Improving Lagrangian Relaxation Unit Commitment with Cuckoo Search Algorithm

Abstract

In many utilities, it is essential to devise an optimum commitment solution of generating units for better operational efficiency, under empirical conditions. Among the methods reported in the technical literatures, Dynamic Programming (DP), Lagrangian Relaxation (LR), and Mixed-Integer Programming (MIP) are the most industry proven algorithms in the line of business. This paper improves the available solution offered in LR technique, which was mainly suffered from high fluctuation of duality gap between the primal and dual solutions. As a remedy, a Cuckoo Search Algorithm (CSA) is proposed to optimize the gap progress throughout the LR solution process. Simulation results reiterate that the developed LR-UC integrating CSA enhances the solution quality.