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