  Renewable energy sources (RES) has become common in modern power systems,
helping to address decarbonization and energy security goals. Despite being
attractive, RES such as solar and have low inertia and high uncertainty, thus
compromising power grid stability and increasing the risk of energy blackouts.
Stochastic (chance-constrained) optimization and other state-of-the-art
algorithms to optimize and control power generation under uncertainty either
explicitly assume the distribution of renewables, or use data-driven
approximations. The latter becomes time-consuming and inaccurate, esp. when
optimizing over multiple time steps. This paper considers a discrete-time
chance-constraint direct current optimal power flow control problem for
minimizing power generation costs subjected to power balance and security
constraints. We propose an importance-sampling-based data-driven approximation
for the optimal automated generation control, which allows to improve accuracy
and reduce data requirements compared to state-of-the-art methods. We support
the proposed approach theoretically and empirically. The results demonstrate
the approach superior performance in handling generation uncertainty, enhancing
the stability of renewable-integrated power systems, and facilitating the
transition to clean energy.
