Enhancing Support Vector Decoders by Integrating an Uncertainty Model

Predictive scheduling is a frequently executed task within the control process of energy grids. Relying on different predictions, planning results are naturally subject to uncertainties. Robust proactive planning of day-ahead real power provision must incorporate uncertainty in feasibility when trading off different schedules against each other during the predictive planning phase. Deviations from the expected initial operational state of an energy unit may easily foil a planned schedule commitment and provoke the need for costly ancillary services. The integration of confidence information into the optimization model allows for a consideration of uncertainty at planning time; resulting in more robust plans. Hence, control power and costs arising from deviations from agreed energy product delivery can be minimized. Integrating uncertainty information can be easily done when using a surrogate model. We extend an existing surrogate model that has been successfully used in energy management for checking feasibility during constraint-based optimization. The surrogate is extended to incorporate confidence scores based on expected feasibility under changed operational conditions. We compare the new surrogate model with the old one and demonstrate the superiority of the new model by results from several simulation studies.