Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy Vol. 13; no. 2; pp. 948 - 956
Main Authors: Wang, Wenbo, Keen, Jeremy, Giraldez, Julieta, Baranko, Kyle, Lunghino, Brent, Morris, Dan, Bell, Frances, Shumavon, Aram, Levine, Kaiya, Ward, Teddy, Dave, Adarsh, Bank, Jason
Format: Journal Article
Language:English
Published: Piscataway IEEE 01-04-2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Approximation
Conductors
Data models
decision tree
Decision trees
Distribution secondary
Electric potential
Energy resources
Energy sources
Energy storage
Integrated circuit interconnections
Integrated circuit modeling
Learning
logistic regression
Modelling
Photovoltaic cells
Photovoltaics
POWER TRANSMISSION AND DISTRIBUTION
Regression models
SOLAR ENERGY
solar interconnection
Storage systems
Supervised learning
Teaching methods
Topology
Transformers
Voltage
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Summary:The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. The proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.
Bibliography:USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
AC36-08GO28308
NREL/JA-5C00-80234
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2022.3140650