dos modelos de otimização de carteiras e o caso uni-período. Analisaremos a seguir as generalizações para o caso multi-período, onde os modelos utilizam apenas restrições para o valor esperado e/ou para a variância da carteira no instante final do período analisado. Apresentaremos então o principal resultado proposto neste trabalho onde consideramos o problema de seleção ótima de ativos multi-período no qual podemos incorporar ao modelo restrições intermediárias para o valor esperado e variância da carteira durante o período de análise. A grande vantagem desta técnica é permitir o controle do valor esperado e/ou da variância da carteira ao longo de todo o horizonte de análise. Faremos uma comparação o entre as formulações apresentadas e realizaremos experimentos numéricos com o modelo proposta nesta tese. Os principais resultados originais desta tese encontram-se no Capítulo 5. No Capítulo 6 apresentamos as simulações numéricas realizadas com o modelo proposto. / The subject of this thesis is the study of multi-period portfolio optimization problems. We focus on a model with intermediate constraints formulated as an optimal control problem and solved by using dynamic programming techniques. Both theoretical and practical issues are addressed. Firstly we will analyze the main hypothesis of portfolio optimization models and the single period case. Then we will present the generalization for the multi-period case, where the models use only constraints for the expected value and variance at the final period. The main result proposed in this work considers the multi-period portfolio selection problem with intermediate constraints on the expected value and variance of the portfolio taken into account in the optimization problem. The main advantage of this technique is that it is possible to control the intermediate expected value or variance of the portfolio during the time horizon considered. Comparison between the presented formulations and numerical experiments of the proposed model will be exposed. The main original results of this thesis can be found in Chapter 5. In Chapter 6 we present numerical simulations with the proposed model.

Esta tese é dedicada ao estudo de modelos de otimização de carteiras de investimento multi-período. Daremos ênfase a um modelo com restrições intermediárias formulado como um problema de controle ótimo e resolvido utilizando técnicas de programação dinâmica. Serão tratados aspectos teóricos e práticos desta classe de problemas. Primeiramente faremos uma revisão das principais hipóteses dos modelos de otimização de carteiras e o caso uni-período. Analisaremos a seguir as generalizações para o caso multi-período, onde os modelos utilizam apenas restrições para o valor esperado e/ou para a variância da carteira no instante final do período analisado. Apresentaremos então o principal resultado proposto neste trabalho onde consideramos o problema de seleção ótima de ativos multi-período no qual podemos incorporar ao modelo restrições intermediárias para o valor esperado e variância da carteira durante o período de análise. A grande vantagem desta técnica é permitir o controle do valor esperado e/ou da variância da carteira ao longo de todo o horizonte de análise. Faremos uma comparação o entre as formulações apresentadas e realizaremos experimentos numéricos com o modelo proposta nesta tese. Os principais resultados originais desta tese encontram-se no Capítulo 5. No Capítulo 6 apresentamos as simulações numéricas realizadas com o modelo proposto. / The subject of this thesis is the study of multi-period portfolio optimization problems. We focus on a model with intermediate constraints formulated as an optimal control problem and solved by using dynamic programming techniques. Both theoretical and practical issues are addressed. Firstly we will analyze the main hypothesis of portfolio optimization models and the single period case. Then we will present the generalization for the multi-period case, where the models use only constraints for the expected value and variance at the final period. The main result proposed in this work considers the multi-period portfolio selection problem with intermediate constraints on the expected value and variance of the portfolio taken into account in the optimization problem. The main advantage of this technique is that it is possible to control the intermediate expected value or variance of the portfolio during the time horizon considered. Comparison between the presented formulations and numerical experiments of the proposed model will be exposed. The main original results of this thesis can be found in Chapter 5. In Chapter 6 we present numerical simulations with the proposed model.

This paper studies the problem of multi-period pricing for perishable products in a competitive (oligopolistic) market. We study non cooperative Nash equilibrium policies for sellers. At the beginning of the time horizon, the total inventories are given and additional production is not an available option. The analysis for periodic production-review models, where production decisions can be made at the end of each period at some production cost after incurring holding or backorder costs, does not extend to this model. Using results from game theory and variational inequalities we study the existence and uniqueness of equilibrium policies. We also study convergence results for an algorithm that computes the equilibrium policies. The model in this paper can be used in a number of application areas including the airline, service and retail industries. We illustrate our results through some numerical examples. / Singapore-MIT Alliance (SMA)

