Simultaneous mixed-integer disjunctive optimization for synthesis of petroleum refinery topology Processing Alternatives for Naphtha Produced from Atmospheric Distillation Unit

In this work, we propose a logic-based modeling technique within a mixed-integer disjunctive superstructure optimization framework on the topological optimization problem for determining the optimal petroleum refinery configuration. We are interested to investigate the use of logic cuts that are linear inequality/equality constraints to the conceptual process synthesis problem of the design of a refinery configuration. The logic cuts are employed in two ways using 0-l variables: ( l) to enforce certain design specifications based on past design experience, engineering knowledge, and heuristics; and (2) to enforce certain structural specifications on the interconnections of the process units. The overall modeling framework conventionally gives rise to a mixedinteger optimization framework, in this case, a mixed-integer linear programming model (because of the linearity of the constraints). But in this work, we elect to adopt a disjunctive programming framework, specifically generalized disjunctive programming (GDP) proposed by Grossmann and co-workers (Grossmann, l. E. (2002). Review of Nonlinear Mixed-Integer and Disjunctive Programming Techniques. Optimization & Engineering, 3, 227.) The proposed GOP-based modeling technique is illustrated on a case study to determine the optimal processing route of naphtha in a refinery using the GAMS/LogMIP platform, which yields practically-acceptable solution. The use of LogMIP obviates the need to reformulate the logic propositions and the overall disjunctive problem into algebraic representations, hence reducing the time involved in the typically time-consuming problem formulation. LogMIP typically leads to less computational time and number of iterations in its computational effort because the associated GDP formulation involves less equations and variables compared to MILP. From the computational experiments, it is found that logical constraints of design specifications and structural specifications potentially play an important role to determine the optimal selection of process units and streams. Hence, in general, the GDP formulation can be improved by adding or eliminating constraits that can accelerate or slow-down the problem solution respectively

Capacity Planning and Resource Acquisition Decisions Using Robust Optimization

This dissertation studies strategic capacity planning and resource acquisition decisions, including the facility location problem and the technology choice problem. These decisions are modeled in an integrative manner, and the main purpose of the proposed models and numerical experiments is to examine the effects of economies of scale, economies of scope, and the combined effects of scale and scope under uncertain demand realizations using robust optimization. The type of capacities, or technology alternatives, that a firm can acquire can be classified on two basic dimensions. The first dimension relates to the effects of scale via distinction between labor-intensive (less automated) technologies and capital-intensive (more automated) technologies. The second dimension relates to the effects of scope via distinction between product-dedicated and flexible technologies. Moreover, each of the product-dedicated and flexible technologies can have different levels of labor or capital-intensiveness, leading to the joint effects of economies of scale and economies of scope. Each of the technology alternatives possesses certain cost structures. Labor-intensive technologies are characterized by low fixed costs and high variable costs, whereas capital-intensive technologies are characterized by just the opposite cost structure, i.e., high fixed costs and low variable costs. Flexible technologies cost more than product-dedicated technologies, both in terms of fixed and variable costs. Robust optimization methodology is used to investigate how different levels of robustness, and facility and technology costs affect the quantities, types and allocation of technologies to facilities. Results show that specific technology choice patterns emerge depending on various cost structures and different levels of model robustness specified to accommodate uncertain demand realizations. The results obtained by the two-stage robust optimization approach are compared to the results obtained by a non-robust approach and a stochastic programming approach

