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

arXiv: https://arxiv.org/abs/2501.14118 · HTML: https://mlsanity.com/paper/2501.14118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [Combinatorial Bayesian Optimization using the Graph Cartesian Product](https://mlsanity.com/paper/1902.00448/) (2019)
- [Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces](https://mlsanity.com/paper/2102.07188/) (2021)
- [On the choice of the low-dimensional domain for global optimization via random embeddings](https://mlsanity.com/paper/1704.05318/) (2017)
- [Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings](https://mlsanity.com/paper/2303.01774/) (2023)
- [A warped kernel improving robustness in Bayesian optimization via random embeddings](https://mlsanity.com/paper/1411.3685/) (2014)
- [Gaussian Process Learning-based Probabilistic Optimal Power Flow](https://mlsanity.com/paper/2004.07757/) (2020)
- [A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction](https://mlsanity.com/paper/1812.04480/) (2018)
- [Netload Range Cost Curves for a Transmission-Aware Distribution System Planning under DER Growth Uncertainty](https://mlsanity.com/paper/2402.07428/) (2024)
- [Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes](https://mlsanity.com/paper/2409.16308/) (2024)

## Abstract (arXiv)

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.