Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units. (September 2016)

• Record Type: Journal Article
• Authors: Hemmati, Reza; Saboori, Hedayat; Saboori, Saeid
• Is Part Of: Renewable energy. Volume 95(2016)
• Page Start: 74
• Page End: 84
• Publication Date: 2016-09
• Subjects: Energy storage system -- Short term operation scheduling -- Two-stage stochastic programming -- Wind power uncertainty; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.03.054
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Renewable resources, especially wind power, are widely integrated into the power systems nowadays. Managing uncertainty of the large scale wind power is often known as one of the most challenging issues in the power system operation scheduling. Additionally, energy storage systems (ESSs) have been widely investigated in the power systems owing to their valuable applications, especially renewable energy smoothing and time shift. In this paper, a stochastic unit commitment (UC) model is proposed to assess the impact of the wind uncertainty impact on ESSs and thermal units schedule in UC problem. Wind uncertainty is modeled based on the two measures. First, the wind penetration level is changed with respect to the basic level. Second, the wind forecasting error is modeled through a normal probability distribution function with different variances. The ESSs are modeled based on several technical characteristics and optimally scheduled considering different levels of the wind penetration and forecasting accuracies. The proposed formulation is a stochastic mixed integer linear programming (SMILP) and solved using GAMS software. Simulation results demonstrate that the wind uncertainty have a considerable impact on operation cost and ESSs schedule while proposed optimum storage scheduling through the stochastic programming will reduce the daily operational cost considerably. Highlights: MILP unit commitment is integrated with ESSs and wind power penetration. ESSs are modeled based on the various technical characteristics. Wind power uncertainty is handled with two-stage stochastic programming model. Impact of the wind uncertainty on the results is evaluated by VSS measure. Optimal ESSs schedule is provided considering uncertain nature of the problem.