Parameter Estimation for Distribution Grid Reliability Assessment

Abstract

Strengthening distribution grids reliability and resilience against technical and natural hazards is a costly endeavor including equipment upgrades and distributed energy resources. Therefore, using accurate data when assessing grid reliability is key to identify effective solutions. As literature parameters can be inaccurate for specific locations, tuning and validating reliability models against real-world data is key for accurate assessments. In this paper, distribution grid reliability is modelled by considering three failure mechanisms in a Monte Carlo simulation: bus and line failures within the distribution grid, blackouts of the surrounding grid, and dependent failures due to extreme events. Ten parameters governing the frequency and duration distributions of the three failure mechanisms are tuned using metaheuristic optimization. A subsequent global sensitivity analysis quantifies the importance of the estimated parameters.