A stochastic multi-interval scheduling framework to quantify operational flexibility in low carbon power systems

Operational flexibility is required in power systems to mitigate load-generation imbalances. Inflexibility either results infeasible scheduling or shift resources from their economic operating point. System operators must estimate requirement, assess its availability committed resources, and take corrective measures handle upcoming inflexibility events. Various metrics are integrated with dispatch quantify different facets of — ramp, power, energy. Consideration all three essential for adequate assessment, but often neglected literature requires an in-depth investigation. Further, existing hardly consider resources’ day-ahead decisions while evaluating real-time operations. This erratic assessment available flexibility. In this context, the paper proposes a comprehensive metric terms energy insufficiency by simultaneously considering system-wide requirement availability. A Resource Flexibility Index based on range ramping capability proposed accurate indication The stochastic multi-interval framework that considers operational constraints. Netload forecast associated uncertainty characterized using Long Short-Term Memory Markov Chain Monte Carlo techniques. Results highlight index proportional system’s netload variability handling average can be reduced up 97% utilization emerging ramp products. tools value system planners manage intermittency. • quantification ramping. Machine-learning methods generate scenarios. Multi-interval integrate metric. Integrating flexible products reduce inflexibility.