Optimal reactive power dispatch using stochastic chance-constrained programming

Deterministic Optimal Reactive Power Dispatch problem has been extensively studied, such that the demand power and the availability of shunt reactive power compensators are known and fixed. Give this background, a two-stage stochastic optimization model is first formulated under the presumption that the load demand can be modeled as specified random parameters. A second stochastic chance-constrained model is presented considering uncertainty on the demand and the equivalent availability of shunt reactive power compensators. Simulations on six-bus and 30-bus test systems are used to illustrate the validity and essential features of the proposed models. This simulations shows that the proposed models can prevent to the power system operator about of the deficit of reactive power in the power system and suggest that shunt reactive sourses must be dispatched against the unavailability of any reactive source. © 2012 IEEE.

Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation

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

A mixed-integer quadratically-constrained programming model for the distribution system expansion planning

This paper presents a mixed-integer quadratically-constrained programming (MIQCP) model to solve the distribution system expansion planning (DSEP) problem. The DSEP model considers the construction/reinforcement of substations, the construction/reconductoring of circuits, the allocation of fixed capacitors banks and the radial topology modification. As the DSEP problem is a very complex mixed-integer non-linear programming problem, it is convenient to reformulate it like a MIQCP problem; it is demonstrated that the proposed formulation represents the steady-state operation of a radial distribution system. The proposed MIQCP model is a convex formulation, which allows to find the optimal solution using optimization solvers. Test systems of 23 and 54 nodes and one real distribution system of 136 nodes were used to show the efficiency of the proposed model in comparison with other DSEP models available in the specialized literature. (C) 2014 Elsevier Ltd. All rights reserved.

On the solution of mathematical programming problems with equilibrium constraints

Mathematical programming problems with equilibrium constraints (MPEC) are nonlinear programming problems where the constraints have a form that is analogous to first-order optimality conditions of constrained optimization. We prove that, under reasonable sufficient conditions, stationary points of the sum of squares of the constraints are feasible points of the MPEC. In usual formulations of MPEC all the feasible points are nonregular in the sense that they do not satisfy the Mangasarian-Fromovitz constraint qualification of nonlinear programming. Therefore, all the feasible points satisfy the classical Fritz-John necessary optimality conditions. In principle, this can cause serious difficulties for nonlinear programming algorithms applied to MPEC. However, we show that most feasible points do not satisfy a recently introduced stronger optimality condition for nonlinear programming. This is the reason why, in general, nonlinear programming algorithms are successful when applied to MPEC.

Bound constrained smooth optimization for solving variational inequalities and related problems

Variational inequalities and related problems may be solved via smooth bound constrained optimization. A comprehensive discussion of the important features involved with this strategy is presented. Complementarity problems and mathematical programming problems with equilibrium constraints are included in this report. Numerical experiments are commented. Conclusions and directions of future research are indicated.

Market-driven security-constrained transmission network expansion planning

This paper presents a Bi-level Programming (BP) approach to solve the Transmission Network Expansion Planning (TNEP) problem. The proposed model is envisaged under a market environment and considers security constraints. The upper-level of the BP problem corresponds to the transmission planner which procures the minimization of the total investment and load shedding cost. This upper-level problem is constrained by a single lower-level optimization problem which models a market clearing mechanism that includes security constraints. Results on the Garver's 6-bus and IEEE 24-bus RTS test systems are presented and discussed. Finally, some conclusions are drawn. © 2011 IEEE.

