  We study the scenario approach for solving chance-constrained optimization in
time-coupled dynamic environments. Scenario generation methods approximate the
true feasible region from scenarios generated independently and identically
from the actual distribution. In this paper, we consider this problem in a
dynamic environment, where the scenarios are assumed to be drawn sequentially
from an unknown and time-varying distribution. Such dynamic environments are
driven by changing environmental conditions that could be found in many
real-world applications such as energy systems. We couple the time-varying
distributions using the Wasserstein metric between the sequence of
scenario-generating distributions and the actual chance-constrained
distribution. Our main results are bounds on the number of samples essential
for ensuring the ex-post risk in chance-constrained optimization problems when
the underlying feasible set is convex or non-convex. Finally, our results are
illustrated on multiple numerical experiments for both types of feasible sets.
