Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems
As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE National Renewable Energy Laboratory (NREL)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 2229080
- Report Number(s):
- NREL/CP-6A40-88224; MainId:88999; UUID:d555ba00-8c3b-46b1-92af-d9e517095750; MainAdminID:71233
- Resource Relation:
- Conference: Presented at the the 2023 IEEE Power & Energy Society General Meeting (PESGM), 16-20 July 2023, Orlando, Florida
- Country of Publication:
- United States
- Language:
- English
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