A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch

TL;DR

This paper introduces a data-driven sparse polynomial chaos expansion surrogate model to efficiently quantify uncertainty in stochastic economic dispatch, especially considering wind power variability, without needing input probability distributions.

Contribution

It presents a novel surrogate modeling approach that accurately estimates statistical measures for stochastic economic dispatch under wind power uncertainty without prior distribution assumptions.

Findings

The method achieves high accuracy compared to Monte Carlo simulations.

It efficiently computes statistical information for complex integrated systems.

The approach reduces computational effort significantly.

Abstract

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.