A class of multiobjective control problems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Transmission system expansion planning by a branch-and-bound algorithm

A branch and bound (B& B) algorithm using the DC model, to solve the power system transmission expansion planning by incorporating the electrical losses in network modelling problem is presented. This is a mixed integer nonlinear programming (MINLP) problem, and in this approach, the so-called fathoming tests in the B&B algorithm were redefined and a nonlinear programming (NLP) problem is solved in each node of the B& B tree, using an interior-point method. Pseudocosts were used to manage the development of the B&B tree and to decrease its size and the processing time. There is no guarantee of convergence towards global optimisation for the MINLP problem. However, preliminary tests show that the algorithm easily converges towards the best-known solutions or to the optimal solutions for all the tested systems neglecting the electrical losses. When the electrical losses are taken into account, the solution obtained using the Garver system is better than the best one known in the literature.

Evolutionary multi-move path-relinking for transmission network expansion planning

This paper presents the application of a new metaheuristic algorithm to solve the transmission expansion planning problem. A simple heuristic, using a relaxed network model associated with cost perturbation, is applied to generate a set of high quality initial solutions with different topologies. The population is evolved using a multi-move path-relinking with the objective of finding minimum investment cost for the transmission expansion planning problem employing the DC representation. The algorithm is tested on the southern Brazilian system, obtaining the optimal solution for the system with better performance than similar metaheuristics algorithms applied to the same problem. ©2010 IEEE.

Domain reduction using GRASP construction phase for transmission expansion planning problem

This paper proposes a new strategy to reduce the combinatorial search space of a mixed integer linear programming (MILP) problem. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) is employed to reduce the domain of the integer variables of the transportation model of the transmission expansion planning (TM-TEP) problem. This problem is a MILP and very difficult to solve specially for large scale systems. The branch and bound (BB) algorithm is used to solve the problem in both full and the reduced search space. The proposed method might be useful to reduce the search space of those kinds of MILP problems that a fast heuristic algorithm is available for finding local optimal solutions. The obtained results using some real test systems show the efficiency of the proposed method. © 2012 Springer-Verlag.

An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.

When less is better: Insights from the product mix dilemma from the Theory of Constraints perspective

Perhaps due to its origins in a production scheduling software called Optimised Production Technology (OPT), plus the idea of focusing on system constraints, many believe that the Theory of Constraints (TOC) has a vocation for optimal solutions. Those who assess TOC according to this perspective indicate that it guarantees an optimal solution only in certain circumstances. In opposition to this view and founded on a numeric example of a production mix problem, this paper shows, by means of TOC assumptions, why the TOC should not be compared to methods intended to seek optimal or the best solutions, but rather sufficiently good solutions, possible in non-deterministic environments. Moreover, we extend the range of relevant literature on product mix decision by introducing a heuristic based on the uniquely identified work that aims at achieving feasible solutions according to the TOC point of view. The heuristic proposed is tested on 100 production mix problems and the results are compared with the responses obtained with the use of Integer Linear Programming. The results show that the heuristic gives good results on average, but performance falls sharply in some situations. © 2013 Copyright Taylor and Francis Group, LLC.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

Interface Gráfica para o planejamento da expansão da transmissão de energia elétrica

Pós-graduação em Engenharia Elétrica - FEIS

Um método para determinação de pontos de operação com diversidade em linha digital de assinante usando balanceamento de espectro e algoritmo evolucionário

