Edimar J. de Oliveira

Stochastic Dynamic Programming Applied to Hydrothermal Power Systems Operation Planning Based on the Convex Hull Algorithm

This paper presents a new approach for the expected cost-to-go functions modeling used in the stochastic dynamic programming (SDP) algorithm. The SDP technique is applied to the long-term operation planning of electrical power systems. Using state space discretization, the Convex Hull algorithm is used for constructing a series of hyperplanes that composes a convex set. These planes represent a piecewise linear approximation for the expected cost-to-go functions. The mean operational costs for using the proposed methodology were compared with those from the deterministic dual dynamic problem in a case study, considering a single inflow scenario. This sensitivity analysis shows the convergence of both methods and is used to determine the minimum discretization level. Additionally, the applicability of the proposed methodology for two hydroplants in a cascade is demonstrated. With proper adaptations, this work can be extended to a complete hydrothermal system.

Hybrid heuristic optimization approach for optimal Distributed Generation placement and sizing

This paper presents a hybrid algorithm that combines Particle Swarm Optimization (PSO) and Nonlinear Optimal Power Flow (OPF) in the optimal sitting and sizing of Distributed Generation (DG). The objective function considered is to minimize the power losses in distribution systems. The proposed approach makes use of a sensitivity index based on derivatives to identify the best candidate buses for sitting the DG. This index is considered in the PSO initial population aiming at reducing the search space, though, improving the convergence of the method. The OPF is then used to determine the optimal size of each DG. Results are compared with previous papers presenting different methodologies for the 69-bus radial distribution system to validate the proposed approach.

Cuckoo Search Optimization technique applied to capacitor placement on distribution system problem

This paper presents an approach based on Cuckoo Search method for optimal capacitor placement in distribution systems. The Cuckoo Search (CS) algorithm is used to solve this mixed non linear integer optimization problem which aims to minimize losses in distribution systems. The proposed algorithm is evaluated using the IEEE 16-bus, IEEE 33-bus and IEEE 69-bus distributions systems. The results obtained show the effectiveness of the proposed technique.

Power system stabilizers tuning using bio-inspired algorithm

This paper presents a methodology for coordinated tuning of power system stabilizers taking into account a set of pre-specified operating conditions. The tuning procedure is formulated as an optimization problem which aims to maximize the system damping ratio. The proposed methodology is based on the bio-inspired Modified Cuckoo Search method. The methodology is applied for coordinated stabilizer tuning to New England test system. The results obtained are compared with a genetic algorithm approach.

An Optimal Power Flow Function to Aid Restoration Studies of Long Transmission Segments

This paper presents an optimal power flow function for aiding restoration studies of subsystems having long transmission segments. The method simultaneously computes an optimal set of variables which are critical for the subsystem restoration: the generation power plant high-side voltage, the minimum shunt reactor configuration and the maximum load to be safely energized. A set of overvoltage restoration scenarios following load rejection is constructed in such a way that the computed shunt reactor compensation becomes distributed along the transmission corridor. The whole problem is formulated as a single optimization problem whose solution is obtained by an optimal power flow, based on the Primal-Dual Interior Point Method. The described results for a restoration study on a system from practice confirm the effectiveness of the proposed method.

Thermal Unit Commitment using improved ant colony optimization algorithm via Lagrange multipliers

This article proposes the use of Lagrange multipliers associated with discrete variables of the Thermal Unit Commitment problem as a source of information for the ant colony algorithm. To achieve this, the discrete variables that are inherent to the problem are mitigated through a sigmoid function. By doing so, the non-linear optimization issue is solved through the use of the primal-dual interior-point method, generating Lagrange multipliers associated to the ON/OFF decision variables as subproducts which are used to draw up a list of priorities, where part of the colony will make use of this information in the search for solutions. The results obtained show that the information taken into consideration significantly improves the efficiency of the colony search process.

Genetic Algorithm Approach Applied to Long Term Generation Expansion Planning

The hydrothermal generation systems expansion is based on the ability of meeting the future energy market through increasing the existing power plants and/or the increase of the ability in transferring energy among the several regions in the country. The optimal investment to be performed is a function of generating ability of new units which are dimensioned according to the energy generation ability, to the impact caused by new interconnections and to the energy supply criterion. Thus, this work aims at obtaining the optimal planning of the existing power plants through the building perspective of new generation units in order to meet the market in a trustful and economic manner. However, the problem involves several entry programs of thermal and hydro plants and several synthetic series corresponding to the hydro scenarios, giving the problem a combinatorial problem. In order to do so, herein we will use a genetic algorithm, which presents a particular genetic structure and incorporate rules used by the system planner, to search for the best expansion strategy independently

A multiple kernel classification approach based on a Quadratic Successive Geometric Segmentation methodology with a fault diagnosis case

This work presents a new approach for solving classification and learning problems. The Successive Geometric Segmentation technique is applied to encapsulate large datasets by using a series of Oriented Bounding Hyper Box (OBHBs). Each OBHB is obtained through linear separation analysis and each one represents a specific region in a patterns solution space. Also, each OBHB can be seen as a data abstraction layer and be considered as an individual Kernel. Thus, it is possible by applying a quadratic discriminant function, to assemble a set of nonlinear surfaces separating each desirable pattern. This approach allows working with large datasets using high speed linear analysis tools and yet providing a very accurate non-linear classifier as final result. The methodology was tested using the UCI Machine Learning repository and a Power Transformer Fault Diagnosis real scenario problem. The results were compared with different approaches provided by literature and, finally, the potential and further applications of the methodology were also discussed.

Influência da variação da produtividade das usinas hidroelétricas no cálculo da energia firme

In the present work, the problem associated to the firm energy evaluation is treated as a non linear optimization model, which allows the representation of the productivity variation of the hydro plants. The proposed model takes into account the individualized representation of the plants and the historical series of flows since the month of January of 1931. The proposed optimization problem will be solved using the Primal-Dual Interior Point Method. A case study will be presented including the Brazilian Interconnected National System. The results obtained show that the proposed methodology is promising, since it presents an energy market value more realistic when compared with existing methodologies.

Reconfiguração ótima de sistemas de distribuição para minimização de perdas de energia

Este artigo apresenta um algoritmo para a reconfiguracao otima de Sistemas de Distribuicao de Energia Eletrica (SDE), com o objetivo de minimizar a perda total de energia considerando diferentes niveis de carregamento. Trata-se de um problema de programacao nao linear inteira mista onde a variavel discreta e modelada como uma funcao continua. Como consequencia, o problema proposto e resolvido atraves de um algoritmo passo a passo, onde em cada passo e utilizado o Metodo Primal-Dual de Pontos Interiores. Os multiplicadores de Lagrange sao utilizados para compor o indice de sensibilidade no processo de reconfiguracao. O algoritmo proposto e testado em tres sistemas encontrados na literatura.