An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems

Zhang, Dong; Fu, Zhengcai; Zhang, Liuchun

doi:10.1016/j.epsr.2006.06.005

Cited by 532 publications

(275 citation statements)

References 12 publications

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“…In order to obtain the power active losses for each demand level and for each candidate solution (each antibody in the population) we used the power flow algorithm detailed in [15]. The tests were developed using distribution systems with 33, 84, 136 and 417 buses and the respective data is available in [20], [21], [5] and [22]. The systems with 33, 84 and 136 nodes are test systems and the one with 417 nodes is a real distribution network.…”

Section: Applications and Resultsmentioning

confidence: 99%

“…In this sense, the literature related with the DSR problem assuming fixed demand levels includes publications reporting the use of heuristic algorithms [1][2], metaheuristics as Genetic Algorithms [3], Simulated Annealing [4], Tabu Search [5], Ant Colonies [6], GRASP [7], Artificial Neural Networks [8] and Artificial Immune Systems [9]. On the other hand, references [10][11][12][13] address the DSR problem assuming variable demand levels.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Applications and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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)

View full text Add to dashboard Cite

show abstract

“…Algunas de las técnicas mono-objetivo más empleadas son: algoritmo genético [50], enjambre de partículas [51], recocido simulado [52], búsqueda tabú [53], algoritmos inmunes [54], colonia de hormigas [55], enjambre de abejas [56], entre otros. Dentro las técnicas multi-objetivo, las más conocidas y empleadas son: algoritmo de ordenamiento no dominado (NSGA, por sus siglas en inglés) [57] y el algoritmo caótico artificial colonia de abejas (CABC, de sus siglas en inglés) [58].…”

Section: Métodos Heurísticos Y Metaheurísticosunclassified

Ubicación y dimensionamiento de generación distribuida: Una revisión

Grisales

Cuestas

Jaramillo

2017

Cien.Ing.Neogranadina

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La generación distribuida (GD) se presenta como un apoyo para suplir la demanda del sistema de forma localizada, lo que permite además como alternativas de generación el uso de fuentes de energía renovables a pequeña escala. Como aporte principal, este artículo presenta las consideraciones que se deben tener en cuenta al momento de analizar, modelar y solucionar el problema de ubicación y dimensionamiento de GD en la red eléctrica de distribución. Inicialmente, se enuncian los aspectos técnicos de la red, el modelado de las cargas e impactos de la GD; seguido de la formulación del modelo matemático del problema, principales técnicas de solución y metodologías aplicadas en la literatura; finalmente, se muestran las investigaciones futuras en este campo de estudio. Todo lo anterior en busca de mejorar las condiciones operativas de la red. Dentro de la revisión de los trabajos presentados por los diferentes autores, se encontró que una gran parte de los estudios se enfocan en los aspectos técnicos, sin tener en cuenta los costos asociados a la integración de GD, ni los incentivos económicos a los que puede ser susceptibles este tipo de proyectos, que son de vital importancia para evaluar la viabilidad de la propuesta de integración.

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“…In [4], the authors solving the problem of distribution network reconfiguration apply both the algorithms for the construction of a maximum spanning tree and the simplified approaches to specification of currents and power losses. It is probably this simplification that did not allow the researchers to find better solutions which were obtained with the help of other algorithms, for example, in [5].…”

Section: Introductionmentioning

confidence: 99%

Consideration of uncertainty of information on load and generation values under daily reconfiguration of primary distribution network

et al. 2017

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Abstract. Intelligent electric networks make it possible to remotely control switchings, thus preventing overloads and enhancing reliability of power supply to consumers. In the paper, the daily reconfiguration of a primary distribution network is considered as a means to reduce energy losses in the network. An algorithm applied for the reconfiguration is based on the methods of the theory of graphs and includes a high-speed program for load flow calculation. The research is focused on the impact of renewable generation and active demand with loads changing depending on daily variation in electricity price on the reduction in losses at reconfiguration. An algorithm is proposed to optimize load curves of load-controlled consumers. The calculation of probabilistic load flows is applied to assess the impact of the uncertainty of nodal power forecast on energy loss reduction at reconfiguration. The results of the research demonstrate the effectiveness of the proposed approaches, and are illustrated by a 33-node test distribution network.

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An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems

Cited by 532 publications

References 12 publications

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

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

Ubicación y dimensionamiento de generación distribuida: Una revisión

Consideration of uncertainty of information on load and generation values under daily reconfiguration of primary distribution network

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