  The increasing penetration of renewable energy resources in power systems,
represented as random processes, converts the traditional deterministic
economic dispatch problem into a stochastic one. To solve this stochastic
economic dispatch, the conventional Monte Carlo method is prohibitively time
consuming for medium- and large-scale power systems. To overcome this problem,
we propose in this paper a novel Gaussian-process-emulator-based approach to
quantify the uncertainty in the stochastic economic dispatch considering wind
power penetration. Based on the dimension-reduction results obtained by the
Karhunen-Lo\`eve expansion, a Gaussian-process emulator is constructed. This
surrogate allows us to evaluate the economic dispatch solver at sampled values
with a negligible computational cost while maintaining a desirable accuracy.
Simulation results conducted on the IEEE 118-bus system reveal that the
proposed method has an excellent performance as compared to the traditional
Monte Carlo method.
