  The rapid integration of variable energy sources (VRES) into power grids
increases variability and uncertainty of the net demand, making the power
system operation challenging. Operating reserve is used by system operators to
manage and hedge against such variability and uncertainty. Traditionally,
reserve requirements are determined by rules-of-thumb (static reserve
requirements, e.g., NERC Reliability Standards), and more recently, dynamic
reserve requirements from tools and methods which are in the adoption process
(e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools
significantly improve the static rule-of-thumb approaches, they rely
exclusively on deterministic data (i.e., best guess only). Consequently, these
methods disregard the probabilistic uncertainty thresholds associated with
specific days and their weather conditions (i.e., best guess plus probabilistic
uncertainty).
  This work presents practical approaches to determine the operating reserve
requirements leveraging the wealth information from probabilistic forecasts.
Proposed approaches are validated and tested using actual data from the CAISO
system. Results show the benefits in terms of risk reduction of considering the
probabilistic forecast information into the dimensioning process of operating
reserve requirements.
