  This letter proposes a data-driven sparse polynomial chaos expansion-based
surrogate model for the stochastic economic dispatch problem considering
uncertainty from wind power. The proposed method can provide accurate
estimations for the statistical information (e.g., mean, variance, probability
density function, and cumulative distribution function) for the stochastic
economic dispatch solution efficiently without requiring the probability
distributions of random inputs. Simulation studies on an integrated electricity
and gas system (IEEE 118-bus system integrated with a 20-node gas system are
presented, demonstrating the efficiency and accuracy of the proposed method
compared to the Monte Carlo simulations.
