Network reconfiguration in distribution systems using simulated annealing

Chang, Hong‐Chan; Kuo, Cheng‐Chien

doi:10.1016/0378-7796(94)90018-3

Cited by 196 publications

(93 citation statements)

References 9 publications

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“…Between 30 % and 40 % of all investments in the electric power system are referred to distribution network, but technological progress of reconfiguration of distribution subsystem is not as fast as it is the case with the generation and transmission network [2]. Many distribution subsystems function with minimum number of control systems and without adequate computer support for the system operator.…”

Section: The Reconfiguration Of Distribution Subsystemmentioning

confidence: 99%

Optimization of switching conditions in distribution power subsystem

2015

Teh. vjesn.

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Original scientific paper To improve function of distribution power subsystem, systematic researches of optimization methods based on modern analytic techniques, have been performed recently. Therefore, several specialized algorithms for power flow and power losses calculations in electric distribution subsystem are developed. Distribution power subsystem 10 kV with 212 buses and 72 switches is analysed in this paper. Stochastic optimization based on simulated annealing method is applied. The aim of analysis is to determine the optimal network switching condition of minimum losses, with the precondition of keeping the stable voltages on all buses. Simulations are performed for two initial configurations with different initial annealing temperatures and cooling speeds. All parameters of distribution network components have constant values. The results show that the obtained solutions are with better efficiency than those that are chosen initially.Keywords: distribution power subsystem; power losses; simulated annealing; system optimization; switching conditions Optimizacija uklopnog stanja u distribucijskom elektroenergetskom sustavuIzvorni znanstveni članak U novije vrijeme se vrše sustavna istraživanja optimizacijskih metoda korištenjem suvremenih analitičkih tehnika radi unaprjeđenja funkcije distribucijskog elektroenergetskog sustava. Stoga je razvijeno više specijaliziranih algoritama za proračun tokova snaga i određivanje gubitaka u distribucijskom sustavu. U ovom radu je analizirana 10 kV distributivna mreža s 212 čvorova i 72 rastavljača. Analiza je provedena primjenom stohastičke optimizacijske metode, odnosno metode simuliranog kaljenja. Cilj istraživanja je određivanje optimalnog uklopnog stanja mreže uz najmanje gubitke, uz zadržavanje stabilnog napona na svim sabirnicama. Simulacije su izvedene za dvije početne konfiguracije s različitim početnim temperaturama kaljenja i brzinama hlađenja. Svi parametri komponenata distribucijske mreže imaju konstantne vrijednosti. Rezultati pokazuju da su dobivena rješenja učinkovitija od početno odabranih.

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Section: The Reconfiguration Of Distribution Subsystemmentioning

confidence: 99%

Optimization of switching conditions in distribution power subsystem

2015

Teh. vjesn.

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“…RGA is achieved with some improvements in chromosome coding, fitness function and mutation pattern. Simulated Annealing (SA) method was also proposed by many authors ( [17]; [18]) to mitigate the power losses in distribution network reconfiguration. Das et.…”

Section: Using Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

Mam¹,

G²,

Saxena³

2016

International Journal of Engineering and Manufacturing

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This paper presents a distribution network reconfiguration based on bacterial foraging optimization algorithm (BFOA) along with backward-forward sweep (BFS) load flow method and geographical information system (GIS). Distribution network reconfiguration (DNR) is a complex, non-linear, combinatorial, and nondifferentiable constrained optimization process aimed at finding the radial structure that minimized network power loss while satisfying all operating constraints. BFOA is used to obtain the optimal switching configuration which results in a minimum loss, BFS is used to optimize the deviation in node voltages, and GIS is used for planning and easy analysis purposes. Simulation is performed on the 33-bus system and results are compared with the other approaches. The obtained results show that the proposed approach is better in terms of efficiency and having good convergence criteria.

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“…The number of mutations affecting each clone of an antibody [17] is given by (4). In this expression m is the number of mutations that will affect a clone of an antibody, round(.)…”

Section: Opt-ainet Algoritmhmentioning

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%

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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Network reconfiguration in distribution systems using simulated annealing

Cited by 196 publications

References 9 publications

Optimization of switching conditions in distribution power subsystem

Optimization of switching conditions in distribution power subsystem

Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

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

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