  Wind power is playing an increasingly important role in electricity markets.
However, it's inherent variability and uncertainty cause operational challenges
and costs as more operating reserves are needed to maintain system reliability.
Several operational strategies have been proposed to address these challenges,
including advanced probabilistic wind forecasting techniques, dynamic operating
reserves, and various unit commitment (UC) and economic dispatch (ED)
strategies under uncertainty. This paper presents a consistent framework to
evaluate different operational strategies in power system operations with
renewable energy. We use conditional Kernel Density Estimation (KDE) for
probabilistic wind power forecasting. Forecast scenarios are generated
considering spatio-temporal correlations, and further reduced to lower the
computational burden. Scenario-based stochastic programming with different
decomposition techniques and interval optimization are tested to examine
economic, reliability, and computational performance compared to deterministic
UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus
system with realistic system load and wind data.
