Journal ArticleDOI
A genetic algorithm solution to the unit commitment problem
TLDR
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.Abstract:
This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported.read more
Citations
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Journal ArticleDOI
A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem
Miguel Carrión,Jose M. Arroyo +1 more
TL;DR: In this paper, a new mixed-integer linear formulation for the unit commitment problem of thermal units is presented, which requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving.
Journal ArticleDOI
State of the Art in Research on Microgrids: A Review
TL;DR: This paper presents a review of issues concerning microgrid issues and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, micro grid operation and control, micro grids clusters, and protection and communications issues.
Journal ArticleDOI
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).
Journal ArticleDOI
Unit commitment by Lagrangian relaxation and genetic algorithms
TL;DR: Numerical results show that the feature of easy implementation, better convergence, and highly near-optimal solution to the UC problem can be achieved by the proposed Lagrangian relaxation and genetic algorithms (LRGA).
Journal ArticleDOI
Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source
TL;DR: In this paper, an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it's needed.
References
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Book
Genetic algorithms in search, optimization, and machine learning
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.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
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.
Book
Power Generation, Operation, and Control
Allen J. Wood,Bruce Wollenberg +1 more
TL;DR: In this paper, the authors present a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems, including characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security.