Optimal placement and sizing of distributed generation units for power loss reduction using Moth-Flame optimization algorithm

2017 ◽  
Author(s):  
Anurag Das ◽  
Laxmi Srivastava
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Optimal Placement ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Moth Flame Optimization Algorithm

scholarly journals TECHNO-ECONOMIC ASSESMENT FOR DISTRIBUTED GENERATION PLACEMENT IN DISTRIBUTION SYSTEM

KURVATEK ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Sugiarto Kadiman
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Distribution Systems ◽  
Reactive Power ◽  
Test System ◽  
Optimal Placement ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Loss Reduction ◽  
Reduction Index

This paper presents a proposed function which is known as techno-economic model for optimal placement of distributed generation (DG) resources in distribution systems in order to minimize the power losses and improve voltage profile. Combined sensitivity factors (CSF), such real power loss reduction index, reactive power loss reduction index, voltage profile improvement index, and life cycle cost, and particle swarm optimization (PSO) are applied to the proposed technique to obtain the best compromise between these costs. Simulation results on IEEE 14-bus test system are presented to demonstrate the usefulness of the proposed procedure.

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 Optimal placement of distributed generation using colliding bodies optimization

2018 ◽  
Vol 7 (3.3) ◽  
pp. 168
Author(s):  
Gera Kalidas Babu ◽  
P V. Ramana rao
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Solar Photovoltaic ◽  
Load Power ◽  
Colliding Bodies Optimization ◽  
Power Loss Reduction ◽  
Radial Distribution Networks ◽  
Distribution Generation

The present paper foremost objective is to resolve best practicable location of solar photovoltaic distribution generation (DG) of several cases using different distribution load power factors and to analyze power loss reduction. This objective achieved by a recent developed method, the so called colliding bodies’ optimization algorithm, to perceive optimum location. Performances of colliding bodies’ optimization algorithm have been evaluated and compared with other search algorithms. The execution to test viability and efficiency, the proposed collid-ing bodies’ optimization is simulated on standard IEEE 38 bus radial distribution networks. The acquired outcome from colliding bodies Optimization algorithm exhibits the possible location of distributed generation through different pre assumed load power factors compared to the other stochastic search bat and genetic algorithm.  

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