An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem

Carreno, E. M.; Romero, Rubén; Padilha‐Feltrin, Antonio

doi:10.1109/tpwrs.2008.2002178

Cited by 224 publications

(132 citation statements)

References 25 publications

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“…En el caso de la ingeniería eléctrica, se han utilizado con éxito en la solución de problemas como: el balance de fases en circuitos de distribución primaria, [9] el planeamiento de redes de distribución, [10] la reconfiguración de sistemas de distribución, [11] la ubicación de dispositivos FACTS [12], así como la compensación de potencia reactiva en los sistemas de distribución primaria [13][14] y de forma simultánea al problema de la reconfiguración [15].…”

Section: Los Algoritmos Genéticos En La Solución De Problemas De Optiunclassified

Control de demanda eléctrica aplicando algoritmos genéticos

Vasconcellos

González

González³

2017

Ingeniare. Rev. chil. ing.

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RESUMENEl problema del control de la demanda y el consumo de energía en empresas productivas y de servicios constituye una importante oportunidad para la reducción de los gastos asociados al uso de la energía eléctrica. En el presente trabajo se obtiene un modelo de compensación basado en la técnica de algoritmos genéticos que permite complementar el procedimiento manual de control de demanda, usualmente denominado acomodo de cargas, de forma tal que se obtenga mínimo consumo de energía en el horario pico, con mínima demanda máxima y mínima discontinuidad del gráfico de carga. El procedimiento resultante fue implementado utilizando las herramientas de programación del MATLAB versión 7.8 (R2010a) de la Global Optimization Toolbox. La efectividad del método se demuestra por medio de su aplicación a un caso de estudio real en el área de servicios a la producción de la Empresa Cervecería Tínima de Camagüey, Cuba.Palabras clave: Control de demanda, acomodo de cargas, algoritmos genéticos. ABSTRACT The problem of demand and energy consumption control in productive and service companies constitutes an important opportunity for the reduction of the expenses associated to the use of the electric power. The presently work offers a compensation model based on the technique of genetics algorithms that

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Section: Los Algoritmos Genéticos En La Solución De Problemas De Optiunclassified

Control de demanda eléctrica aplicando algoritmos genéticos

Vasconcellos

González

González³

2017

Ingeniare. Rev. chil. ing.

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“…In this work we used the coding proposed in [18] for the DSR problem. In this coding scheme, the EDS is represented as a tree (graph theory) constituted by an array of arcs (branches).…”

Section: A Coding Of the Candidate Topologiesmentioning

confidence: 99%

“…To generate the initial population (P) including N elements for the Opt-aiNet algorithm, we used the Prim heuristic detailed in [18]. This heuristic uses the codification detailed in the previous section and it is described in the next steps.…”

Section: B Strategy To Generate the Initial Populationmentioning

confidence: 99%

Distribution System Reconfiguration with variable demands using the Opt-aiNet algorithm

Souza

Romero

Pereira

et al. 2016

2016 13th International Conference on the European Energy Market (EEM)

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Abstract-This paper describes the application of the Opt-aiNet algorithm to the reconfiguration problem of distribution systems considering variable demand levels. The Opt-aiNet algorithm is an optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with variable demands is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of power losses along an extended operation period. This work includes results of the application of the Opt-aiNet algorithm to distribution systems with 33, 84, 136 and 417 buses. These results demonstrate the robustness and efficiency of the proposed approach.

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“…Most authors have used different well known heuristics (branch exchange [2,3,21], branch and bound [1,4], simulated annealing [5]), other heuristic rules or meta-heuristics [7][8][9][11][12][13]15,17,22,23,25,27,28] or multi-agent technologies [20]. On the other hand, some authors have developed methods based on evolutionary computation techniques [6,14,16,18,19,24,26,29,30]. An important drawback of these methods is the fact that they solve the reconfiguration problems as single objective problems.…”

Section: Introductionmentioning

confidence: 99%

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

et al. 2013

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Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

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An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem

Cited by 224 publications

References 25 publications

Control de demanda eléctrica aplicando algoritmos genéticos

Control de demanda eléctrica aplicando algoritmos genéticos

Distribution System Reconfiguration with variable demands using the Opt-aiNet algorithm

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

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