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 based on principle of natural selection and natural genetics. The Lagrangian Relaxation (LR) method provides a fast solution but it may suffer from numerical convergence and solution quality problems. The proposed Lagrangian Relaxation and Genetic Algorithms (LRGA) incorporates Genetic Algorithms into Lagrangian Relaxation method to update the Lagrangian multipliers and improve 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 obtained using Lagrangian Relaxation (LR) and Genetic Algorithms (GA's), show that the feature of easy implementation, better convergence, and highly near-optimal solution to the UC problem can be achieved by the LRGA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.