Stochastic framework for scheduling preemptive upgrades of distribution transformers

TL;DR

This paper presents a stochastic Monte Carlo framework for prioritizing transformer upgrades in distribution networks by predicting failure probabilities under increasing electrification loads, aiming to prevent operational failures.

Contribution

It introduces a novel probabilistic scheduling method incorporating stochastic load growth and failure modeling for transformer upgrade planning.

Findings

Less than 20% of transformers face substantial failure risk over 20 years.

The framework effectively identifies transformers at high risk under various electrification scenarios.

The optimization routine helps schedule upgrades to minimize expected failures.

Abstract

Electrification of residential heating and transporta- tion has the potential to overload transformers in distribution feeders. Strategic scheduling of transformer upgrades to antici- pate increasing loads can avoid operational failures and reduce the risk of supply shortages. This work proposes a framework to prioritize transformer upgrades based on predicted loads at each meter, including heat pumps and electric vehicle chargers. The framework follows a Monte Carlo approach to forecasting, generating many possible loading instances and collecting a distribution of failure probabilities for each transformer. In each loading instance, heat pumps and EVs are added stochastically to each meter over time, based on an overall estimated growth rate and factors specific to each customer. We set heat pump load profiles by temperature and EV load profiles based on a stochastic driving model and charging pattern. The load profiles feed into network topology and transformer failure models to calculate failure probabilities.We formulate a cost optimization based on these failure probabilities to schedule transformer upgrades. We demonstrate this approach on a real-world distribution feeder in rural Vermont under low, medium, and high-electrification scenarios. We find generally less than 20% of transformers having substantial risk of failure over a 20-year simulation. Lastly, we develop an optimization routine to schedule upgrades and discuss the expected number of failures.