  Risk limiting dispatch (RLD) has been proposed as an approach that
effectively trades off economic costs with operational risks for power dispatch
under uncertainty. However, how to solve the RLD problem with provably
near-optimal performance still remains an open problem. This paper presents a
learning-based solution to this challenge. We first design a data-driven
formulation for the RLD problem, which aims to construct a decision rule that
directly maps day-ahead observable information to cost-effective dispatch
decisions for the future delivery interval. Unlike most existing works that
follow a predict-then-optimize paradigm, this end-to-end rule bypasses the
additional suboptimality introduced by separately handling prediction and
optimization. We then propose neural RLD, a novel solution method to the
data-driven formulation. This method leverages an L2-regularized neural network
to learn the decision rule, thereby transforming the data-driven formulation
into a neural network training task that can be efficiently completed by
stochastic gradient descent. A theoretical performance guarantee is further
established to bound the suboptimality of our method, which implies that its
suboptimality approaches to zero with high probability as more samples are
utilized. Simulation tests across various systems demonstrate our method's
superior performance in convergence, suboptimality, and computational
efficiency compared with benchmarks.
