  The transition of the electrical power grid from fossil fuels to renewable
sources of energy raises fundamental challenges to the market-clearing
algorithms that drive its operations. Indeed, the increased stochasticity in
load and the volatility of renewable energy sources have led to significant
increases in prediction errors, affecting the reliability and efficiency of
existing deterministic optimization models. The RAMC project was initiated to
investigate how to move from this deterministic setting into a risk-aware
framework where uncertainty is quantified explicitly and incorporated in the
market-clearing optimizations. Risk-aware market-clearing raises challenges on
its own, primarily from a computational standpoint. This paper reviews how RAMC
approaches risk-aware market clearing and presents some of its innovations in
uncertainty quantification, optimization, and machine learning. Experimental
results on real networks are presented.
