Abstract
This paper proposes a multi objective model for Advanced Unit Commitment (AUC) with wind power and Pumped Storage (PS) units using Cuckoo Search (CS) algorithm. The novelty of the proposed method is improved levy flight searching ability, random reduction and ability to adapt complex optimization problems. Here, the CS algorithm to accommodate wind output uncertainty, with the multi-objective of providing an optimal AUC schedule for the thermal generators in the day-ahead market that minimizes the total cost under the different wind power output scenario. The proposed method is more reliable for AUC because it considering the wind power uncertainty using the Artificial Neural Network (ANN) and PS units, which are significantly reduces the total cost. Then the proposed method is implemented in the MATLAB/simulink platform and tested under IEEE standard bench mark system. The proposed method performance has been verified through the comparison analysis with the existing techniques. The comparison results were proved the superiority of the proposed method.
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