Implementation of Fast technique for Unit Commitment Based on Unit Clustering

Abstract

A new approach to the problem of large scale unit commitment is presented in this paper. The units are classified into various clusters based on their similar characteristics in order to reduce the computational time and also to satisfy the minimum up/down constraints easily. Unit commitment problem is an important optimizing task in daily operational planning of power systems which can be mathematically formulated as a large scale nonlinear mixed-integer minimization problem. A new methodology employing the concept of cluster algorithm called as additive and divisive hierarchical clustering has been employed along with particle swarm optimization in order to carry out the technique of unit commitment. Proposed methodology involves two individual algorithms. While the load is increasing, additive cluster algorithm has been employed while divisive cluster algorithm is used when the load is decreasing. The proposed technique is tested on a 10 unit system and the simulation results show the performance of the proposed technique.

Keywords: Unit commitment, additive clustering, divisive clustering, Lambda iteration method.