JES_V1_N3_RP2 Nimain Charan Nayak C. Christober Asir Rajan Journal on Embedded Systems 2278 - 7895 1 3 14 26 Evolutionary Programming, Tabu Search, Unit Commitment, Dynamic Programming, Lagrangian Relaxation IThis Paper proposes a new hybrid algorithm for solving the Unit Commitment problem in Hydrothermal power system using a hybrid Evolutionary Programming — Tabu Search method with cooling-banking constraints. The main objective of this paper is to find the generation scheduling by committing the generating units such that the total operating cost can be minimized by satisfying both the forecasted load demand and various operating constraints of the generating units. It is a Global optimization technique for solving Unit Commitment Problem, operates on a system, which is designed to encode each unit’s operating schedule with regard to its minimum up/down time. In this method, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here the parents are obtained from a pre-defined set of solutions i.e. each and every solution is adjusted to meet the requirements. Then, random recommitment is carried out with respect to the unit’s minimum down time. Tabu Search (TS) is a powerful optimization procedure that has been successfully applied to a number of combinatorial optimization problems. It avoids entrapment at local optimum by maintaining a short term memory of recently obtained solutions. The memory structure assists in forbidding certain moves that deteriorates the quality of the solution by assigning Tabu status to the forbidden solutions. The Tabu status of a solution can be overruled if certain conditions are satisfied expressed in the form of Aspiration Level. Aspiration Level (AL) adds flexibility in Tabu Search by directing the search towards attractive moves. The best population is selected by Evolutionary Strategy. Numerical results are shown comparing the cost solutions and computation time obtained by using the proposed hybrid method than conventional methods like Dynamic Programming, Lagrangian Relaxation. August - October 2012 Copyright © 2012 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/ArticleHTML.aspx?articleID=1991