Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation

TL;DR

This paper introduces a Gaussian process emulator to efficiently quantify uncertainty in stochastic economic dispatch with renewable energy, significantly reducing computational costs compared to Monte Carlo methods.

Contribution

A novel Gaussian-process-emulator-based approach is developed for uncertainty quantification in stochastic economic dispatch considering wind power penetration.

Findings

The proposed method achieves high accuracy with negligible computational cost.

Simulation on IEEE 118-bus system shows superior performance over Monte Carlo.

The approach effectively handles large-scale power system uncertainties.

Abstract

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.