Cost effective Distributed Generation placement using differential evolution for loss reduction in networked systems

2015 ◽  
Author(s):  
K. Dhananjaya Babu ◽  
A. Lakshmi Devi
Keyword(s):  
Differential Evolution ◽  
Distributed Generation ◽  
Cost Effective ◽  
Networked Systems ◽  
Loss Reduction ◽  
Distributed Generation Placement

scholarly journals Reliability Analysis with Renewable DGs for Loss Reduction in Radial Distributed Generation

2021 ◽  
Vol 309 ◽  
pp. 01071
Author(s):  
R. Kavyasree ◽  
J. Sridevi ◽  
V. Usha Rani
Keyword(s):  
Power Systems ◽  
Distributed Generation ◽  
Distribution Systems ◽  
Reducing Power ◽  
Cost Effective ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Losses ◽  
Joint System ◽  
Effective Manner

Nowadays, in the Evolving Power System, reliability testing plays an important role in the design and implementation of distribution systems that operate in a cost-effective manner with minimal customer load disruption. The distributed generation (DG) will play a major role in emerging Power systems as they use a variety of resources and technologies to harness energy in Power systems by reducing Power losses while maintaining the Voltage profile in the system within the limits set. In this paper, two case studies with one DG and two DGs were analysed. The results obtained showed that the DG Number with the plan will increase the reliability of the joint system. The proven system is verified before the IEEE 6-Bus Radial Distributed System to reflect exposure and impact on ETAP software.

Application of Tabu Search for Optimal Placement and Sizing of Distributed Generation for Loss Reduction

2012 ◽  
Vol 433-440 ◽  
pp. 7190-7194 ◽  
Author(s):  
Nattachote Rugthaicharoencheep ◽  
Thong Lantharthong ◽  
Awiruth Ratreepruk ◽  
Jenwit Ratchatha
Keyword(s):  
Tabu Search ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Power Flow ◽  
Search Algorithm ◽  
Optimal Placement ◽  
Loss Reduction ◽  
Placement Problem ◽  
Bus Voltage

This paper presents the optimal and sizing of distributed generation (DG) placement in a radial distribution system for loss reduction. The main emphasis of this paper is to identify proper locations for installing DGs in a distribution system to reduce active power loss and improve bus voltages. Nevertheless, proper placement and sizing of DG units are not straightforward to be identified as a number of their positions and capacities need to be determined. It is therefore proposed in this paper to solve a DG placement problem based on a Tabu search algorithm. The objective function of the problem is to minimize the system loss subject to power flow constraints, bus voltage limits, pre specified number of DGs, and their allowable total installed capacity, and only one distributed generator for one installation position. The effectiveness of the methodology is demonstrated by a practical sized distribution system consisting of 69 bus and 48 load points. The results show that the optimal DG placement and sizing can be identified to give the minimum power loss while respecting all the constraints.

scholarly journals Distributed Generation Placement in Radial Distribution Networks using a Bat-inspired Algorithm

DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 60-67 ◽  
Author(s):  
John Edwin Candelo-Becerra ◽  
Helman Hernández-Riaño
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Reactive Power ◽  
Distribution Networks ◽  
Power Losses ◽  
Number Of Generators ◽  
Metaheuristic Techniques ◽  
Distributed Generation Placement ◽  
Made In

<p>Distributed generation (DG) is an important issue for distribution networks due to the improvement in power losses, but the location and size of generators could be a difficult task for exact techniques. The metaheuristic techniques have become a better option to determine good solutions and in this paper the application of a bat-inspired algorithm (BA) to a problem of location and size of distributed generation in radial distribution systems is presented. A comparison between particle swarm optimization (PSO) and BA was made in the 33-node and 69-node test feeders, using as scenarios the change in active and reactive power, and the number of generators. PSO and BA found good results for small number and capacities of generators, but BA obtained better results for difficult problems and converged faster for all scenarios. The maximum active power injections to reduce power losses in the distribution networks were found for the five scenarios.</p>

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