Design for Optimized Multi-Lateral Multi-Commodity Markets

In this paper, we propose a design for an an economically efficient, optimized, centralized, multi-lateral, periodic commodity market that addresses explicitly three issues: (i) substantial transportation costs between sellers and buyers; (ii) non homogeneous, in quality and nature, commodities; (iii) complementary commodities that have to be traded simultaneously. The model allows sellers to offer their commodities in lots and buyers to explicitly quantify the differences in quality of the goods produced by each individual seller. The model does not presume that products must be shipped through a market hub. We also propose a multi-round auction that enables the implementation of the direct optimized market and approximates the behaviour of the "ideal" direct optimized mechanism. The process allows buyers and sellers to modify their initial bids, including the technological constraints. The proposed market designs are particularly relevant for industries related to natural resources. We present the models and algorithms required to implement the optimized market mechanisms, describe the operations of the multi-round auction, and discuss applications and perspectives. Nous présentons un concept de marché optimisé, centralisé, multilatéral et périodique pour l'acquisition de produits qui traite explicitement les trois aspects suivants: (i) des coûts de transport importants des vendeurs vers les acheteurs; (ii) des produits non homogènes en valeur et qualité; des complémentarités entre les divers produits qui doivent donc être négociés simultanément. Le modèle permet aux vendeurs d'offrir leurs produits groupés en lots et aux acheteurs de quantifier explicitement leur évaluation des lots mis sur le marché par chaque vendeur. Le modèle ne suppose pas que les produits doivent être expédiés par un centre avant d'être livrés. Nous proposons également un mécanisme de tâtonnement à rondes multiples qui approxime le comportement du marché direct optimisé et qui permet de mettre ce dernier en oeuvre. Le processus de tâtonnement permet aux vendeurs et aux acheteurs de modifier leurs mises initiales, incluant les contraintes technologiques. Les concepts proposés sont particulièrement adaptés aux industries reliées aux matières premières. Nous présentons les modèles et algorithmes requis à la mise en oeuvre du marché multi-latéral optimisé, nous décrivons le fonctionnement du processus de tâtonnement, et nous discutons les applications et perspectives reliées à ces mécanismes de marché.Market design, optimized multi-lateral multi-commodity markets, multi-round auctions, Design de marché, marché multi-latéraux optimisés, processus de tâtonnement

Optimization Approaches for Electricity Generation Expansion Planning Under Uncertainty

In this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems\u27 infrastructures require a large amount of investments, and need to stay in operation for a long time and accommodate many different scenarios in the future. The uncertainties we are addressing in this dissertation mainly include demands, electricity prices, investment and maintenance costs of power generation units. To address these future uncertainties in the decision-making process, this dissertation adopts two different optimization approaches: decision-dependent stochastic programming and adaptive robust optimization. In the decision-dependent stochastic programming approach, we consider the electricity prices and generation units\u27 investment and maintenance costs being endogenous uncertainties, and then design probability distribution functions of decision variables and input parameters based on well-established econometric theories, such as the discrete-choice theory and the economy-of-scale mechanism. In the adaptive robust optimization approach, we focus on finding the multistage adaptive robust solutions using affine policies while considering uncertain intervals of future demands. This dissertation mainly includes three research projects. The study of each project consists of two main parts, the formulation of its mathematical model and the development of solution algorithms for the model. This first problem concerns a large-scale investment problem on both thermal and wind power generation from an integrated angle without modeling all operational details. In this problem, we take a multistage decision-dependent stochastic programming approach while assuming uncertain electricity prices. We use a quasi-exact solution approach to solve this multistage stochastic nonlinear program. Numerical results show both computational efficient of the solutions approach and benefits of using our decision-dependent model over traditional stochastic programming models. The second problem concerns the long-term investment planning with detailed models of real-time operations. We also take a multistage decision-dependent stochastic programming approach to address endogenous uncertainties such as generation units\u27 investment and maintenance costs. However, the detailed modeling of operations makes the problem a bilevel optimization problem. We then transform it to a Mathematic Program with Equilibrium Constraints (MPEC) problem. We design an efficient algorithm based on Dantzig-Wolfe decomposition to solve this multistage stochastic MPEC problem. The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems\u27 expansion planning

Uncertainty and Investment Dynamics

This paper shows that, with (partial) irreversibility, higher uncertainty reduces the impact effect of demand shocks on investment. Uncertainty increases real option values making firms more cautious when investing or disinvesting. This is confirmed both numerically for a model with a rich mix of adjustment costs, time-varying uncertainty, and aggregation over investment decisions and time, and also empirically for a panel of manufacturing firms. These cautionary effects of uncertainty are large - going from the lower quartile to the upper quartile of the uncertainty distribution typically halves the first year investment response to demand shocks. This implies the responsiveness of firms to any given policy stimulus may be much lower in periods of high uncertainty, such as after major shocks like OPEC I and 9/11.Investment, uncertainty, real options, panel data

Uncertainty and Investment Dynamics

This paper shows that, with (partial) irreversibility, higher uncertainty reduces the impact effect of demand shocks on investment. Uncertainty increases real option values making firms more cautious when investing or disinvesting. This is confirmed both numerically for a model with a rich mix of adjustment costs, time-varying uncertainty, and aggregation over investment decisions and time, and also empirically for a panel of manufacturing firms. These cautionary effects of uncertainty are large %u2013 going from the lower quartile to the upper quartile of the uncertainty distribution typically halves the first year investment response to demand shocks. This implies the responsiveness of firms to any given policy stimulus may be much lower in periods of high uncertainty, such as after major shocks like OPEC I and 9/11.

Multiobjective metaheuristic approaches for mean-risk combinatorial optimisation with applications to capacity expansion

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200