Gestão de Recursos Energéticos nas SmartGrids

A sustentabilidade do sistema energético é crucial para o desenvolvimento económico e social das sociedades presentes e futuras. Para garantir o bom funcionamento dos sistemas de energia actua-se, tipicamente, sobre a produção e sobre as redes de transporte e de distribuição. No entanto, a integração crescente de produção distribuída, principalmente nas redes de distribuição de média e de baixa tensão, a liberalização dos mercados energéticos, o desenvolvimento de mecanismos de armazenamento de energia, o desenvolvimento de sistemas automatizados de controlo de cargas e os avanços tecnológicos das infra-estruturas de comunicação impõem o desenvolvimento de novos métodos de gestão e controlo dos sistemas de energia. O contributo deste trabalho é o desenvolvimento de uma metodologia de gestão de recursos energéticos num contexto de SmartGrids, considerando uma entidade designada por VPP que gere um conjunto de instalações (unidades produtoras, consumidores e unidades de armazenamento) e, em alguns casos, tem ao seu cuidado a gestão de uma parte da rede eléctrica. Os métodos desenvolvidos contemplam a penetração intensiva de produção distribuída, o aparecimento de programas de Demand Response e o desenvolvimento de novos sistemas de armazenamento. São ainda propostos níveis de controlo e de tomada de decisão hierarquizados e geridos por entidades que actuem num ambiente de cooperação mas também de concorrência entre si. A metodologia proposta foi desenvolvida recorrendo a técnicas determinísticas, nomeadamente, à programação não linear inteira mista, tendo sido consideradas três funções objectivo distintas (custos mínimos, emissões mínimas e cortes de carga mínimos), originando, posteriormente, uma função objectivo global, o que permitiu determinar os óptimos de Pareto. São ainda determinados os valores dos custos marginais locais em cada barramento e consideradas as incertezas dos dados de entrada, nomeadamente, produção e consumo. Assim, o VPP tem ao seu dispor um conjunto de soluções que lhe permitirão tomar decisões mais fundamentadas e de acordo com o seu perfil de actuação. São apresentados dois casos de estudo. O primeiro utiliza uma rede de distribuição de 32 barramentos publicada por Baran & Wu. O segundo caso de estudo utiliza uma rede de distribuição de 114 barramentos adaptada da rede de 123 barramentos do IEEE.

Planeamento de redes de distribuição de energia

O planeamento de redes de distribuição tem como objetivo assegurar a existência de capacidade nas redes para a fornecimento de energia elétrica com bons níveis de qualidade de serviço tendo em conta os fatores económicos associados. No âmbito do trabalho apresentado na presente dissertação, foi elaborado um modelo de planeamento que determina a configuração de rede resultante da minimização de custos associados a: 1) perdas por efeito de joule; 2) investimento em novos componentes; 3) energia não entregue. A incerteza associada ao valor do consumo de cada carga é modelada através de lógica difusa. O problema de otimização definido é resolvido pelo método de decomposição de benders que contempla dois trânsitos de potências ótimos (modelo DC e modelo AC) no problema mestre e escravo respectivamente para validação de restrições. Foram também definidos critérios de paragem do método de decomposição de benders. O modelo proposto classifica-se como programação não linear inteira mista e foi implementado na ferramenta de otimização General Algebraic Modeling System (GAMS). O modelo desenvolvido tem em conta todos componentes das redes para a otimização do planeamento, conforme podemos analisar nos casos de estudo implementados. Cada caso de estudo é definido pela variação da importância que cada uma das variáveis do problema toma, tendo em vista cobrir de alguma todos os cenários de operação expetáveis. Através destes casos de estudo verifica-se as várias configurações que a rede pode tomar, tendo em conta as importâncias atribuídas a cada uma das variáveis, bem como os respetivos custos associados a cada solução. Este trabalho oferece um considerável contributo no âmbito do planeamento de redes de distribuição, pois comporta diferentes variáveis para a execução do mesmo. É também um modelo bastante robusto não perdendo o ‘norte’ no encontro de solução para redes de grande dimensão, com maior número de componentes.

Synthesis of plantwide control strategies using mixed integer optimization

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2012

Optimal operation of multiproduct batch plants

Multiproduct plants, Dynamic Optimization, Mixed Integer Linear/Non-Linear Programming, Scheduling

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

On the throughput-WIP trade-off in queueing systems, diminishing returns and the threshold property: A linear programming approach

