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Multi-Objective Metaheuristic Algorithm for Optimal Distributed Generator Placement and Profit Analysis

Multi-Objective Metaheuristic Algorithm for Optimal Distributed Generator Placement and Profit... The drastic growth in power demand and the high capital investment for infrastructure developments, power system utilities are forced to concentrate on improving the reliability and efficiency by integrating Distributed Generators(DG) to the existing grid. The integration of DG to grid faces many challenges. This paper presents the implementation of an algorithm based on multiobjective function for optimal placement of Distributed Generation(DG), which is one among the major challenges in DG integration. Optimal placement and sizing of Distributed Generator (DG), is done with the objective of loss minimization and maximization of loading capability without affecting voltage stability of the system. Flower Pollination Algorithm (FPA), which is a metaheuristic algorithm is used to solve this problem, since this algorithm is based on updating tuning parameters to obtain the most effective solution. The major expenditure in DG integration, as installation costs, operational cost and maintenance cost are taken into consideration and a cost based analysis is also carried out in this work to check the feasibility of optimum placement and sizing in a DG interconnected system. The benefits due to DG placement at optimum location with suitable size are the loss reduction and cost reduction. The performance of the proposed algorithm is tested on standard IEEE 33 bus and IEEE 69 bus systems. The effectiveness of the proposed algorithm is also tested on a 301 bus distribution system of Kerala State Electricity Board (KSEB). The test results are compared with other metaheuristic methods like Discrete Artificial Bee Colony algorithm (DABC) and Particle Swam Optimization (PSO) algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technology and Economics of Smart Grids and Sustainable Energy Springer Journals

Multi-Objective Metaheuristic Algorithm for Optimal Distributed Generator Placement and Profit Analysis

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer Nature Singapore Pte Ltd.
Subject
Energy; Energy Systems; Power Electronics, Electrical Machines and Networks; Energy Policy, Economics and Management
eISSN
2199-4706
DOI
10.1007/s40866-019-0067-z
Publisher site
See Article on Publisher Site

Abstract

The drastic growth in power demand and the high capital investment for infrastructure developments, power system utilities are forced to concentrate on improving the reliability and efficiency by integrating Distributed Generators(DG) to the existing grid. The integration of DG to grid faces many challenges. This paper presents the implementation of an algorithm based on multiobjective function for optimal placement of Distributed Generation(DG), which is one among the major challenges in DG integration. Optimal placement and sizing of Distributed Generator (DG), is done with the objective of loss minimization and maximization of loading capability without affecting voltage stability of the system. Flower Pollination Algorithm (FPA), which is a metaheuristic algorithm is used to solve this problem, since this algorithm is based on updating tuning parameters to obtain the most effective solution. The major expenditure in DG integration, as installation costs, operational cost and maintenance cost are taken into consideration and a cost based analysis is also carried out in this work to check the feasibility of optimum placement and sizing in a DG interconnected system. The benefits due to DG placement at optimum location with suitable size are the loss reduction and cost reduction. The performance of the proposed algorithm is tested on standard IEEE 33 bus and IEEE 69 bus systems. The effectiveness of the proposed algorithm is also tested on a 301 bus distribution system of Kerala State Electricity Board (KSEB). The test results are compared with other metaheuristic methods like Discrete Artificial Bee Colony algorithm (DABC) and Particle Swam Optimization (PSO) algorithm.

Journal

Technology and Economics of Smart Grids and Sustainable EnergySpringer Journals

Published: Jul 13, 2019

References