A biased random key genetic algorithm approach for unit commitment problem

Authors:
Luís A. C. Roque;Dalila B. M. M. Fontes;Fernando A. C. C. Fontes
Affiliations:
ISEP-DEMA, GECAD, Instituto Superior de Engenharia do Porto, Portugal;FEP, LIAAD-INESC Porto L.A., Universidade do Porto, Portugal;FEUP, ISR-Porto, Universidade do Porto, Portugal
Venue:
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
Year:
2011

Citing 2
Cited 1

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Genetic Algorithms in Search, Optimization and Machine Learning
A Seeded Memetic Algorithm for Large Unit Commitment Problems

Journal of Heuristics

Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm

Applied Soft Computing

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Abstract

A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0,1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up to 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, from the comparisons made it can be concluded that the results produced improve upon some of the best known solutions.