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Title: Ensembles of realistic power distribution networks
The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.  more » « less
Award ID(s):
1927791 1916805 2039716
NSF-PAR ID:
10377215
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
119
Issue:
42
ISSN:
0027-8424
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    !!! UPDATE 08-2023 !!!: We provide a new, improved version of HISDAC-ES. Specifically, we fixed two bugs in the production code that caused an incorrect rasterization of the multitemporal BUFA layers and of the PHYS layers (BUFA, BIA, DWEL, BUNITS sum and mean). Moreover, we added decadal raster datasets measuring residential building footprint and building indoor area (1900-2020), and provide a country-wide, harmonized building footprint centroid dataset in GeoPackage vector data format.

    File descriptions:

    Datasets are available in three spatial reference systems:

    1. HISDAC-ES_All_LAEA.zip: Raster data in Lambert Azimuthal Equal Area (LAEA) covering all Spanish territory.
    2. HISDAC-ES_IbericPeninsula_UTM30.zip: Raster data in UTM Zone 30N covering all the Iberic Peninsula + Céuta and Melilla.
    3. HISDAC-ES_CanaryIslands_REGCAN.zip: Raster data in REGCAN-95, covering the Canary Islands only.
    4. HISDAC-ES_MunicipAggregates.zip: Municipality-level aggregates and completeness statistics (CSV, GeoPackage), in LAEA projection.
    5. ES_building_centroids_merged_spatjoin.gpkg: 7,000,000+ building footprint centroids in GeoPackage format, harmonized from the different cadastral systems, representing the input data for HISDAC-ES. These data can be used for sanity checks or for the creation of further, user-defined gridded surfaces.

    Source data:

    HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:

    • Araba province: https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilities
    • Bizkaia province: https://web.bizkaia.eus/es/inspirebizkaia
    • Gipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/
    • Navarra region: https://inspire.navarra.es/services/BU/wfs
    • Other regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xml
    • Data source of municipality polygons: Centro Nacional de Información Geográfica (https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)

    Technical notes:

    Gridded data

    File nomenclature:

    ./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tif

    Regions:

    • all: complete territory of Spain
    • can: Canarian Islands only
    • ibe: Iberic peninsula + Céuta + Melilla

    Projections:

    • laea: Lambert azimuthal equal area (EPSG:3035)
    • regcan: REGCAN95 / UTM zone 28N (EPSG:4083)
    • utm: ETRS89 / UTM zone 30N (EPSG:25830)

    Themes:

    • evolution / evol: multi-temporal physical measurements
    • landuse: multi-temporal building counts per land use (i.e., building function) class
    • physical / phys: physical building characteristics in 2020
    • temporal / temp: temporal characteristics (construction year statistics)

    Variables: evolution

    • budens: building density (count per grid cell area)
    • bufa: building footprint area
    • deva: developed area (any grid cell containing at least one building)
    • resbufa: residential building footprint area
    • resbia: residential building indoor area

    Variables: physical

    • bia: building indoor area
    • bufa: building footprint area
    • bunits: number of building units
    • dwel: number of dwellings

    Variables: temporal

    • mincoy: minimum construction year per grid cell
    • maxcoy: minimum construction year per grid cell
    • meancoy: mean construction year per grid cell
    • medcoy: median construction year per grid cell
    • modecoy: mode (most frequent) construction year per grid cell
    • varcoy: variety of construction years per grid cell

    Variable: landuse

    Counts of buildings per grid cell and land use type.

    Municipality-level data

    • hisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.
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    • hisdac_es_municipality_stats_completeness_v1.csv: This CSV file contains the missingness rates (in %) of the building attribute per municipality, ranging from 0.0 (attribute exists for all buildings) to 100.0 (attribute exists for none of the buildings) in a given municipality.

    Column names for the completeness statistics tables:

    • NATCODE: National municipality identifier*
    • num_total: number of buildings per munic
    • perc_bymiss: Percentage of buildings with missing built year (construction year)
    • perc_lumiss: Percentage of buildings with missing landuse attribute
    • perc_luother: Percentage of buildings with landuse type "other"
    • perc_num_floors_miss: Percentage of buildings without valid number of floors attribute
    • perc_num_dwel_miss: Percentage of buildings without valid number of dwellings attribute
    • perc_num_bunits_miss: Percentage of buildings without valid number of building units attribute
    • perc_offi_area_miss: Percentage of buildings without valid official area (building indoor area, BIA) attribute
    • perc_num_dwel_and_num_bunits_miss: Percentage of buildings missing both number of dwellings and number of building units attribute

    The same statistics are available as geopackage file including municipality polygons in Lambert azimuthal equal area (EPSG:3035).

    *From the NATCODE, other regional identifiers can be derived as follows:

    • NATCODE: 34 01 04 04001
    • Country: 34
    • Comunidad autónoma (CA_CODE): 01
    • Province (PROV_CODE): 04
    • LAU code: 04001 (province + municipality code)
     
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