Genetic Algorithm Based Network Reconfiguration in Distribution Systems with Multiple DGs for Time Varying Loads

Chidanandappa, R.; Ananthapadmanabha, T.; C, Ranjith H

doi:10.1016/j.protcy.2015.10.023

Cited by 33 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed approach performed satisfactorily based on the results obtained. The use of Rowlett wheel selection process and elitism in GA increases the efficiency of the algorithm [37] Simone et al (2014) presented reconfiguration problem of distribution networks using different demand scenarios with a view to identifying the most adequate radial topology of RDS that minimized the cost of energy losses in the network. The test systems used were 33, 70, 84 and 136 buses and a real system with 417 buses.…”

Section: 0mentioning

confidence: 99%

Application of Genetic Algorithm to Network Feeder Reconfiguration in Radial Distribution System

Ajenikoko¹,

Ajenikoko²,

Adedoyin³

et al. 2018

IJASTR

View full text Add to dashboard Cite

Distribution system being the final link in the hierarchical structure of power system is expanding daily due to exponential growth in man's activities that require power supply for its function. The fidelity of distribution system to faithfully accounts for energy obtained is highly hampered by the losses occurring at this end. It is reported that distribution system alone account for 13% of the total loss in power system. In view of this, power system engineers are faced with the challenges of minimizing the loss, reducing voltage deviation and maintaining system topology. One of the most cost effective means of doing this is network feeder reconfiguration which harnessed the feature of distribution system. Network reconfiguration re-positions the status of sectionalizing the tie-switches with a view to minimize real power losses, reduce voltage deviation and maintain network topology. This paper presents application of genetic algorithm to network feeder reconfiguration in radial distribution system. The motivation of picking GA is not unconnected with its ability to handle ill-structured optimization problems effectively. Samples of relevant papers were reviewed from 2010 till date, it was discovered that several variants of GA has emerged, alongside with hybridized version of GA. The modification(s) made and improvement on the original GA has been carefully presented. In all, the summary of improvement on GA on this concept revolved around codification methods, adaptive operators, and changes in fitness functions.It is hoped that this piece of work will be found useful to those working or intend working on distribution network reconfiguration using GA.

show abstract

Section: 0mentioning

confidence: 99%

Application of Genetic Algorithm to Network Feeder Reconfiguration in Radial Distribution System

Ajenikoko¹,

Ajenikoko²,

Adedoyin³

et al. 2018

IJASTR

View full text Add to dashboard Cite

show abstract

“…In the literature, several optimisation techniques were provided to address the issue of network reconfiguration. These optimisation techniques can be categorised into two groups: (i) metaheuristic [8–12] and (ii) heuristic techniques [13]. However, heuristic and metaheuristic both type of techniques have been applied successfully for this issue.…”

Section: Introductionmentioning

confidence: 99%

“…However, heuristic and metaheuristic both type of techniques have been applied successfully for this issue. Ant colony [8], genetic algorithm (GA) [9], selective firefly algorithm [10], multi‐objective particle swarm optimisation [11], tabu search algorithm [12] and time‐varying acceleration coefficients particle swarm optimisation [14] are some metaheuristic techniques used to determine the optimal radial configuration for the distribution network.…”

Section: Introductionmentioning

confidence: 99%

Impact of battery energy storage, controllable load and network reconfiguration on contemporary distribution network under uncertain environment

Sharma

Niazi

Verma

et al. 2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…However, both the studies did not consider renewable DG and reconfiguration. Researchers have also used other optimization techniques such as simulated annealing, artificial immune algorithm, vaccine‐enhanced artificial immune system, modified plant growth simulation algorithm, PSO, GA, runner root algorithm, harmony search algorithm, artificial bee colony algorithm, hybrid Harmony search and particle artificial bee colony algorithm, interval analysis, stochastic MILP, Ant Colony Optimization, hybrid receding horizon control and scenario analysis, nondominated sorting GA, fuzzy mutated GA, tabu search, Benders decomposition approach, and evolutionary programming . Summary of the reviewed literature is presented in Tables and .…”

Section: Introductionmentioning

confidence: 99%

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

show abstract

Genetic Algorithm Based Network Reconfiguration in Distribution Systems with Multiple DGs for Time Varying Loads

Cited by 33 publications

References 17 publications

Application of Genetic Algorithm to Network Feeder Reconfiguration in Radial Distribution System

Application of Genetic Algorithm to Network Feeder Reconfiguration in Radial Distribution System

Impact of battery energy storage, controllable load and network reconfiguration on contemporary distribution network under uncertain environment

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Contact Info

Product

Resources

About