Non-Linear Clustering of Distribution Feeders

Octavio Ramos-Leaños (), Jneid Jneid and Bruno Fazio
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Octavio Ramos-Leaños: Hydro-Quebec Research Center, Varennes, QC J3X 1S1, Canada
Jneid Jneid: Hydro-Quebec Distribution Network Strategy Unit, Montreal, QC H2Z 1A4, Canada
Bruno Fazio: Hydro-Quebec Distribution Network Strategy Unit, Montreal, QC H2Z 1A4, Canada

Energies, 2022, vol. 15, issue 21, 1-20

Abstract: Distribution network planners are facing a strong shift in the way they plan and analyze the network. With their intermittent nature, the introduction of distributed energy resources (DER) calls for yearly or at least seasonal analysis, which is in contrast to the current practice of analyzing only the highest demand point of the year. It requires not only a large number of simulations but long-term simulations as well. These simulations require significant computational and human resources that not all utilities have available. This article proposes a nonlinear clustering methodology to find a handful of representative medium voltage (MV) distribution feeders for DER penetration studies. It is shown that the proposed methodology is capable of uncovering nonlinear relations between features, resulting in more consistent clusters. Obtained results are compared to the most common linear clustering algorithms.

Keywords: clustering; distribution feeders; machine learning; DER; time series (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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