A Risk-Managed Steady-State Analysis to Assess the Impact of Power Grid Uncertainties

TL;DR

This paper presents a probabilistic steady-state analysis method for power systems that efficiently assesses operational risks caused by uncertainties, offering significant speed improvements over traditional Monte Carlo simulations.

Contribution

The work introduces a novel risk-managed steady-state analysis inspired by worst-case circuit analysis, enabling faster probabilistic assessments of power system operability under uncertainty.

Findings

Achieved up to 24x faster runtime compared to Monte Carlo simulations.

Maintained comparable probabilistic accuracy in risk assessment.

Demonstrated effectiveness on a Texas7k low-wind day test system.

Abstract

Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets. The increased effects introduce new uncertainties into power systems that can impact system operability and reliability. Existing steady-state methods for assessing system-level operability and reliability are primarily deterministic, therefore, ill-suited to capture randomness and variability. This work introduces a probabilistic steady-state analysis inspired by statistical worst-case circuit analysis to evaluate the risk of operational violations due to stochastic resources. Compared to parallelized Monte Carlo analyses (MCS), we have seen up to 24x improvement in runtime speed using our approach without significant loss of probabilistic accuracy for a Texas7k low-wind day test system.