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.
|Title of host publication||2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)|
|Number of pages||6|
|Publication status||Published - 21 Dec 2021|
- distributed energy resources (DER)
- k-nearest neighbours (kNN)
- low voltage distribution networks
- supervised non-intrusive load monitoring algorithm