Evolutionary programming techniques for economic load dispatch

Evolutionary programming techniques for economic load dispatch

Evolutionary programming techniques for economic load dispatch

Abstract:

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.
Published in: IEEE Transactions on Evolutionary Computation ( Volume: 7, Issue: 1, Feb 2003 )
Date of Publication: 19 February 2003
ISSN Information:
INSPEC Accession Number: 7549828
Publisher: IEEE
Sponsored by: IEEE Computational Intelligence Society


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Biogeography-Based Optimization for Different Economic Load Dispatch Problems

Biogeography-Based Optimization for Different Economic Load Dispatch Problems

Biogeography-Based Optimization for Different Economic Load Dispatch Problems

Abstract:

This paper presents a biogeography-based optimization (BBO) algorithm to solve both convex and non-convex economic load dispatch (ELD) problems of thermal plants. The proposed methodology can take care of economic dispatch problems involving constraints such as transmission losses, ramp rate limits, valve point loading, multi-fuel options and prohibited operating zones. Biogeography deals with the geographical distribution of biological species. Mathematical models of biogeography describe how a species arises, migrates from one habitat to another and gets wiped out. BBO has some features that are in common with other biology-based optimization methods, like genetic algorithms (GAs) and particle swarm optimization (PSO). This algorithm searches for the global optimum mainly through two steps: migration and mutation. The effectiveness of the proposed algorithm has been verified on four different test systems, both small and large, involving varying degree of complexity. Compared with the other existing techniques, the proposed algorithm has been found to perform better in a number of cases. Considering the quality of the solution obtained, this method seems to be a promising alternative approach for solving the ELD problems in practical power system.
Published in: IEEE Transactions on Power Systems ( Volume: 25, Issue: 2, May 2010 )
Date of Publication: 01 December 2009
ISSN Information:
INSPEC Accession Number: 11256764
Publisher: IEEE

Sponsored by: IEEE Power & Energy Society


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Optimal dynamic economic dispatch of generation

Optimal dynamic economic dispatch of generation

Optimal dynamic economic dispatch of generation

Abstract

This paper presents a review of the research of the optimal power dynamic dispatch problem. The dynamic dispatch problem differs from the static economic dispatch problem by incorporating generator ramp rate constraints. There are two different formulations of this problem in the literature. The first formulation is the optimal control dynamic dispatch (OCDD) where the power system generation has been modeled as a control system and optimization is done in the optimal control setting with respect to the ramp rates as input variables. The second one is a later formulation known as the dynamic economic dispatch (DED) where optimization is done with respect to the dispatchable powers of the committed generation units. In this paper we first outline the two formulations, then present an overview on the mathematical optimization methods, Artificial Intelligence (AI) techniques and hybrid methods used to solve the problem incorporating extended and complex objective functions or constraints. The DED problem in deregulated electricity markets is also reported.

Publisher: ELSEVIER


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A particle swarm optimization for economic dispatch with nonsmooth cost functions

A particle swarm optimization for economic dispatch with nonsmooth cost functions

A particle swarm optimization for economic dispatch with nonsmooth cost functions

Abstract:

This work presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve-point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hopfield neural network approaches.
Published in: IEEE Transactions on Power Systems ( Volume: 20, Issue: 1, Feb. 2005 )
Date of Publication: 31 January 2005
ISSN Information:
INSPEC Accession Number: 8277246
Publisher: IEEE

A particle swarm optimization for economic dispatch with nonsmooth cost functions

A particle swarm optimization for economic dispatch with nonsmooth cost functions

A particle swarm optimization for economic dispatch with nonsmooth cost functions

Abstract:

This work presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve-point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hopfield neural network approaches.
Published in: IEEE Transactions on Power Systems ( Volume: 20, Issue: 1, Feb. 2005 )
Date of Publication: 31 January 2005
ISSN Information:
INSPEC Accession Number: 8277246
Publisher: IEEE

Sponsored by: IEEE Power & Energy Society


What we provide:

Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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  • Complete Code of this paper
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  • A document containing complete explanation of code and research approach
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Evolutionary programming techniques for economic load dispatch

Evolutionary programming techniques for economic load dispatch

Evolutionary programming techniques for economic load dispatch

Abstract:

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.
Published in: IEEE Transactions on Evolutionary Computation ( Volume: 7, Issue: 1, Feb 2003 )
Date of Publication: 19 February 2003
ISSN Information:
INSPEC Accession Number: 7549828
Publisher: IEEE
Sponsored by: IEEE Computational Intelligence Society


What we provide:

Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

Deliverables:-  

  • Complete Code of this paper
  • Complete Code of the approach to be propose
  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work