We present a new unifying framework for investigating throughput-WIP(Work-in-Process) optimal control problems in queueing systems,based on reformulating them as linear programming (LP) problems withspecial structure: We show that if a throughput-WIP performance pairin a stochastic system satisfies the Threshold Property we introducein this paper, then we can reformulate the problem of optimizing alinear objective of throughput-WIP performance as a (semi-infinite)LP problem over a polygon with special structure (a thresholdpolygon). The strong structural properties of such polygones explainthe optimality of threshold policies for optimizing linearperformance objectives: their vertices correspond to the performancepairs of threshold policies. We analyze in this framework theversatile input-output queueing intensity control model introduced byChen and Yao (1990), obtaining a variety of new results, including (a)an exact reformulation of the control problem as an LP problem over athreshold polygon; (b) an analytical characterization of the Min WIPfunction (giving the minimum WIP level required to attain a targetthroughput level); (c) an LP Value Decomposition Theorem that relatesthe objective value under an arbitrary policy with that of a giventhreshold policy (thus revealing the LP interpretation of Chen andYao's optimality conditions); (d) diminishing returns and invarianceproperties of throughput-WIP performance, which underlie thresholdoptimality; (e) a unified treatment of the time-discounted andtime-average cases.

Implementing TURF analysis through binary linear programming

This paper introduces the approach of using Total Unduplicated Reach and Frequency analysis (TURF) to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. The results obtained through our exact algorithm are presented, and this method shows to be extremely efficient both in obtaining optimal solutions and in computing time for very large instances of the problem at hand. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

Restless bandits, linear programming relaxations and a primal-dual index heuristic

We develop a mathematical programming approach for the classicalPSPACE - hard restless bandit problem in stochastic optimization.We introduce a hierarchy of n (where n is the number of bandits)increasingly stronger linear programming relaxations, the lastof which is exact and corresponds to the (exponential size)formulation of the problem as a Markov decision chain, while theother relaxations provide bounds and are efficiently computed. Wealso propose a priority-index heuristic scheduling policy fromthe solution to the first-order relaxation, where the indices aredefined in terms of optimal dual variables. In this way wepropose a policy and a suboptimality guarantee. We report resultsof computational experiments that suggest that the proposedheuristic policy is nearly optimal. Moreover, the second-orderrelaxation is found to provide strong bounds on the optimalvalue.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Smooth height/age curves from stem analysis with linear programming

[Abstract]

Computational modeling for irrigated agriculture planning. Part I: general description and linear programming

Linear programming models are effective tools to support initial or periodic planning of agricultural enterprises, requiring, however, technical coefficients that can be determined using computer simulation models. This paper, presented in two parts, deals with the development, application and tests of a methodology and of a computational modeling tool to support planning of irrigated agriculture activities. Part I aimed at the development and application, including sensitivity analysis, of a multiyear linear programming model to optimize the financial return and water use, at farm level for Jaíba irrigation scheme, Minas Gerais State, Brazil, using data on crop irrigation requirement and yield, obtained from previous simulation with MCID model. The linear programming model outputted a crop pattern to which a maximum total net present value of R$ 372,723.00 for the four years period, was obtained. Constraints on monthly water availability, labor, land and production were critical in the optimal solution. In relation to the water use optimization, it was verified that an expressive reductions on the irrigation requirements may be achieved by small reductions on the maximum total net present value.

Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming

We study the preconditioning of symmetric indefinite linear systems of equations that arise in interior point solution of linear optimization problems. The preconditioning method that we study exploits the block structure of the augmented matrix to design a similar block structure preconditioner to improve the spectral properties of the resulting preconditioned matrix so as to improve the convergence rate of the iterative solution of the system. We also propose a two-phase algorithm that takes advantage of the spectral properties of the transformed matrix to solve for the Newton directions in the interior-point method. Numerical experiments have been performed on some LP test problems in the NETLIB suite to demonstrate the potential of the preconditioning method discussed.

Inclusion of nonlinear demand-supply relationships within large-scale partial equilibrium linear programming models

This paper presents a new method for the inclusion of nonlinear demand and supply relationships within a linear programming model. An existing method for this purpose is described first and its shortcomings are pointed out before showing how the new approach overcomes those difficulties and how it provides a more accurate and 'smooth' (rather than a kinked) approximation of the nonlinear functions as well as dealing with equilibrium under perfect competition instead of handling just the monopolistic situation. The workings of the proposed method are illustrated by extending a previously available sectoral model for the UK agriculture.

Analysis of feeding strategies for small-scale dairy systems in central Mexico using linear programming

A limitation of small-scale dairy systems in central Mexico is that traditional feeding strategies are less effective when nutrient availability varies through the year. In the present work, a linear programming (LP) model that maximizes income over feed cost was developed, and used to evaluate two strategies: the traditional one used by the small-scale dairy producers in Michoacan State, based on fresh lucerne, maize grain and maize straw; and an alternative strategy proposed by the LIP model, based on ryegrass hay, maize silage and maize grain. Biological and economic efficiency for both strategies were evaluated. Results obtained with the traditional strategy agree with previously published work. The alternative strategy did not improve upon the performance of the traditional strategy because of low metabolizable protein content of the maize silage considered by the model. However, the Study recommends improvement of forage quality to increase the efficiency of small-scale dairy systems, rather than looking for concentrate supplementation.