Implementation of Clustering Based Unit Commitment Employing Genetic Algorithm

A new approach to the problem of large scale unit commitment has been presented in this paper. The units are classified into various clusters based on genetic algorithm in order to reduce the overall operating cost 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 genetic algorithm 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.