Efficient Stochastic Scheduling for Simulation of Wind-Integrated Power Systems

Time-domain scheduling simulation is the most effective tool for predicting the operational costs in wind-integrated power systems, because it can represent the inter-temporal constraints that limit the balancing actions of the thermal plant, storage, and demand-side measures. High wind penetrations demand just-in-time commitment decisions that reflect the uncertainties in the wind infeed, so that it is desirable to generate the scheduling decisions using stochastic unit commitment (SUC) with rolling planning. However, the computational burden can make such methods impractical in long simulations. We present an efficient formulation of the SUC problem that is designed for use in scheduling simulations of single-bus power systems. Unlike traditional SUC techniques, the proposed formulation uses a quantile-based scenario tree structure that avoids the need for exogenous operating reserves. We compare the performance of various tree topologies in year-long simulations of a large system. Simple quantile-based trees give statistically significant cost improvements over fixed-quantile deterministic methods and compare favorably with trees based on Monte Carlo-generated scenarios.