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Placement and Sizing of Distributed Generation on Distribution Systems with a Multi-Objective Particle Swarm Optimization and Analytical Approach: A Review

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Pooja Shivwanshi, Sameena Elias Mubeen

Pooja Shivwanshi and Sameena Elias Mubeen. Placement and Sizing of Distributed Generation on Distribution Systems with a Multi-Objective Particle Swarm Optimization and Analytical Approach: A Review. International Journal of Computer Applications 161(12):21-24, March 2017. BibTeX

	author = {Pooja Shivwanshi and Sameena Elias Mubeen},
	title = {Placement and Sizing of Distributed Generation on Distribution Systems with a Multi-Objective Particle Swarm Optimization and Analytical Approach: A Review},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {12},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {21-24},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913111},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The Distributed generations (DGs) have number of benefits in the electric power industry, such as improvement of voltage stability, enhancement of reliability and power quality. This paper compares the DG placement result of analytical approach with the Multi-Objective Particle Swarm Optimization (MOPSO). The analytical method is based on a formulation for the power flow problem. A priority is loss sensitivity to determine the best locations of applicant distributed generation units. The multi-objective particle swarm optimization determines the optimal DGs places and sizes. The MOPSO improves voltage profile and stability, power-loss reduction, and reliability enhancement. The results show that the analytical method could lead to optimal or near-optimal result, while requiring lower computational effort.


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DG placement, DG sizing, distributed generation, Analytical optimization method, multiobjective particle swarm optimization (MOPSO).