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Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes
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DOI：10.1109/TSTE.2022.3140650.png)
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摘要
En 中文
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.
Keyword:
Transformers
Integrated circuit interconnections
Topology
Data models
Integrated circuit modeling
Decision trees
Voltage
Distribution secondary
decision tree
logistic regression
solar interconnection
supervised learning
期刊
IF:
5.4
论文数:
6.8K
被引数:
1.5W
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