Optimal reconfiguration of radial distribution systems to maximize loadability

Venkatesh, Bala; Rakesh, R.; Gooi, Hoay Beng

doi:10.1109/pes.2004.1372823

Cited by 62 publications

(96 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Efficiency of the proposed LFA algorithm in terms of required time and space complexity is analyzed, and the proposed method is tested on 2 different RDNs namely 33-bus and 69-bus RDN. Data of these 2 networks can be obtained from Venkatesh et al 13,14 respectively. Table 2, whereas Table 3 contains details of the converge voltage magnitude for 69-bus system obtained on implementing the proposed LFA algorithm, which are exactly matching with one reported in Sharma et al 17 Singh and Ghose 22 The simulation is carried out using 64-bit quad core Intel Xeon Phi processors.…”

Section: Results and Efficiency Considerationsmentioning

confidence: 99%

Faster load flow algorithm for radial distribution network using graph theory

Sharma

Chaturvedi

Saxena

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Since last 3 decades, load flow solutions have enjoyed success on different fronts. Primarily, the importance and utility of these algorithms is assessed using performance measures, which usually include issue like implementation complicacy, optimized execution time, and memory storage. In this work, a graph‐theoretical approach is used to facilitate load flow solutions for a static network topology. Algorithm is tested for 2 different radial distribution topologies, and its deployment for both of these network finally results in phenomenal saving on 2 important algorithm performance measures, ie, time and space complexity. Obtained phenomenal saving for both of these 2 parameters is compared with earlier reported work on statistical basis.

show abstract

Section: Results and Efficiency Considerationsmentioning

confidence: 99%

Faster load flow algorithm for radial distribution network using graph theory

Sharma

Chaturvedi

Saxena

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…32 In fact, the main reason for instability is the network's weakness to provide enough reactive power of loads. 33 Voltage stability is related to network load ability. According to Poornazaryan et al, 34 limit of maximum loading is the distance between the loading from minimum voltage profile and voltage collapse.…”

Section: Voltage Stabilitymentioning

confidence: 99%

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Increasing integration of local generators is a challenge in the planning, design, and operation of the distribution system. The distributed energy resources, especially renewable ones, have caused dispersion of generation units along the feeder. This paper presents a dual index combination of the penetration level and the dispersion factor of distributed generation. Maximum penetration level of distributed generation was determined by a multiobjective function of the proposed dual index. This function has subroutines based on voltage stability, active and reactive power flows, voltage profile limits, and power loss that are restricted by technical constraints associated system with shunt compensator. A limited memory quasi‐Newton trust‐region algorithm was developed to optimize the objective function by using the first‐ and second‐order sensitivities of the nonlinear objective function and a trust region based on the constraints. Two networks were used for testing, and obtained results revealed accuracy and validity of the proposed method.

show abstract

“…P DGi is a continuous variable from zero to the maximum DG capacity in this paper. Reactive power of DG is calculated with Equation (17). The initial number of chromosome is 300.…”

Section: Caga Application To the Optimizationmentioning

confidence: 99%

DG location and capacity optimization considering several objectives with cloud theory adapted GA

Lou

et al. 2013

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

SUMMARY How to decide the location and the capacity of distributed generators (DGs) in the distribution network is a complex and hard work. In this paper, several technical indexes involving energy loss, voltage quality and stability, and line loadability are considered to optimize DG capacity and location. It is an optimization problem wherein both discrete and continuous variables are involved, and cloud theory adapted genetic algorithm (CAGA) is employed to avoid search exploration and long time search. The weights of the indexes are decided by judgment matrix. DGs in a 28‐node distributed network are employed to be optimized quickly to improve the indexes aforementioned to demonstrate the CAGA developed in this paper. Meanwhile, the optimization results are compared with traditional GA. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Optimal reconfiguration of radial distribution systems to maximize loadability

Cited by 62 publications

References 0 publications

Faster load flow algorithm for radial distribution network using graph theory

Faster load flow algorithm for radial distribution network using graph theory

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

DG location and capacity optimization considering several objectives with cloud theory adapted GA

Contact Info

Product

Resources

About