An analytical approach for sizing and siting of DGs in balanced radial distribution networks for loss minimization

Viral, Rajkumar; Khatod, Dheeraj Kumar

doi:10.1016/j.ijepes.2014.11.017

Cited by 201 publications

(95 citation statements)

References 31 publications

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“…Rajkumar Viral et al [26] have presented an analytical approach to determine the optimal setting and sizing of distributed generation (DG) units in balanced radial distribution network to minimize the power loss of the system. Their proposed analytical expressions were based on a minimizing the loss associated with the active and reactive component of branch currents by placing the DG at various locations.…”

Section: Recent Related Workmentioning

confidence: 99%

Hybrid Technique for Locating and Sizing of Renewable Energy Resources in Power System

Durairasan¹,

Kalaiselvan²,

Sait³

2017

Journal of Electrical Engineering and Technology

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-In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem

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Section: Recent Related Workmentioning

confidence: 99%

Hybrid Technique for Locating and Sizing of Renewable Energy Resources in Power System

Durairasan¹,

Kalaiselvan²,

Sait³

2017

Journal of Electrical Engineering and Technology

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“…A voltage stability index [6,7] and power stability index [8] are developed for optimal placement of DG in distribution systems. A new analytical expression is proposed in [9] for finding optimal size of distributed generation and it is noticed that optimal power factor of DG for minimizing losses is in close agreement with load power factor and depends on location as well. In recent years, major focus of the system operators, decision makers and various stakeholders is to understand the current status, barriers and challenges for better planning and management in the field of solar based power generation [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Placement of Solar Cell Distributed Generator Suitable for Integrated Power System Environment

2018

Teh. vjesn.

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A novel fuzzified Clustered Gravitational Search Algorithm (CGSA) has been employed for solving multi-objective problem formulated for solar based distributed generation. Optimal sizing and placement of solar distributed generation is considered. High solar penetration can lead to high-risk level in power system reliability. In order to maintain the system reliability, solar power dispatch is usually restricted based on the reliability level of the system. Two conflicting objective functions such as power loss and reliability level of the system are also considered for solving optimal placement of solar distributed generation (SDG). Binary coded CGSA is employed for solving optimal placement of SDG and sizing is determined using real coded CGSA. The fuzzy membership function for each objective is designed and multi-objective optimal placement problem has been presented. The proposed method is validated on IEEE standard 69-bus radial distribution networks. The efficiency of the proposed optimization technique is validated by comparing the results with other results available in the existing articles.

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“…In addition, in iterative techniques, the sizes of oncoming DG(s) are calculated based on the already installed DG(s), which limits the range for the optimal sizes of the oncoming DGs. A recent example of analytical method based optimal sitting is discussed in [30] where loss minimization is done by reducing the line currents after placing DGs at various locations. Moreover, based on the found optimal sizes, the combination of DG types is also suggested.…”

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

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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.

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An analytical approach for sizing and siting of DGs in balanced radial distribution networks for loss minimization

Cited by 201 publications

References 31 publications

Hybrid Technique for Locating and Sizing of Renewable Energy Resources in Power System

Hybrid Technique for Locating and Sizing of Renewable Energy Resources in Power System

Optimal Sizing and Placement of Solar Cell Distributed Generator Suitable for Integrated Power System Environment

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

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