Stochastic Unit Commitment with Significant Wind Penetration Using Novel Scenario Generation and Reduction

Wind energy has become a remarkable source of energy over the last decade. However, the uncertainty of wind prediction results in a great number of problems for the power systems operators in the day-ahead unit commitment. In order to maintain the power system reliable, the uncertainty offorecasting should be considered. In the field of data-mining, there are a number of techniques to model the uncertainties. This paper presents a novel solution for stochastic unit commitment with modeling the uncertainties of wind prediction. The uncertainty of demand has been considered as well. To do so, anew method for scenario generation and reduction is developed and solved with a modified shuffled frog leaping algorithm (SFLA) named adaptive SFLA (ASFLA). The case study shows the capability of the proposed method and a comparison indicated the benefits of wind energy employment in a system.

Keywords:

Unit commitment , stochastic programming , shuffled frog leaping algorithm , scenario reduction , wind power