Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization

TL;DR

This paper introduces a Bayesian Optimization-based method to efficiently identify the most critical DER adoption scenarios affecting distribution grid stability, significantly reducing computational effort compared to exhaustive searches.

Contribution

It presents a novel multi-objective Bayesian Optimization framework for scenario selection in distribution grids, improving speed and accuracy over traditional methods.

Findings

Order of magnitude faster than exhaustive search

Accurately identifies critical DER adoption scenarios

Effective on realistic feeders with 200-400 buses

Abstract

We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.