  The Security-Constrained Economic Dispatch (SCED) is a fundamental
optimization model for Transmission System Operators (TSO) to clear real-time
energy markets while ensuring reliable operations of power grids. In a context
of growing operational uncertainty, due to increased penetration of renewable
generators and distributed energy resources, operators must continuously
monitor risk in real-time, i.e., they must quickly assess the system's behavior
under various changes in load and renewable production. Unfortunately,
systematically solving an optimization problem for each such scenario is not
practical given the tight constraints of real-time operations. To overcome this
limitation, this paper proposes to learn an optimization proxy for SCED, i.e.,
a Machine Learning (ML) model that can predict an optimal solution for SCED in
milliseconds. Motivated by a principled analysis of the market-clearing
optimizations of MISO, the paper proposes a novel ML pipeline that addresses
the main challenges of learning SCED solutions, i.e., the variability in load,
renewable output and production costs, as well as the combinatorial structure
of commitment decisions. A novel Classification-Then-Regression architecture is
also proposed, to further capture the behavior of SCED solutions. Numerical
experiments are reported on the French transmission system, and demonstrate the
approach's ability to produce, within a time frame that is compatible with
real-time operations, accurate optimization proxies that produce relative
errors below $0.6\%$.
