A parallel repair genetic algorithm to solve the unit commitment problem

This paper addresses the unit commitment problem of thermal units. This optimization problem is large-scale, combinatorial, mixed-integer, and nonlinear. Exact solution techniques to solve it are not currently available. This paper proposes a novel repair genetic algorithm conducted through heuristics to achieve a near optimal solution to this problem. This optimization technique is directly parallclizable. Three different parallel approaches have been developed. The modeling framework provided by genetic algorithms is less restrictive than the frameworks provided by other approaches such as dynamic programming or Lagrangian relaxation. A state-of-the-art Lagrangian relaxation algorithm is used to appraise the behavior of the proposed parallel genetic algorithm. The computing time requirement to solve problems of realistic size is moderate. The developed genetic algorithm has been successfully applied to realistic case studies.