BRKGA adapted to multiobjective unit commitment: Solving Pareto Frontier for UC Multiobjective Problem using BRKGA SPEA2 NPGA and NSGA II Techniques

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

roque, lac (Author) Other; fontes, dbmm (Author) FEP; fontes, facc (Author) FEUP

Conference proceedings International

Title: ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems

Pages: 64-72

1st International Conference on Operations Research and Enterprise Systems, ICORES 2012

Vilamoura, Algarve, 4 February 2012 through 6 February 2012

Indexing

ISI Web of Knowledge

Scopus - 3 Citations

Other information

Authenticus ID: P-008-484

Abstract (EN): The environmental concerns are having a significant impact on the operation of power systems. The traditional Unit Commitment problem, which to minimizes the fuel cost is inadequate when environmental emissions are also considered in the operation of power plants. This paper presents a Biased Random Key Genetic Algorithm (BRKGA) approach combined with non-dominated sorting procedure to find solutions for the unit commitment multiobjective optimization problem. In the first stage, the BRKGA solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0,1]. In the subsequent stage, a non-dominated sorting procedure similar to NSGA II is employed to approximate the set of Pareto solution through an evolutionary optimization process. 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. Test results with the existent benchmark systems of 10 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) multiobjective optimization methods reveal the effectiveness of the proposed method.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9