Efeito da adubação na manifestação da resistência de feijoeiro ao ataque de caruncho em testes com e sem chance de escolha

Objetivando-se estudar a influência da adubação na manifestação da resistência de feijoeiro (Phaseolus vulgaris L.) (cultivares Rosinha G-2, ESAL-564 - Carioca e Diamante Negro) ao ataque de Acanthoscelides obtectus (Say, 1831) (Coleoptera: Bruchidae) foram conduzidos, na época das águas, testes com e sem chance de escolha, utilizando-se grãos obtidos de parcelas adubadas com N, P, K, NP, NK, PK, NPK, e sem adubo, totalizando 24 tratamentos. Os testes foram realizados em condições controladas de temperatura, umidade e luz. Foram avaliados o número de insetos atraídos e de ovos, em cada tratamento. Concluiu-se, no teste com chance de escolha, que o número de ovos de A. obtectus por recipiente foi reduzido pela aplicação do nitrogênio. A aplicação de nitrogênio em adubação resultou em menor porcentagem de insetos atraídos e menor número de ovos no genótipo Rosinha G-2. A manifestação da resistência nos genótipos ESAL-564 e Rosinha G-2 ao ataque de A. obtectus ficou evidente quando utilizados nitrogênio e potássio. Nos testes sem chance de escolha o consumo dos insetos foi reduzido nos grãos produzidos com a aplicação do nitrogênio. A aplicação de nitrogênio em adubação resultou no aumento do número de ovos de A. obtectus no genótipo ESAL-564. Não ficou evidente a manifestação da resistência nos genótipos ESAL-564 e Rosinha G-2 ao ataque do caruncho, pela aplicação dos macronutrientes N, P e K. Na ausência de fósforo o ciclo biológico do inseto foi maior na presença de potássio.

Padrões nutricionais para lavouras arrozeiras irrigadas por inundação pelos métodos da CDN chance matemática

Para o arroz irrigado, poucos trabalhos utilizam métodos de diagnose foliar desenvolvidos para as condições locais de clima, solo ou cultivares. O objetivo deste trabalho foi avaliar os métodos da Diagnose da Composição Nutricional e da Chance Matemática na definição dos padrões nutricionais de lavouras arrozeiras do Estado do Rio Grande do Sul. Resultados de produtividade de grãos e teores foliares de N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, Zn e Mo de 356 lavouras arrozeiras cultivadas sob sistema de irrigação por inundação foram utilizados para a determinação das faixas de suficiência calculadas pelo método da Chance Matemática. As faixas de suficiência foram comparadas com valores críticos propostos pela literatura e com o intervalo de confiança do teor médio dos nutrientes em lavouras consideradas nutricionalmente equilibradas, identificadas pelo método Diagnose da Composição Nutricional. Observou-se pouca concordância entre os valores das faixas de suficiência indicados pelos métodos da Chance Matemática e da Diagnose da Composição Nutricional e os respectivos valores indicados na literatura. A faixa de teores foliares adequados, consistentes com maior produtividade média das lavouras arrozeiras, foi indicada ser de 23 a 28 g kg-1 para N; 11 a 14 g kg-1 para K; 1,4 a 2,0 g kg-1 para S; 6 a 12 mg kg-1 para B; e 70 a 200 mg kg-1 para Fe. Para os teores foliares de P, Ca, Mg, B, Cu, Mn e Zn e Mo nenhuma das faixas adequadas testadas indicou capacidade para distinguir as lavouras arrozeiras quanto à produtividade média.

Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

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

The open capacitated arc routing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Binary integer programming formulations for scheduling in market-driven foundries

This paper describes the development and solution of binary integer formulations for production scheduling problems in market-driven foundries. This industrial sector is comprised of small and mid-sized companies with little or no automation, working with diversified production, involving several different metal alloy specifications in small tailor-made product lots. The characteristics and constraints involved in a typical production environment at these industries challenge the formulation of mathematical programming models that can be computationally solved when considering real applications. However, despite the interest on the part of these industries in counting on effective methods for production scheduling, there are few studies available on the subject. The computational tests prove the robustness and feasibility of proposed models in situations analogous to those found in production scheduling at the analyzed industrial sector. (C) 2010 Elsevier Ltd. All rights reserved.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.

A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Security constrained optimal active power flow via network model and interior point method

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