Este trabalho apresenta um método para encontrar um conjunto de pontos de operação, os quais são ótimos de Pareto com diversidade, para linhas digitais de assinante (DSL - digital subscriber line). Em diversos trabalhos encontrados na literatura, têm sido propostos algoritmos para otimização da transmissão de dados em linhas DSL, que fornecem como resultado apenas um ponto de operação para os modems. Esses trabalhos utilizam, em geral, algoritmos de balanceamento de espectro para resolver um problema de alocação de potência, o que difere da abordagem apresentada neste trabalho. O método proposto, chamado de diverseSB , utiliza um processo híbrido composto de um algoritmo evolucionário multiobjetivo (MOEA - multi-objective evolutionary algorithm), mais precisamente, um algoritmo genético com ordenamento por não-dominância (NSGA-II - Non-Dominated Sorting Genetic Algorithm II), e usando ainda, um algoritmo de balanceamento de espectro. Os resultados obtidos por simulações mostram que, para uma dada diversidade, o custo computacional para determinar os pontos de operação com diversidade usando o algoritmo diverseSB proposto é muito menor que métodos de busca de “força bruta”. No método proposto, o NSGA-II executa chamadas ao algoritmo de balanceamento de espectro adotado, por isso, diversos testes envolvendo o mesmo número de chamadas ao algoritmo foram realizadas com o método diverseSB proposto e o método de busca por força bruta, onde os resultados obtidos pelo método diverseSB proposto foram bem superiores do que os resultados do método de busca por força bruta. Por exemplo, o método de força bruta realizando 1600 chamadas ao algoritmo de balanceamento de espectro, obtém um conjunto de pontos de operação com diversidade semelhante ao do método diverseSB proposto com 535 chamadas.

Um modelo bi-nível para o investimento multi-etapa em capacidade de transmissão e geração de energia elétrica em mercados competitivos

Pós-graduação em Engenharia Elétrica - FEIS

Reconfiguração de sistemas de distribuição utilizando metaheurísticas e critérios de confiabilidade

In an ever more competitive environment, power distribution companies must satisfy two conflicting objectives: minimizing investment costs and the satisfaction of reliability targets. The network reconfiguration of a distribution system is a technique that well adapts to this new deregulated environment for it allows improvement of reliability indices only opening and closing switches, without the onus involved in acquiring new equipment. Due to combinatorial explosion problem characteristic, in the solution are employed metaheuristics methods, which converge to optimal or quasi-optimal solutions, but with a high computational effort. As the main objective of this work is to find the best configuration(s) of the distribution system with the best levels of reliability, the objective function used in the metaheuristics is to minimize the LOLC - Loss Of Load Cost, which is associated with both, number and duration of electric power interruptions. Several metaheuristics techniques are tested, and the tabu search has proven to be most appropriate to solve the proposed problem. To characterize computationally the problem of the switches reconfiguring was developed a vector model (with integers) of the representation of the switches, where each normally open switch is associated with a group of normally closed switches. In this model simplifications have been introduced to reduce computational time and restrictions were made to exclude solutions that do not supply energy to any load point of the system. To check violation of the voltage and loading criteria a study of power flow for the ten best solutions is performed. Also for the ten best solutions a reliability evaluation using Monte Carlo sequential simulation is performed, where it is possible to obtain the probability distributions of the indices and thus calculate the risk of paying penalty due to not meeting the goals. Finally, the methodology is applied in a real Brazilian distribution network, and the results are discussed.

Otimização dinâmica de rotores com eixos em compósito

Pós-graduação em Engenharia Mecânica - FEG

Double-objective economic statistical design of the VP T-2 control chart: Wald's identity approach

Research has shown that applying the T-2 control chart by using a variable parameters (VP) scheme yields rapid detection of out-of-control states. In this paper, the problem of economic statistical design of the VP T-2 control chart is considered as a double-objective minimization problem with the statistical objective being the adjusted average time to signal and the economic objective being expected cost per hour. We then find the Pareto-optimal designs in which the two objectives are met simultaneously by using a multi-objective genetic algorithm. Through an illustrative example, we show that relatively large benefits can be achieved by applying the VP scheme when compared with usual schemes, and in addition, the multi-objective approach provides the user with designs that are flexible and adaptive.

Condições suficientes de otimalidade em cálculo variacional

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Volt-VAR multiobjective optimization to peak-load relief and energy efficiency in distribution networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)