Implementation of DG for loss minimization and voltage profile in distribution system

Rahim, Siti Rafidah Abdul; Rahman, Titik Khawa Abdul; Musirin, Ismail; Hussain, M. H.; Sulaiman, Mohd Herwan; Alfandi, Omar; Isa, Zainuddin Mat

doi:10.1109/peoco.2010.5559251

Cited by 15 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Table I, in [1], [4][5][6][7][8][9][10][11][12][13], the planning problem is done either with one or multi level load models, and moreover only one load type is considered in their formulations. The drawback of these assumptions is that there are various load types in distribution networks which their profiles are different.…”

Section: Why New Model For Dg Planning?mentioning

confidence: 99%

Optimal planning of dispatchable and non-dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization

Nasri¹,

Golshan²,

Nejad³

2012

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

SUMMARY Distribution Companies (DisCos) play a pivotal role in developing new planning strategies for maintaining the proper operation and efficiency of distribution networks. Moreover, power system deregulation, demand increasing, shortage of transmission capacities and interests in renewable sources have led to the increased studies on integration of distributed generation (DG) into the distribution systems. Hence, proper planning of DG units is important for obtaining their maximum potential benefits from the perspective of DisCos. This paper aims to present a new DG planning model. This new model would help planners to find optimal planning of different types of DG units, dispatchable or non‐dispatchable, in distribution systems for minimizing system's annual energy loss. The planning problem is formulated to determine the optimal hourly production profile, power factor and penetration of DG units. A particle swarm optimization algorithm, implemented in MATLAB software, is used for finding the optimal solution of planning problem. The proposed approach has been evaluated on 69‐Bus Test System in matpower 4.0, with different scenarios in view of DG units' combination. The results show that a significant reduction in energy loss is achieved and the constraints including voltage limits and the feeders' capacity are satisfied. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Section: Why New Model For Dg Planning?mentioning

confidence: 99%

Optimal planning of dispatchable and non-dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization

Nasri¹,

Golshan²,

Nejad³

2012

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

show abstract

“…With fast infiltration of DG into distribution systems, it is basic to evaluate its effects to power system precisely so that these DG units be connected in a way that avoid degradation of power quality, flexibility in control of the utility system and increase reliability. Then again, DG can possibly enhance distribution system performance and it ought to be encouraged [9][10][11][12][13][14]. In this way, it has turned out to be critical to study changes that DG acquires with a change its area or size of DG.…”

Section: Introductionmentioning

confidence: 99%

Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system.

show abstract

“…It is known that the DG can help to reduce the utilization of fossil fuel, environmental impact and electrical line power losses. It also helps to improve power system voltage profile [2]. Unfortunately, installation at unsuitable location and size of the DG could cause effect in the opposite way such as increase of electrical line power losses.…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Distributed Generation Using Bacterial Foraging Optimization Algorithm

Sodsri

Sookananta

Pusayatanont

2015

AMM

View full text Add to dashboard Cite

This paper presents the determination of the optimal distributed generation (DG) placement using bacterial foraging optimization algorithm (BFOA). The BFO mimics the seeking-nutrient behavior of the E. coli bacteria. It is utilized here to find the location and size of the DG installation in radial distribution system in order to obtain minimum system losses. The operation constraints include bus voltage limits, distribution line thermal limits, system power balance and generation power limits. The algorithm is tested on the IEEE 33 bus system. The result shows that the algorithm could be used as an alternative to the other techniques and improvement of the algorithm is required for acceleration and better accuracy of the calculation.

show abstract

Implementation of DG for loss minimization and voltage profile in distribution system

Cited by 15 publications

References 15 publications

Optimal planning of dispatchable and non-dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization

Optimal planning of dispatchable and non-dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization

Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

Optimal Placement of Distributed Generation Using Bacterial Foraging Optimization Algorithm

Contact Info

Product

Resources

About