Unit commitment by Lagrangian relaxation and genetic algorithms

This paper presents an application of a combined the Genetic Algorithms (GA 's) and Lagrangian Relaxation (LR) method for the unit commitment problem. Genetic Algorithms (GA's) are a general purpose optimization technique base d on principle of natural selection and natural genetics. The Lagrangian Re laxation (LR) method provides a fast solution but it may suffer from numeri cal convergence and solution quality problems. The proposed Lagrangian Rela xation and Genetic Algorithms (LRGA) incorporates Genetic Algorithms into L agrangian Relaxation method to update the Lagrangian multipliers and improv e the performance of Lagrangian Relaxation method in solving combinatorial optimization problems such as unit commitment problem. Numerical results on two cases including a system of 100 units and comparisons with results obt ained using Lagrangian Relaxation (LR) and Genetic Algorithms (GA's), show that the feature of easy implementation, better convergence, and highly nea r-optimal solution to the UC problem can be achieved by the LRGA.