A multi-period optimal energy planning program for Ontario has been developed in mixed-integer non-linear programming using General Algebraic Modeling System, GAMS. The program applies both time-dependent and time-independent constraints. These include, but not limited to, construction time, fluctuation of fuel prices, and CO2 emission reduction target. It also offer flexibility of fuel balancing and fuel switching of the existing boilers and option purchasing of Carbon credit if the reduction target is not achievable. The objective function incorporates all these constraints as well as minimizes over all the cost of electricity and meets the projected electricity demand over the 30 years horizon. A number of Ontario study cases are performed utilizing this 30 years model. These cases include a number of CO2 emission reduction target from 6% to 75% below that of 1990 levels by 2014, doubling of natural gas over the forecasted price in 2020, an arbitrary year. A study case in appliance with the Environmental Protection Act where no new or existing coal-fired power stations are available after 2014, as well as study cases where no new nuclear power stations are available. The overall cost of the electricity for different CO2 emission reduction targets increases linearly with slope of ~ 5. The fuel switching, fuel balancing for coal stations, and retrofitting of the carbon capture and storage are the main strategy in order to keep the cost of electricity relative low and satisfy the CO2 emission constraints. Nuclear power is an essential supply technology to the fleet especially when CO2 emission is concerned. An additional 248 Mt of CO2 emission is observed over the reference case when no new nuclear supply is offered. Eliminating all coal technologies by 2014 in accordance to the Environmental Protection Act may also reduce the CO2 emission with less additional expenditure normally associated with the emission reduction processes. This however also reduces the energy port folio diversity, forcing the system to depend on a smaller group of supply technologies and decreasing the reliability of the system overall. These results help us better understand the factors affecting the fleet’s structure. It may also help plan the energy direction of Ontario and perhaps serve as an example for other provinces, territories, states, and even countries.

A multi-period optimal energy planning program for Ontario has been developed in mixed-integer non-linear programming using General Algebraic Modeling System, GAMS. The program applies both time-dependent and time-independent constraints. These include, but not limited to, construction time, fluctuation of fuel prices, and CO2 emission reduction target. It also offer flexibility of fuel balancing and fuel switching of the existing boilers and option purchasing of Carbon credit if the reduction target is not achievable. The objective function incorporates all these constraints as well as minimizes over all the cost of electricity and meets the projected electricity demand over the 30 years horizon. A number of Ontario study cases are performed utilizing this 30 years model. These cases include a number of CO2 emission reduction target from 6% to 75% below that of 1990 levels by 2014, doubling of natural gas over the forecasted price in 2020, an arbitrary year. A study case in appliance with the Environmental Protection Act where no new or existing coal-fired power stations are available after 2014, as well as study cases where no new nuclear power stations are available. The overall cost of the electricity for different CO2 emission reduction targets increases linearly with slope of ~ 5. The fuel switching, fuel balancing for coal stations, and retrofitting of the carbon capture and storage are the main strategy in order to keep the cost of electricity relative low and satisfy the CO2 emission constraints. Nuclear power is an essential supply technology to the fleet especially when CO2 emission is concerned. An additional 248 Mt of CO2 emission is observed over the reference case when no new nuclear supply is offered. Eliminating all coal technologies by 2014 in accordance to the Environmental Protection Act may also reduce the CO2 emission with less additional expenditure normally associated with the emission reduction processes. This however also reduces the energy port folio diversity, forcing the system to depend on a smaller group of supply technologies and decreasing the reliability of the system overall. These results help us better understand the factors affecting the fleet’s structure. It may also help plan the energy direction of Ontario and perhaps serve as an example for other provinces, territories, states, and even countries.

Processing facilities are normally designed with sufficient flexibility to handle nominal variations. When the process features planned changes in feedstock and products, scheduling is often used to optimize process operation. The objective of this dissertation is to develop a new approach to design and scheduling with economic, environmental, heat integration and inherently safer design objectives. Specifically, this work introduces a systematic framework and the associated mathematical formulation for simultaneous process design and scheduling while simultaneously addressing economic, environmental, heat integration and inherently safer design objectives. Therefore, more than one type of proper tradeoffs are established between these objectives. The environmental issues pertaining to the parameterized process retrofitting, scheduling, and operation strategies are simultaneously considered along with the environmental impact of these changes. Similarly, the design synthesis of heat-exchange networks (HENs) is addressed in the context of optimizing energy consumption under scheduling scenarios. Finally, the goal of inherently safer design is simultaneously considered with the expected schedules of the process. Several optimization formulations are developed for the projected schedules while allowing design modifications and retrofitting changes. The modifications and changes include new environmental management units, synthesis of flexible and optimal HENs, and design of an inherently safer process. Process models with the appropriate level of relevant details are included in the formulations. A discretization approach has been adopted to allow for a multiperiod optimization formulation over a given time horizon. The resulting framework identifies opportunities for synergism between the economic, environmental, heat integration and inherently safer design objectives. It also determines points of diminishing return beyond which tradeoffs between the above mentioned objectives are established. The devised procedure is illustrated with case studies.

No description available.

The advent of the Internet has not only enabled traditional brick-and-mortar retailers to open online channels, but also provided a platform that facilitated consumer-to-consumer information exchange on retailers and/or products. As a result, the purchasing decisions of today's consumers are often affected by the purchasing decisions of other consumers. In this dissertation, we adopt an interdisciplinary approach that brings together tools and concepts from operations management, economics, systems dynamics and marketing literatures to create analytical models in order to address a dual-channel retailer's optimal demand shaping strategy, through e-commerce advertisement efforts, store service levels, and pricing, in this new environment. Our findings show that the retailer's optimal demand shaping strategy, in terms of store service levels and e-commerce advertisement effort, critically depends on the product's e-commerce adoption phase. We also show that in the presence of higher operating costs for the store channel compared to the online channels, a channel-tailored pricing policy always dominates a uniform pricing strategy. Our work sheds light on the benefits of channel integration for multi-channel retailers. We show that the retailer can leverage the online channels to provide in-store pricing and inventory availability information in order to enable a more transparent shopping experience for consumers, and this strategy results in a "win-win" situation for all parties. / Ph. D.