Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks

2009 ◽  
pp. 216-227 ◽  
Author(s):  
Carlos Barrico ◽  
Carlos Henggeler Antunes ◽  
Dulce Fernão Pires
Keyword(s):  
Robustness Analysis ◽  
Distribution Networks ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Electrical Distribution ◽  
Electrical Distribution Networks

scholarly journals A multi-objective optimization-based model for the deployment of reclosers and remote-controlled switches using NSGA2 and entropy weighted TOPSIS method

2020 ◽  
Vol 20 (3) ◽  
pp. 1128
Author(s):  
Anass Lekbich ◽  
Abdelaziz Belfqih ◽  
Tayeb Ouaderhman ◽  
Chaimae Zedak ◽  
Jamal Boukherouaa ◽  
...  
Keyword(s):  
Distribution Networks ◽  
Optimization Method ◽  
Pareto Solutions ◽  
Performance Control ◽  
Multi Objective ◽  
Electrical Distribution ◽  
Criteria Weights ◽  
Electrical Distribution Networks ◽  
Entropy Weighted ◽  
The Ideal

<p>Since they are fast, remote-controlled, automated and intelligent, reclosers<br />and switches are an inevitable solution for improving the reliability of<br />intelligent electrical distribution networks at optimal cost. However, their<br />location and coordination have great effects on the protection and automation<br />strategies of complex electrical distribution networks against multiple<br />unpredictable faults. Which requires a flexible and multi-criteria optimization<br />method. In this article, we propose a multi-objective method based on an<br />analytical model by considering the fault rate, restoration times, outage cost<br />and coordination between devices. The non-dominated genetic sorting<br />algorithm II was proposed to obtain the optimal Pareto solutions, and a<br />technique of performance control by similarity with the ideal solution was<br />used to classify them. The objective criteria weights are based on the entropy<br />method which allows solutions to be obtained and better classified with the<br />minimum of subjectivity. The IEEE33 and IEEE13 bus test networks were<br />used to verify the method. The results obtained are compared to a binary<br />multi-objective particle swarm optimization method and the results show that<br />the proposed method reduces the overall costs, reduces the undelivered<br />energy of the system and improves the reliability of the service.</p>

Multi-objective optimization of optimal capacitor allocation in radial distribution systems

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Sayed Mir Shah Danish ◽  
Mikaeel Ahmadi ◽  
Atsushi Yona ◽  
Tomonobu Senjyu ◽  
Narayanan Krishna ◽  
...  
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Reactive Power ◽  
Stability Index ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Size ◽  
Multi Objective Optimization ◽  
Multi Objective

AbstractThe optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

scholarly journals Influence of Clandestine Connections on Energy Loss Evaluation in Electrical Distribution Networks

2020 ◽  
Author(s):  
Clainer B. Donadel ◽  
Gilberto C. D. Sousa ◽  
Flávio M. Varejão
Keyword(s):  
Artificial Intelligence ◽  
Developing Countries ◽  
Energy Loss ◽  
Loss Factor ◽  
Distribution Systems ◽  
Distribution Network ◽  
Distribution Networks ◽  
Operation Costs ◽  
Electrical Distribution ◽  
Electrical Distribution Networks

In the literature, there are several methodologies to estimate technical losses in electrical distribution networks. The range of techniques is broad, ranging from basic techniques (based on loss factor, for example) to sophisticated ones (based on artificial intelligence). These methodologies are important, because the costs of technical losses represent a huge part of the total operation costs of distribution network operators (DNOs). However, the presence of clandestine connections, common in developing countries, was not considered in the methodologies encountered in the literature. Clandestine connections occur when a consumer has made his/her connection without DNO permission. In these cases, the amount of energy consumed by a clandestine "consumer" is a nontechnical loss (and, therefore, should be correctly computed as nonbilled energy). Therefore, a new methodology is proposed to consider the presence of clandestine connections in energy loss estimation in distribution systems.

scholarly journals Optimal Segmentation of Electrical Distribution Networks

2021 ◽  
Vol 19 (8) ◽  
pp. 1375-1382
Author(s):  
Carlos Bonetti ◽  
Jezabel Bianchotti ◽  
Jorge Vega ◽  
Gabriel Puccini
Keyword(s):  
Distribution Networks ◽  
Electrical Distribution ◽  
Electrical Distribution Networks ◽  
Optimal Segmentation

Multi-objective optimization of water distribution networks using particle swarm optimization

2021 ◽  
Vol 218 ◽  
pp. 18-31
Author(s):  
Douglas F. Surco ◽  
Diogo H. Macowski ◽  
Flávia A.R. Cardoso ◽  
Thelma P.B. Vecchi ◽  
Mauro A.S.S. Ravagnani
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective
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