  We consider the problem of look-ahead economic dispatch (LAED) with uncertain
renewable energy generation. The goal of this problem is to minimize the cost
of conventional energy generation subject to uncertain operational constraints.
The risk of violating these constraints must be below a given threshold for a
family of probability distributions with characteristics similar to observed
past data or predictions. We present two data-driven approaches based on two
novel mathematical reformulations of this distributionally robust decision
problem. The first one is a tractable convex program in which the uncertain
constraints are defined via the distributionally robust
conditional-value-at-risk. The second one is a scalable robust optimization
program that yields an approximate distributionally robust chance-constrained
LAED. Numerical experiments on the IEEE 39-bus system with real solar
production data and forecasts illustrate the effectiveness of these approaches.
We discuss how system operators should tune these techniques in order to seek
the desired robustness-performance trade-off and we compare their computational
scalability.
