A chance-constrained wind range quantification approach to robust scuc by determining dynamic uncertainty intervals

With increasing penetration of wind energy, the variability and uncertainty of wind resources have become important factors for power systems operation. In particular, an effective method is required for identifying the stochastic range of wind power output, in order to better guide the operational security of power systems. This paper proposes a metric to determine accurate wind power output ranges so that the probability of actual wind power outputs being out of the range would be less than a small pre-defined value. A mixed-integer linear programming (MILP) based chance-constrained optimization model is proposed for efficiently determining optimal wind power output ranges, which are quantified via maximum and the minimum wind generation levels with respect to a certain time interval. The derived wind power range is then used to construct dynamic uncertainty intervals for the robust security-constrained unit commitment (SCUC) model. A comparison with the deterministic SCUC model and the traditional robust SCUC model with presumed static uncertainty interval demonstrates that the proposed approach can offer more accurate wind power variabilities (i.e., different variability degrees with respect to different wind power output levels at different time periods). The proposed approach is also shown to offer more effective and robust SCUC solutions, guaranteeing operational security and economics of power systems. Numerical case studies on a 6-bus system and the modified IEEE 118-bus system with realworld wind power data illustrate the effectiveness of the proposed approach.