Evaluating stochastic methods in power system operations with wind power

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