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AUGMENTED LAGRANGE HOPFIELD NEURAL NETWORK BASED METHOD FOR UNIT COMMITMENT

Khai Phuc Nguyen 1, *
Dieu Ngoc Vo 1
Tu Phan Vu 1, 2
  1. University of Technology, VNU-HCM
  2. VNU- HCM
Correspondence to: Khai Phuc Nguyen, University of Technology, VNU-HCM. Email: pvphuc@hcmuns.edu.vn.
Volume & Issue: Vol. 15 No. 2 (2012) | Page No.: 39-49 | DOI: 10.32508/stdj.v15i2.1789
Published: 2012-06-30

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This article is published with open access by Viet Nam National University, Ho Chi Minh City, Viet Nam. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Abstract

This paper proposed an enhanced merit order (EMO) and augmented Lagrange Hopfield neural network (ALHN) for solving unit commitment problem. This problem is solved on 2 stages. At first, with the heuristic search EMO method we plan the unit scheduling. And then, we use ALHN, a continuous Hopfield neural network combines with augmented Lagrange relaxation, to solve the economic dispatch problem. The proposed method is tested on systems with 10 units, 17 units and up to 100 units. The obtained results is compared to conventional priority list (PL-ALHN) and other methods in literature. Test results show that the proposed method is totally more efficient than PLALHN and others for finding optimal solution of unit commitment problem. And the computer time of proposed method is vastly faster than other methods.

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