Optimal economic power dispatch using genetic algorithms
19 Apr 1993-pp 157-162
TL;DR: This paper presents the genetic algorithm approach to adaptive optimal economic dispatch of electrical power systems and the suitability of the proposed approach is described for the case of a 15 generator power system.
Abstract: This paper presents the genetic algorithm approach to adaptive optimal economic dispatch of electrical power systems. Genetic algorithms, also termed as the machine learning approach to artificial intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The unique problem solving strategy of the genetic algorithm and their suitability for power system optimization is described. The advantages of the genetic algorithmic approach in terms of problem reduction, flexibility and solution methodology are also discussed. The suitability of the proposed approach is described for the case of a 15 generator power system. >
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TL;DR: The performance of evolutionary programs on ELD problems is examined and modifications to the basic technique are proposed, where adaptation is based on scaled cost and adaptation based on an empirical learning rate are developed.
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.
1,207 citations
01 Feb 1998
TL;DR: In this paper, the authors provide an overview and a list of references on the use of evolutionary algorithms in power systems and related fields, and present two applications of EA for two different problems in Power Systems.
Abstract: This paper provides an overview and a list of references on the use of Evolutionary Algorithms (EA) in Power Systems and related fields. As didactic examples, the paper presents two applications of EA for two different problems in Power Systems.
206 citations
TL;DR: By the proposed approach, learning stagnation is avoided, the neural network stability and accuracy are significantly increased, and the computational performance of unit commitment in a power system is therefore highly improved.
Abstract: A new approach using genetic algorithms based neural networks and dynamic programming (GANN-DP) to solve power system unit commitment problems is proposed in this paper. A set of feasible generator commitment schedules is first formulated by genetic-enhanced neural networks. These pre-committed schedules are then optimized by the dynamic programming technique. By the proposed approach, learning stagnation is avoided. The neural network stability and accuracy are significantly increased. The computational performance of unit commitment in a power system is therefore highly improved. The proposed method has been tested on a practical Taiwan Power (Taipower) thermal system through the utility data. The results demonstrate the feasibility and practicality of this approach.
70 citations
Cites background from "Optimal economic power dispatch usi..."
...Manuscript submitted July 5, 1995; made available for printing April 23 , 1996....
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02 Nov 2003
TL;DR: In this paper, an improved genetic algorithm for economic load dispatch with valve-point loadings is presented, where new crossover and mutation operations are introduced to solve the problems of economic load dispatching under three cases.
Abstract: Economic load dispatch is one of the optimization problems in power systems. This paper presents an improved genetic algorithm for economic load dispatch with valve-point loadings. New crossover and mutation operations are introduced. The solutions of the economic load dispatch with valve-point loadings under three cases are solved by the improved genetic algorithm. Test results are given and compared with those from different published genetic algorithms. It is shown that the proposed improved genetic algorithm performs better than the published genetic algorithms.
38 citations
01 Jan 2012
36 citations
Additional excerpts
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...optimization, [132, 705, 1427, 1403, 489, 491]...
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References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
Book•
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
32,573 citations
Book•
03 Oct 2013
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
Abstract: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective. Research directions covered include: learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
2,824 citations
"Optimal economic power dispatch usi..." refers background in this paper
...A reproductive population search is a blind search algorithm that starts with some population of strings and repeatedly performs the following cycle of operations until some termination condition is satisfied[ 8 ]....
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TL;DR: In this article, a survey of papers and reports that address various aspects of economic dispatch is presented, including optimal power flow, economic dispatch in relation to AGC, dynamic dispatch, and economic dispatch with nonconventional generation sources.
Abstract: A survey is presented of papers and reports that address various aspects of economic dispatch. The time period considered is 1977-88. Four related areas of economic dispatch are identified and papers published in the general areas of economic dispatch are classified into these. These areas are: optimal power flow, economic dispatch in relation to AGC, dynamic dispatch, and economic dispatch with nonconventional generation sources. >
587 citations
01 Jan 1977
TL;DR: A review of the progress of optimal dispatch, also called economic dispatch, since its inception to the present in chronological sequence is given in this paper, where the classic single area as well as multi-area cases are summarized, and the important theoretical work in optimal load flows suggested to date reviewed.
Abstract: A review is given of the progress of optimal dispatch, also called economic dispatch, since its inception to the present in chronological sequence. The classic single area as well as multiarea cases are summarized, and the important theoretical work in optimal load flows suggested to date reviewed. Approaches to the optimal load flow taken by industry are also reported, as well as an itemization of problems that still remain to be solved.
349 citations
"Optimal economic power dispatch usi..." refers background in this paper
...At this stage, it is uncertain whether the ideal of a single method that possess the necessary speed, reliability and flexibility for on-line applications will ever be achieved [ 4 ,5]....
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