Real power loss reduction in distribution network through Distributed Generation integration by implementing SPSO

2016 ◽  
Author(s):  
Sunaina Saini ◽  
Gagandeep Kaur
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Distribution Network ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction

scholarly journals Multi-Objective Planning for Optimal Allocation of DG and RPC Units in Radial Distribution System Using Genetic Algorithm

10.29007/bngk ◽  
2018 ◽  
Author(s):  
Jaydeepsinh Sarvaiya ◽  
Mahipalsinh Chudasama
Keyword(s):  
Genetic Algorithm ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Cost Effective ◽  
Loss Reduction ◽  
Effective Solution ◽  
Radial Distribution System ◽  
Real Power ◽  
Power Loss Reduction

DG penetration is continuously increased across distribution network not only to reduce carbon emission, but also to enhance the performance of the distribution network. In a restructured environment any distribution utility need to address DG placement and sizing problem to find a cost effective solution for the specific investment. Most of the authors have attempted to solve the problem based on real power loss reduction across the network. Some authors consider voltage stability based analysis for increased loadability of network with real power loss. However, optimal reactive power compensation also need to be incorporated for a cost effective solution. In this paper an attempt has been made to address various types of DG and RPC units citing and sizing problem with multi-objectives consists real power loss reduction and VSI improvement. A new approach includes development of cost function to find cost-effective solution for distribution network. Evolutionary based Genetic Algorithm used to optimize the objective function. Proposed algorithm is tested onIEEE-33 bus radial distribution system.

scholarly journals Teaching-Learning Based Optimization Approach for Determining Size and Location of Distributed Generation for Real Power Loss Reduction on Nigerian Grid

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
M. O. Okelola ◽  
O.E Olabode ◽  
T.O. Ajewole
Keyword(s):  
Power System ◽  
Distributed Generation ◽  
Power Loss ◽  
Optimization Technique ◽  
Stability Index ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

The ever increasing sensitization on the need for clean energies that are not only environmental friendly but also have comparative cost advantages encourages the use of distributed generation. Using distributed generation at the load ends or close to the load centers has not only reduced carbon emission, but also improves power system performances. Presented in this paper is the adoption of Teaching-Learning Based Optimization technique for determining the most suitable site and size of distributed generation for real power loss reduction on Nigerian power system. Backward/Forward Sweep technique was employed for the power flow analysis, while the suitable locations of the distributed generations were pre-selected using Voltage Stability Index and Teaching-Learning Based Optimization technique was employed to establish the optimal location and the optimum size of the required distributed generation. This approach was demonstrated on the IEEE 34-bus test system, with the placement of 1 kW DG at bus 11 of the system. The aggregate real power loss diminished from 571 kW to 208.5954 kW (63.5726% reduction), while Voltage Stability Index and voltage profile of the system also improved remarkably. Also, by placing distributed generation on typical Nigerian 11 kV feeder, the real power loss reduced from 1.1 kW to 0.75 kW while the magnitude of bus voltage increased from 0.8295 to 0.8456 p.u. Based on the results of this analysis, Teaching-Learning Based Optimization has demonstrated excellent performance on the two test cases and therefore would be a tool to adopt on the Nigerian radial distribution system.

scholarly journals Real power loss reduction by hyena optimizer algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 186
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Mathematical Modelling ◽  
Convergent Evolution ◽  
Power Loss ◽  
Social Relationship ◽  
Reactive Power ◽  
Scent Marking ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Power Problem

<p><span>To solve optimal reactive power problem this paper projects Hyena Optimizer (HO) algorithm and it inspired from the behaviour of Hyena. Collaborative behaviour &amp; Social relationship between Hyenas is the key conception in this algorithm. Hyenas a form of carnivoran mammal &amp; deeds are analogous to canines in several elements of convergent evolution. Hyenas catch the prey with their teeth rather than claws – possess hardened skin feet with large, blunt, no retractable claws are adapted for running and make sharp turns. However, the hyenas' grooming, scent marking, defecating habits, mating and parental behaviour are constant with the deeds of other feliforms. Mathematical modelling is formulated for the basic attributes of Hyena. Standard IEEE 14,300 bus test systems used to analyze the performance of Hyena Optimizer (HO) algorithm. Loss has been reduced with control variables are within the limits.</span></p>

Real Power Loss Reduction by Billfish and Red Mullet Optimization Algorithms

2021 ◽  
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Voltage Stability ◽  
Reactive Power ◽  
Smart Cities ◽  
Fitness Function ◽  
Loss Reduction ◽  
Real Power ◽  
Red Mullet ◽  
Power Loss Reduction ◽  
Continuous Power

In this paper Billfish Optimization Algorithm (BOA) and Red Mullet Optimization (RMO) Algorithm has been designed for voltage stability enhancement and power loss reduction. Electrical Power is one among vital need in the society and also it plays lead role in formation of smart cities. Continuous power supply is essential and mainly quality of the power should be maintained in good mode. In this work real power loss reduction is key objective. Natural hunting actions of Billfish over pilchards are utilized to model the algorithm. Candidate solutions in the projected algorithm are Billfish and population in the exploration space is arbitrarily engendered. Movement of Billfish is high, it will attack the pilchards vigorously and it can’t escape from the attack done by the group of Billfish. Then in this paper Red Mullet Optimization (RMO) Algorithm is proposed to solve optimal reactive power problem. Projected RMO algorithm modeled based on the behavior and characteristics of red mullet. As a group they hunt for the prey and in each group there will be chaser and blocker. When the prey approaches any one of the blocker red mullet then automatically it will turn as new chaser. So roles will interchangeable and very much flexible. At any time chaser will become blocker and any of the blocker will become a chaser with respect to prey position and conditions. Then in that particular area when all the preys are hunted completed then red mullet group will change the area. So there will be flexibility and changing the role quickly with respect to prey position. Alike to that with reference to the fitness function the particle will be chosen as chaser. By means of considering L (voltage stability) - index BOA, and RMO algorithms verified in IEEE 30- bus system. Then without L-index BOA and RMO algorithms is appraised in 30 bus test systems. Both BOA and RMO algorithms condensed the power loss proficiently with improvement in voltage stability and minimization of voltage deviation.

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