Identification of Distributed Energy Resources in Low Voltage Distribution Networks

Andres F. Moreno Jaramillo, Javier Lopez-Lorente, David Laverty, Jesus Martinez-del-Rincon, Aoife M. Foley

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The increase of distributed energy resources, namely rooftop photovoltaic (PV) systems and electric vehicles (EV), has brought new technical challenges related to the operation and planning in low-voltage distribution networks. The modification of electrical network dynamics has exposed a lack of observability in this side of the electric system requiring innovative techniques to increase flexibility, reliability, and security of supply in power networks. A supervised nonintrusive load monitoring method commonly used for the identification of traditional electrical appliances connected behind-the-meter in household distribution boards is proposed in this research study. The work is based on a low-complexity, effective machine learning algorithm to identify the presence of PV generation or EV power consumption in aggregated measurements of low-voltage networks. The model is developed in the IEEE European low voltage test feeder. It evaluates several scenarios using data with 1-minute sampling simulated with the tool OpenDSS and with k-nearest neighbour as classification algorithm. The results of the proposed method exhibit high performance for both PV and EV identification and illustrate the potential for these non-intrusive monitoring solutions in supporting the integration of distributed resources in low-voltage power networks.
Original languageEnglish
Title of host publication2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)978-1-6654-4875-8
ISBN (Print)978-1-6654-4876-5
Publication statusPublished - 21 Dec 2021


  • distributed energy resources (DER)
  • k-nearest neighbours (kNN)
  • low voltage distribution networks
  • supervised non-intrusive load monitoring algorithm
  • observability


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