  Independent system operators (ISOs) in the US clear their real-time
electricity market every five minutes, optimizing energy and reserve dispatches
to minimize operating costs while meeting technical and regulatory constraints.
Currently, many real-time markets are cleared by solving a single-period,
deterministic, security-constrained economic dispatch (SCED). Nevertheless,
given the growing penetration of renewable generation and the forthcoming
retirement of conventional generation units, it becomes increasingly
challenging to manage operational uncertainty at the real-time stage via the
current SCED formulations. This limitation is best illustrated by the recent
introduction into the real-time market of multiple short-term ramping products,
which aim at bridging the gap between deterministic and stochastic
formulations. In contrast, this paper explores the scalability and potential
benefits of explicitly considering uncertainty in real-time market formulations
by combining multi-period look-ahead dispatch (LAD) and stochastic look-ahead
(SLAD) formulations. An accelerated Benders' decomposition is presented to
solve the resulting problems efficiently. The paper conducts extensive
numerical experiments on a real, industry-size transmission grid that
benchmarks the proposed approaches and evaluates their benefits. The results
demonstrate that stochastic optimization is now tractable enough to be used in
real-time markets. Furthermore, the combination of multi-period and stochastic
look-ahead offers significant benefits in both reliability and cost, as SLAD
can better position online generators to accommodate future ramping needs,
thereby reducing future operational costs and violations. Overall, SLAD reduces
import costs and the risk of transmission violation and saves an average of
more than 2% of costs compared to SCED.
