Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems

Savier, J S; Das, Debapriya

doi:10.1109/tpwrd.2007.905370

Cited by 478 publications

(207 citation statements)

References 30 publications

Supporting

Mentioning

205

Contrasting

Unclassified

Order By: Relevance

“…Test System 1 is a modified version of the IEEE-69 bus Test Distribution System found in [16]. Test System 2 is an entire substation model received from a local utility.…”

Section: Resultsmentioning

confidence: 99%

Optimal Partitioning of Distribution Networks for Micro-Grid Operation

Kimble¹,

Vedullapalli²,

Makram³

2017

JPEE

View full text Add to dashboard Cite

A great concern for the modern distribution grid is how well it can withstand and respond to adverse conditions. One way that utilities are addressing this issue is by adding redundancy to their systems. Likewise, distributed generation (DG) is becoming an increasingly popular asset at the distribution level and the idea of micro-grids operating as standalone systems apart from the bulk electric grid is quickly becoming a reality. This allows for greater flexibility as systems can now take on exponentially more configurations than the radial, one-way distribution systems of the past. These added capabilities, however, make the system reconfiguration with a much more complex problem causing utilities to question if they are operating their distribution systems optimally. In addition, tools like Supervisory Control and Data Acquisition (SCADA) and Distribution Automation (DA) allow for systems to be reconfigured faster than humans can make decisions on how to reconfigure them. As a result, this paper seeks to develop an automated partitioning scheme for distribution systems that can respond to varying system conditions while ensuring a variety of operational constraints on the final configuration. It uses linear programming and graph theory. Power flow is calculated externally to the LP and a feedback loop is used to recalculate the solution if a violation is found. Application to test systems shows that it can reconfigure systems containing any number of loops resulting in a radial configuration. It can connect multiple sources to a single micro-grid if more capacity is needed to supply the microgrid's load.

show abstract

“…Test System 1 is a modified version of the IEEE-69 bus Test Distribution System found in [16]. Test System 2 is an entire substation model received from a local utility.…”

Section: Resultsmentioning

confidence: 99%

Optimal Partitioning of Distribution Networks for Micro-Grid Operation

Kimble¹,

Vedullapalli²,

Makram³

2017

JPEE

View full text Add to dashboard Cite

show abstract

“…Savier and Das [14] proposed an LA technique for the deregulated environment before and after reconfiguration. The method was formed in a quadratic way and stood on the determination of two different parts of current in each branch.…”

Section: Related Workmentioning

confidence: 99%

Effective Loss Minimization and Allocation of Unbalanced Distribution Network

Kaur

Ghosh

2017

Energies

View full text Add to dashboard Cite

An efficient distribution network must be able to supply power with good voltage profile. The main objective of the proposed work is to allocate losses of the unbalanced distribution network by the firefly algorithm in regulated and deregulated environments before and after loss minimization. Reconfiguration is one of the methods for loss reduction of unbalanced distribution network. Further, optimal placement of distributed generation and capacitor in the reconfigured unbalanced distribution network can further reduce the loss. The results of reconfigured unbalanced distribution network in regulated environment have already been reported. In this paper reconfiguration of an unbalanced distribution network in a deregulated environment is also carried out using an established Fuzzy Firefly algorithm. Loss sensitivity factor of unbalanced distribution networks is used to get the appropriate location of distributed generation and capacitor to be placed in the unbalanced distribution network. Their ratings have been found out by using bacteria foraging optimization algorithm (BFOA). The suggested loss allocation method using Firefly algorithm is implemented at first on 13 node unbalanced distribution network to check the performance of the proposed loss allocation method when compared to other available method. Finally the proposed method has been implemented on 25 node unbalanced distribution network. Both of the implementations are carried out under MATLAB environment.

show abstract

“…When the high DG penetration system is in normal state, the system transfers to the generation state, and the generation reliability assessment process does not need to use FMEA method to be simplified. And the FMEA method is just simply mentioned as [37] in low DG penetration section in this paper because it is a mature tool to simplify the network and it is not the core work of this paper.…”

Section: Simulation Principlesmentioning

confidence: 99%

“…The DG integration will have an impact on the traditional FMEA process. The FMEA process in [37] is used in this paper.…”

Section: Failure Mode and Effect Analysis (Fmea) Processmentioning

confidence: 99%

“…With flexible operational mode and environment-friendly features, more and more distributed generations are integrated into the distribution network, which brings benefit on the carbon reduction of distribution system [20]. As two basic methods of loss of expected energy (LOEE) for the distribution system with DG, the studies of analytical method [21][22][23][24][25][26][27][28][29] and Monte Carlo method [30][31][32][33][34][35][36] have made some progress. These studies indicate that the adoption of DG is actually equals to increasing the number and capacity of the backup of distribution system, thus improving the carbon reduction of the distribution system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-carbon benefit analysis on DG penetration distribution system

Liu

et al. 2015

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

In response to the severe energy and environmental issues, CO 2 emission reduction and low-carbon development are inevitable. China has become the biggest CO 2 emitter in the world since 2006. As a major CO 2 emission source in China, the power industry is facing greater pressure for carbon emission abatement. By applying various low-carbon power technologies and mechanisms, the potential for CO 2 emission reduction in power systems is considerable. This paper proposes a pseudo-sequential Monte Carlo simulation method for the low-carbon benefit evaluation of distribution system including distributed wind turbines, solar array and battery energy storage systems. The hourly sequential models and state variation models for wind turbines, solar array and battery are established. The non-power components are sequentially sampled and the components of the wind turbine, the solar array and the battery are non-sequentially sampled. The failure modes and effect analysis (FMEA) procedure for the system are discussed and the heuristic load shedding approach is used, then the low-carbon benefit evaluation procedure is illustrated. Based on this, the system state transition sampling method for calculating the loss of expected energy index with high DG penetration is proposed. The state transition models for non-active elements and DGs are established. The validity of the proposed method is demonstrated by a study case. The method can be used for the low-carbon benefit analysis on DG penetration distribution networks.

show abstract

Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems

Cited by 478 publications

References 30 publications

Optimal Partitioning of Distribution Networks for Micro-Grid Operation

Optimal Partitioning of Distribution Networks for Micro-Grid Operation

Effective Loss Minimization and Allocation of Unbalanced Distribution Network

Low-carbon benefit analysis on DG penetration distribution system

Contact Info

Product

Resources

About