Cost Reduction in Clustering Based Unit Commitment Employing Hybrid Genetic-Simulated Annealing Technique
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 technique that has been developed has been tested on system with generating units in range of 10–100 and the superior performance of the technique has been reported through simulation results.
KeywordsUnit commitment Additive clustering Divisive clustering Genetic algorithm Simulated annealing
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