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

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.