A framework for intelligent feeder overloading

Component ampacity in conventional feeder design and engineering is based on worst-case scenarios that, by virtue of necessity, disregards the inherent thermal inertia and realtime or anticipated ambient temperatures. From operations point of view, reliance on these static figures leads to underutilization and limited options to re-route power flow during normal and emergency conditions. The framework proposed in this article formalizes the engineering practice of overloading that helps optimize feeder utilization based on dynamic ratings as — preferably — an integral function of a substation-based thermal overload monitoring and prediction system. Such a system leverages real-time data from intelligent electronic devices (IEDs) and sensors in the age of anticipated data deluge. The ultimate goal is to empower operators with tools and intelligence that allows them to take advantage of capacity margins in a calculated manner in the midst of ever-increasing dynamics and variability in generation and consumption.