UNIT COMMITMENT USING GENETIC ALGORITHM

Abstract

A need for optimality exists in the highly nonlinear and computationally difficult power system environment. Artificial intelligence (AI), unlike st-rict mathematical methods, has the apparent ability to adapt to nonlineari-ties and discontinuities commonly found in power systems. Previously, solution techniques used many assumptions to try to simplify and reduce computational effort in the unit commitment problem. Research has focussed on unit commitment techniques with various degrees of near optimality, efficiency and ability to handle difficult constraints. Unit commitment (UC) is the problem of determining the optimal set of generating units within a power system to be used during the next one to seven days. The general UC problem is to minimize operational costs (mainly fuel cost), transition costs (start-up/shut-down costs), and no-load cost (idle,banking or standby). The most computationally intensive part of a UC program is economic despatch (ED). ED is the process of allocating the required load demand among generating units such that the cost of operation is a minimum. Approximately 70% of the computer time in classical UC programs is used in the calculation of ED.