Geraldo R. M. da Costa

Penalty-Based Nonlinear Solver for Optimal Reactive Power Dispatch With Discrete Controls

The optimal reactive dispatch problem is a nonlinear programming problem containing continuous and discrete control variables. Owing to the difficulty caused by discrete variables, this problem is usually solved assuming all variables as continuous variables, therefore the original discrete variables are rounded off to the closest discrete value. This approach may provide solutions far from optimal or even unfeasible solutions. This paper presents an efficient handling of discrete variables by penalty function so that the problem becomes continuous and differentiable. Simulations with the IEEE test systems were performed showing the efficiency of the proposed approach.

Optimal Distributed Generation and Reactive Power Allocation in Electrical Distribution Systems

Optimal and simultaneous siting and sizing of distributed generators and capacitor banks in distribution systems have attracted a lot of attention from distribution companies. The placement and capacity of these devices have direct effects on the systems performance. This paper presents a model for the simultaneous allocation of capacitor banks and distributed generation, which takes into account the stochastic nature of distributed generation. To solve the model presented, we propose an efficient hybrid method based on Tabu search and genetic algorithms. The hybrid method is applied to a well-known system in literature.

A modified Primal-Dual Logarithmic-Barrier Method for solving the Optimal Power Flow problem with discrete and continuous control variables

The aim of solving the Optimal Power Flow problem is to determine the optimal state of an electric power transmission system, that is, the voltage magnitude and phase angles and the tap ratios of the transformers that optimize the performance of a given system, while satisfying its physical and operating constraints. The Optimal Power Flow problem is modeled as a large-scale mixed-discrete nonlinear programming problem. This paper proposes a method for handling the discrete variables of the Optimal Power Flow problem. A penalty function is presented. Due to the inclusion of the penalty function into the objective function, a sequence of nonlinear programming problems with only continuous variables is obtained and the solutions of these problems converge to a solution of the mixed problem. The obtained nonlinear programming problems are solved by a Primal–Dual Logarithmic-Barrier Method. Numerical tests using the IEEE 14, 30, 118 and 300-Bus test systems indicate that the method is efficient.

Optimal Reactive Dispatch Problem via Improved Newton Approach

Abstract This article proposes a new approach to solve the optimal reactive dispatch problem, based on Newton approach and the primal-dual logarithmic barrier method. A Lagrangian function is associated with the modified problem. The first-order necessary conditions for optimality are fulfilled by Newtons method and by updating the penalty and barrier terms. The proposed approach does not require the set of binding constraints to be identified and can be utilized from an infeasible starting point. The effectiveness of the proposed approach has been examined by solving the Brazilian 53-bus and 662-bus systems.Abstract This article proposes a new approach to solve the optimal reactive dispatch problem, based on Newton approach and the primal-dual logarithmic barrier method. A Lagrangian function is associated with the modified problem. The first-order necessary conditions for optimality are fulfilled by Newtons method and by updating the penalty and barrier terms. The proposed approach does not require the set of binding constraints to be identified and can be utilized from an infeasible starting point. The effectiveness of the proposed approach has been examined by solving the Brazilian 53-bus and 662-bus systems.

Optimal power flow solution using the penalty/modified barrier method

The solution of the optimal power flow problem through the penalty/modified barrier method is described in this paper. This approach features considerable advantages over interior point methods. In this method, the inequality constraints are transformed into equalities by the introduction of slack variables, which are handled by either the modified barrier function or the quadratic penalty function. Then, first order optimality conditions and Newtons method are applied in the solution of the problem. In order to validate the proposed method, electrical power systems of 3, 14, 30, 118, 162 and 300 buses were used as case studies, and the obtained results proved the methods efficiency.

