Handling forecast uncertainty and variability in solar generation to mitigate schedule deviation penalties

With increasing installed renewable capacity the uncertainty and variability poses many challenges to planners and operators of the power systems in terms of generators deviating from commitments due to imperfect forecasts as well as fluctuations due to weather phenomena. These schedule deviations cause power imbalances and increase the balancing/regulating resources requirements. A possible alternative approach is to compensate for fluctuations locally: this study is geared towards exploring this approach. A multi-timescale scheduling framework is presented herein which ensures high degree of compliance between the day-ahead grid commitments and the actual grid injections. Battery is deployed as a local hedging device to provide compensating measures against uncertainty and variability. A two-stage recourse-based stochastic optimization framework for day-ahead scheduling is presented to tackle deviations due to forecast uncertainty. And model predictive control strategy is employed to tackle fluctuations due to short-term variability. Since the two approaches work on different time-scales, an effective temporal link between these two approaches is established and the energy management system for batteries is presented to reflect this link. A case study using on-field recorded solar generation data set for a PV producer is presented to showcase the effectiveness of the proposed approach.