Reliability-Aware Control of Distributed Energy Resources using Multi-Source Data Models

TL;DR

This paper develops a multi-source data model for reliability-aware control of distributed energy resources, improving system failure risk estimation and operational decision-making to enhance distribution system reliability.

Contribution

It introduces ensemble tree-based models to accurately estimate failure rates from weather and system data, integrating these into a dynamic control optimization framework.

Findings

Improved failure rate estimation accuracy

Enhanced system reliability and cost savings

Effective feature selection reduces model complexity

Abstract

Distributed energy resources offer a control-based option to improve distribution system reliability by ensuring system states that positively impact component failure rates. This option is an attractive complement to otherwise costly and lengthy physical infrastructure upgrades. However, required models that adequately map operational decisions and environmental conditions to system failure risk are lacking because of data unavailability and the fact that distribution system failures remain rare events. This paper addresses this gap and proposes a multi-source data model that consistently maps comprehensive weather and system state information to component failure rates. To manage collinearity in the available features, we propose two ensemble tree-based models that systematically identify the most influential features and reduce the dataset's dimensionality based on each feature's impact on failure rate estimates. These estimates are embedded within a sequential, non-convex optimization procedure, that dynamically updates operational control decisions. We perform a numerical experiment to demonstrate the cost and reliability benefits that can be achieved through this reliability-aware control approach and to analyze the properties of each proposed estimation model.