Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908840864251904 |
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| author | Mulkin, Olivier Heleno, Miguel Ludkovski, Mike |
| author_facet | Mulkin, Olivier Heleno, Miguel Ludkovski, Mike |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization Mulkin, Olivier Heleno, Miguel Ludkovski, Mike Machine Learning Applications 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. |
| title | Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2501.14118 |