A Framework for Generating Synthetic Distribution Feeders using OpenStreetMap

Abstract

This work proposes a framework to generate synthetic distribution feeders mapped to real geo-spatial topologies using available OpenStreetMap data. The synthetic power networks can facilitate power systems research and development by providing thousands of realistic use cases. The location of substations is taken from recent efforts to develop synthetic transmission test cases, with underlying real and reactive power in the distribution network assigned using population information gathered from United States 2010 Census block data. The methods illustrate how to create individual synthetic distribution feeders, and groups of feeders across entire ZIP Code, with minimal input data for any location in the United States. The framework also has the capability to output data in OpenDSS format to allow further simulation and analysis.

Ensembles of realistic power distribution networks

“…For distribution networks, Schweitzer et al ( 24 ) were one of the first to analyze real power distribution networks, learn statistical distributions of network attributes from an extensive dataset of actual distribution networks in the Netherlands, and create synthetic networks that preserve these attributes. The reference network model (RNM) framework ( 22 , 23 , 34 ) and some of its variants ( 35 , 36 ) have proposed heuristics to generate synthetic distribution networks that satisfy structural and power engineering constraints. These heuristics include clustering load groups to identify feeders ( 21 , 23 ), identifying substation locations from cluster centroids ( 22 , 23 ), and constructing networks using a minimum spanning tree algorithm (22, 34 , 35 ).…”

Assessing Cascading Effects from Multiple Hazards: An Example from Sweden

“…This is done through a flow-based method applying network analysis. Wherever location data for critical infrastructure and its network are lacking, we use a methodology called synthetic distribution networks (Ahmad et al, 2020;Saha et al, 2019). Figure 4 shows a synthetic network.…”

“…This is done through a mixed methods approach combining results from the network analysis with participatory mapping and expert-based validations. Data collection is structured according to the Impact Chain model (Fritzsche et al, 2014), that Sources: Ahmad et al (2020); Saha et al (2019) Water distribution…”

The right tools for the job: The case for spatial science tool‐building

“…For example, Hofer, Jäger, and Füllsack (2018a, 2018b) model the street network of Graz, Austria, to simulate carbon dioxide emissions and traffic congestion and avoidance behavior using mobility data. Saha, Schweitzer, Scaglione, and Johnson (2019) model the streets of Mesa, Arizona, to generate a synthetic feeder network for electrical distribution. Wang, Gao, Feng, Murray, and Zeng (2018) model Washington, DC’s street network to develop a geoprocessing framework for optimizing the meetup locations of multiple people under congested traffic conditions.…”