Neural-network representation of Voltage Stability Indices at the voltage collapse

Representing system security in power flow based system models by the scalar magnitude of a Voltage Stability Index (VSI) may be a very difficult task, which may even render the applicability of such models impractical. VSIs at voltage collapse points are difficult to predict when reactive power generation limits are taken into account in system modeling. Therefore, this paper proposes the determination of the Minimum Singular Value (MSV) and the Tangent Vector Norm (TVN) indices at the voltage collapse by means of Neural Networks (NN), being the latter a novel VSI based on the norm of the tangent vectors used in voltage collapse assessment. In order to determine voltage collapse points for different patterns of load and generation increase, an Optimal Power Flow (OPF) approach for solving the maximum loading problem was used. With these points, the MSV and TVN were calculated and used for training and testing the NNs. A small, but realistic, 6-bus system was used for carrying out this study. Results have shown that NNs can be readily applied to representing some VSIs at the voltage collapse. This approach overcomes some difficulties encountered in problems that account for system security through these VSIs.

Sensitivity Analysis Applied on the Electrical Power Systems Operation Planning

This paper proposes a study of electrical power system operation planning by Sensitivity Analysis (SA). The SA implemented was oriented by Optimal Power Flow (OPF). An optimal operation point was obtained by OPF. If some perturbation occurs in the power system, a new operation point will have to be evaluated to keep the feasibility of the power grid. This new operation point will be evaluated by SA. The SA was methodologically based in the theorem proposed by Fiacco. The set of non-linear equations generated by SA was solved by Newton´s method. Sparsity techniques were used. The principals contributions of this paper are the inclusion of transmission line constrained in the SA model and the complete use of the Fiacco´s theorem. Studies of cases were carried out in the IEEE 14, 118 and 300 buses, where the efficiency of the SA was verified.

Um método de reescalamento não-linear integrado

Neste trabalho, propomos um metodo de reescalamento nao-linear integrado, o qual mescla as familias de funcoes penalidades nao-quadraticas desenvolvidas por Polyak e Teboulle [11] e Matioli e Gonzaga [7] para os metodos de lagrangiano aumentado. Alem disso, definimos uma nova funcao para ser usada como penalidade para as restricoes de desigualdade e uma abordagem primal-dual do tipo previsor-corretor. Aplicamos o metodo proposto no problema de fluxo de potencia otimo reativo, da engenharia eletrica, ao sistema eletrico de 118 barras.

Smart Service Restoration of Electric Power Systems

This paper presents a smart service restoration method for electric power systems. Power system restoration (PSR) is the procedure of restoring the power supply after a power outage. Its objective is to restore the power system rapidly while satisfying all the operation constraints. Although it has been studied and applied most only to electrical distribution systems. The proposed methodology takes account the sub-transmission and distribution systems combined, and considers the objective of minimizing of out-of-service area. The PSR is formulated as a constrained objective optimization problem. Therefore, to solve this problem and obtain feasible solutions of valuable quality, with suitable computational effort, a Tabu Search approach is proposed. Tests were performed on an extended distribution test system, and the results show that the model and methodology are robust and efficient for practical size restoration problem applications.

A Decision-making Framework Based on Artificial Neural Networks and Intelligent Agents for Transmission Grid Operation

Abstract Security margins have been reduced in restructured and deregulated power systems, and as a result, these systems have been operated close to their security limits. Therefore, it is of utmost importance that power system operation be tracked in a real-time fashion, making decisions as fast as possible to ensure operating points within security limits. In this context, this article proposes a practical decision-making framework for transmission grid operation featuring artificial neural networks and intelligent agents. In this framework, the system operating point is tracked by means of voltage stability margins estimated by artificial neural networks,while the decision-making process is supported by means of intelligent agents. The output of this framework is a qualitative answer that supports system operators in making decisions to enhance security margins. A 6-bus test-system and the CIGRE 32-bus system were used for validating the neural network approach for voltage stability margin estimations; the proposed framework was validated with the IEEE 300-bus system. Results show that such a framework can be readily applied to support decisions aimed at ensuring secure system operating points.