  In this paper, we develop an exact reformulation and a deterministic
approximation for distributionally robust joint chance-constrained programmings
(DRCCPs) with a general class of convex uncertain constraints under data-driven
Wasserstein ambiguity sets. It is known that robust chance constraints can be
conservatively approximated by worst-case conditional value-at-risk (CVaR)
constraints. It is shown that the proposed worst-case CVaR approximation model
can be reformulated as an optimization problem involving biconvex constraints
for joint DRCCP. We then derive a convex relaxation of this approximation model
by constructing new decision variables which allows us to eliminate biconvex
terms. Specifically, when the constraint function is affine in both the
decision variable and the uncertainty, then the resulting approximation model
is equivalent to a tractable mixed-integer convex reformulation for joint
binary DRCCP. Numerical results illustrate the computational effectiveness and
superiority of the proposed formulations.
