Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm

► A discrete teaching–learning-based optimization method is employed. ► Optimal sizes and locations to connect DG systems are determined. ► Effectiveness of the algorithm has been tested on two sample networks. ► We prove this approach is highly suitable in DG placement. In this paper, a method whic...

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Bibliographic Details
Published in:International journal of electrical power & energy systems Vol. 50; pp. 65 - 75
Main Authors: Martín García, Juan Andrés, Gil Mena, Antonio José
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.09.2013
Elsevier
Subjects:
Applied sciences
Distributed Generation (DG)
Distribution systems
Electric power plants
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Miscellaneous
Modified Teaching–Learning-Based Optimization (MTLBO)
Optimal placement
Power loss reduction
Power networks and lines
Distribution systems
Modified Teaching–Learning-Based Optimization (MTLBO)
Power loss reduction
Optimal placement
Distributed Generation (DG)
Performance evaluation
Positioning
Modified Teaching-Learning-Based
Optimization method
Multiobjective programming
Distributed power generation
Optimization
Optimal design
Learning
Facility location
Power losses
Learning algorithm
Distribution network
Mixed integer programming
Nonlinear problems
Optimization (MTLBO)
Electrical network
Numerical simulation
Objective function
Electric power production
Effectiveness factor
Comparative study
ISSN:0142-0615, 1879-3517
Online Access:Get full text
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Description
Summary:► A discrete teaching–learning-based optimization method is employed. ► Optimal sizes and locations to connect DG systems are determined. ► Effectiveness of the algorithm has been tested on two sample networks. ► We prove this approach is highly suitable in DG placement. In this paper, a method which employs a Modified Teaching–Learning Based Optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Generation (DG) units in distribution systems. For the sake of clarity, and without loss of generality, the objective function considered is to minimize total electrical power losses, although the problem can be easily configured as multi-objective (other objective functions can be considered at the same time), where the optimal location of DG systems, along with their sizes, are simultaneously obtained. The optimal DG site and size problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used by researchers to solve this problem because of their independence from type of the objective function and constraints. Recently, a new evolutionary method called Teaching–Learning Based Optimization (TLBO) algorithm has been presented, which is modified and used in this paper to find the best sites to connect DG systems in a distribution network, choosing among a large number of potential combinations. A comparison between the proposed algorithm and a brute force method is performed. Besides this, it has also been carried out a comparison using several results available in other articles published by others authors. Numerical results for two test distribution systems have been presented in order to show the effectiveness of the proposed approach.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2013.02.023