GA based energy loss minimization approach for optimal sizing &amp; placement of distributed generation

Singh, Deependra; Singh, Devender; Verma, Khushboo

doi:10.3233/kes-2008-12206

Cited by 35 publications

(25 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The power carrying capacity of feeders is represented by MVA limits (S k ) through any feeder (k) must be well within the maximum thermal capacity (S k max ) of the lines. References (Keane et al, 2005;Popović et al, 2005;Gautam et al 2007;El-Khattam et al, 2005;Haghifam et al, 2008;Singh et al, 209;Jabr et al, 2009;Algarni et al, 2009;Keane et al, 2007;Singh et al, 2008;Krueasuk et al, 2005;Lalitha et al, 2010;Harrison et al, 2007;Harrison et al, 2008;Mao et al, 2003;Wang et al, 2010;EL-Khattam et al, 2006;Rodriguez et al, 2009;Atwa et al, 2010) have considered this constraint and expressed as: …”

Section: Reactive Power Balance Limit (Rpbl)mentioning

confidence: 99%

“…Simulated annealing(SA) Sutthibun et al, 2010 . Evolutionary algorithms(EA) Kim et al, 1998;Popović et al, 2005;Celli et al, 2005;Carpinelli et al, 2005;Celli et al, 2005;Carpinelli et al, 2005;Haghifam et al, 2008;Abou et al, 2010 ;Kim et al, 2002;Haghifam et al, 2008;Singh et al, 2008;Harrison et al, 2007;Harrison et al, 2008;Teng et al, 2002;Borges et al, 2006 ;Rodriguez et al, 2009. Tabu search algorithm(TSA) Golshan et al, 2006 ;Nara et al, 2001 Particle swarm optimization(PSO) Krueasuk et al, 2005 ;Lalitha et al, 2010.…”

Section: Fuzzy Set Theory (Fst)mentioning

confidence: 99%

“…Improved Herefoord Ranch Algorithm (HRA) was implemented with single objective function to minimize the active power loss and compared with Second-order method, simple GA (SGA), HRA, improved SGA in (Kim et al, 1998). GA has been used in (Popović et al, 2005;Singh et al, 2008;Harrison et al, 2007 ) to handle single objective. It has been used in Carpinelli et al, 2005;Abou et al, 2010;Rodriguez et al, 2009) to handle multi-objective (MO) model including ε-constraint technique in Carpinelli et al, 2005) for DGP problem.…”

Section: Evolutionary Algorithms (Eas)mentioning

confidence: 99%

“…The overall impact is positive due to the active power injection with objective to minimize the losses (1). In (Acharya et al, 2006;Singh et al, 2008;Lalitha et al, 2010;EL-Khattam et al, 2006;Hung et al, 2010) , problem was formulated using the expression for the total real loss (P L ) in power systems, as represented by (7) , popularly known as "exact loss formula" (Kothari et al, 2006).…”

Section: Minimize Line Lossmentioning

confidence: 99%

See 3 more Smart Citations

Review of distributed generation planning: objectives, constraints, and algorithms

Payasi¹,

Singh²,

Singh³

2011

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

The Distributed Generation (DG) technologies, which include both conventional and non-conventional type of energy sources for generating power, are gaining momentum and play major role in distribution system as an alternative distribution system planning option. The penetration of DGs is potentially beneficial if distributed generation planning (DGP) is optimal i.e. their site and size are selected optimally by optimization of single or multi-objective function under certain operating constraints. Many researchers have presented some rigorous optimization-based methods for DGP. This paper will review the various objectives, different constraints as well as optimization based models using conventional algorithms, intelligent searches and fuzzy set applications in DGP.

show abstract

Section: Reactive Power Balance Limit (Rpbl)mentioning

confidence: 99%

Section: Fuzzy Set Theory (Fst)mentioning

confidence: 99%

Section: Evolutionary Algorithms (Eas)mentioning

confidence: 99%

Section: Minimize Line Lossmentioning

confidence: 99%

See 2 more Smart Citations

Review of distributed generation planning: objectives, constraints, and algorithms

Payasi¹,

Singh²,

Singh³

2011

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

show abstract

“…The integration of DG(s) becomes the most economical solution to meet the increased demand due to load growth in the conventional system [1]. Technological advancements in generators, power electronics devices and storage devices further fuel this trend.…”

Section: Introductionmentioning

confidence: 99%

Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement

et al. 2016

View full text Add to dashboard Cite

This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced to select the optimal location(s) for DG placement. Exact loss formula based analytical expressions are derived for calculating the optimal sizes of any number of DGs simultaneously. Since neither optimizing the location nor optimizing the size is done iteratively, like existing methods do, the simulation time is reduced considerably. The exhaustive method is used to find the operational power factor, and it is shown with the results that the losses are further reduced and voltage profile is improved by operating the DGs at operational power factor. Results for power loss reduction and voltage profile improvement in IEEE 37 and 119 node radial distribution systems are presented and compared with the the loss sensitivity factor (LSF) method, improved analytical (IA) and exhaustive load flow method (ELF). The comparison for operational power factor and other power factors is also presented.

show abstract

A time‐varying load‐based analytical approach for DG optimization in the distribution network

Iqteit

Arsoy

Çakır

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents a novel analytical approach to estimate the optimal generation profiles/size and location of a distributed generation (DG) unit that can be integrated into distribution network to attain the lowest level of active loss. This approach considered the impact of significant parameters including daily load profiles, best power limits of a DG unit, and varying injected power profiles of the substation on results of minimum active loss of the distribution network and optimal calculations of DG. Additionally, the DG placement was speedily identified in this approach by using a new active loss expression along with four different scenarios. These scenarios considered a number of assumptions on the load profiles and on the state variables (voltage and angle). The obtained optimal active generation profile was also used to manage the uncertainty in the energy of the wind and photovoltaic distribution generators for achieving the highest loss reduction. Additionally, the exhaustive load flow method was used to check the validity of the analytical approach by using 13‐bus and 69‐bus distribution networks. The results obtained suggest the effectiveness of this approach in estimating the optimal location and profile/size of DG.

show abstract

GA based energy loss minimization approach for optimal sizing & placement of distributed generation

Cited by 35 publications

References 14 publications

Review of distributed generation planning: objectives, constraints, and algorithms

Review of distributed generation planning: objectives, constraints, and algorithms

Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement

A time‐varying load‐based analytical approach for DG optimization in the distribution network

Contact Info

Product

Resources

About