  Intensified netload uncertainty and variability led to the concept of a new
market product, flexible ramping product (FRP). The main goal of FRP is to
enhance the generation dispatch flexibility inside real-time (RT) markets to
mitigate energy imbalances due to ramp capability shortage. Generally, the FRP
requirements are based on system-wide or proxy requirements, so the effect of
FRP awards on the transmission line constraints is not considered. This can
lead to FRP deployability issues in RT operation. This paper proposes a new FRP
design based on a datadriven policy incorporating ramping response factor sets
to address FRP deployability issue. First, a data-mining algorithm is performed
to predict the ramp-qualified generators to create the data-driven policy.
Then, the FRP awards are assigned to these units while considering effects of
post-deployment of FRPs on the transmission line limits. Finally, the proposed
data-driven policy is tested against proxy policy through an out-of-sample
validation phase that (i) mimics the RT operation of the CAISO, and (ii)
represents the expensive ad-hoc actions needed for procuring additional ramping
capability to follow realized netload changes. The results show the
effectiveness of the proposed data-driven policy from reliability and economic
points of view
