Proceedings ArticleDOI
Extremal optimization for unit commitment problem for power systems
Jin Ding,Yong-zai Lu,Jian Chu +2 more
- pp 1-8
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TLDR
Simulation results on power systems which are composed of up to 100-units over a scheduling horizon of 24-hours demonstrate competitive performance with EO method compared with other existing methods for UC problem.Abstract:
This paper introduces extremal optimization (EO) method to solve unit commitment problem for power systems. EO is a local-search heuristic algorithm and originally developed from the fundamentals of statistical physics. In the implementation of EO for unit commitment (UC) problem, a novel problem-specific mutation operator is introduced and rule-based heuristic constraint-repairing techniques are devised. Simulation results on power systems which are composed of up to 100-units over a scheduling horizon of 24-hours demonstrate competitive performance with EO method compared with other existing methods for UC problem.read more
Citations
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Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization
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An improved multi-objective population-based extremal optimization algorithm with polynomial mutation
TL;DR: The proposed IMOPEO-PLM adopts population-based iterated optimization, a more effective mutation operation called polynomial mutation, and a novel and more effective mechanism of generating new population to solve multi-objective optimization problems (MOPs).
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Design of multivariable PID controllers using real-coded population-based extremal optimization
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A Real-coded Extremal Optimization Method with Multi-non-uniform Mutation for the Design of Fractional Order PID Controllers
TL;DR: This paper presents a novel real-coded extremal optimization algorithm with multi-non-uniform mutation called RCEO-FOPID to design FOPID controllers that is superior to other reported evolutionary algorithms-based FOPIDs and PID controllers in terms of accuracy and robustness.
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Extremal optimisation approach to joint routing and scheduling for industrial wireless networks
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References
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A genetic algorithm solution to the unit commitment problem
TL;DR: This paper presents a genetic algorithm (GA) solution to the unit commitment problem using the varying quality function technique and adding problem specific operators, satisfactory solutions to theunit commitment problem were obtained.
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Unit commitment-a bibliographical survey
TL;DR: In this article, a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of modern unit commitment (UC) problem for past 35 years based on more than 150 published articles.
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An evolutionary programming solution to the unit commitment problem
TL;DR: The practical implementation of this procedure yielded satisfactory results when the EP-based algorithm was tested on a reported UC problem previously addressed by some existing techniques such as Lagrange relaxation (LR), dynamic programming (DP), and genetic algorithms (GAs).
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