A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Luís A.C. Roque (Author) Other; Dalila B.M.M. Fontes (Author) FEP; Fernando A.C.C. Fontes (Author) FEP

Conference proceedings International

Title: EXPERIMENTAL ALGORITHMS

Pages: 327-339

10th International Symposium on Experimental Algorithms

Kolimpari, GREECE, MAY 05-07, 2011

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 19 Citations

Scientific classification

FOS: Social sciences > Economics and Business

Other information

Authenticus ID: P-002-WQX

DOI: 10.1007/978-3-642-20662-7_28

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13