  With more renewable energy sources (RES) integrated into the power system,
the intermittency of RES places a heavy burden on the system. The uncertainty
of RES is traditionally handled by controllable generators to balance the real
time wind power deviation. As the demand side management develops, the
flexibility of aggregate loads can be leveraged to mitigate the negative impact
of the wind power. In view of this, we study the problem of how to exploit the
multi-dimensional flexibility of elastic loads to balance the trade-off between
a low generation cost and a low system risk related to the wind curtailment and
the power deficiency. These risks are captured by the conditional
value-at-risk. Also, unlike most of the existing studies, the uncertainty set
of the wind power output in our model is not fixed. By contrast, it is
undetermined and co-optimized based on the available load flexibility. We
transform the original optimization problem into a convex one using surrogate
affine approximation such that it can be solved efficiently. In case studies,
we apply our model on a six-bus transmission network and demonstrate that how
flexible load aggregators can help to determine the optimal admissible region
for the wind power deviations.
