Geo-referenced Synthetic Medium-voltage Distribution Networks: A Data-Driven Approach

The integration of new technologies such as fuel cells, battery electric vehicle charging stations, and heavy-duty battery vehicles, is transforming the way electricity is generated and consumed. This integration presents many challenges for power systems including the need to balance the supply and demand, power quality, and stability of the system. To understand the effects of new technologies on power systems, novel methodologies are being developed and tested. How-ever, analyzing these behaviors on real networks is challenging due to their limited data. Therefore, this paper contributes a new data-driven methodology for the automated generation of geo-referenced synthetic medium-voltage networks based on open data. The methodology first generates a graphical network using geo-referenced synthetic low-voltage networks, non-residential buildings classified from OpenStreetMap (OSM) building data, and the roadways network from OSM data. The edges and nodes of the graphical network are then modeled with electrical parameters to successfully depict the medium-voltage network. Additionally, the methodology considers the existing power generation units nearby each net-work and expands the network. Eventually, the electrical network is prepared for network and economic analysis by performing power flow simulations. The model’s performance is exhibited through the integration of battery electric vehicle charging stations within the synthetic medium-voltage network that was created. The simulation outcomes yield information on the voltage and power levels at nodes, transformers, and lines. This data is subsequently utilized to assess the effects of newly installed charging stations on the network.