Cost Improvement of Clustering based Unit Commitment Employing Combined Genetic Algorithm-Simulated Annealing

Fuel cost savings can be obtained by proper commitment of available generating units. This paper describes a new approach to the unit commitment problem through classification of units into various clusters based on hybrid technique of genetic algorithm and simulated annealing. This classification is carried out in order to reduce the overall operating cost and 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 based on hybrid technique of genetic algorithm and simulated annealing 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.