Context:

Rooftop PV systems in urban areas are very interesting because they do not emit air pollutants nor GHGs during their exploitation, they produce electricity where this electricity is consumed, and they add value to unused urban roofs and parking shades and may reduce urban heat island effect. But, due to complex shading effects in urban context (vegetation, surrounding buildings, superstructures of roofs, etc.) and local atmospheric and meteorological effects, their massive penetration in urban areas will induce a significant variability in space and in time in the energy injected in the electric grid. As far as the electric demand side is concerned, a detailed modelling of energy requirements from residential, commercial, and industrial buildings with varying demand profiles for electricity is also required. Therefore, there is a need, in urban area, for Geographical Information System (GIS)-like tool for grid operators, urban planning decision makers, industries, aggregators for solar energy trading, citizen (PV self-consumption) and researchers. This GIS-like tool is meant to provide an urban energy system modelling of distribution grids to plan, monitor and nowcast (i.e. and short term forecast) the spatiotemporal variability of the electric consumption on one hand and of the production of fleet of PV rooftop systems on the other hand.

Objectives:

GISlike tool for urban areas to simulate variability in space and in time the electric energy load and the yield productions of rooftop PV systems to assess for planning phase:

the temporal and spatial variability of the injected electric energy in the grid for grid operators and urban planning decision makers;
the specific behavior of residential, commercial, and industrial buildings with their different electric load patterns;
the optimal integration and placement of electricity stationery storage applications and the charging and battery exchange stations;
the adequacy of a specific rooftop PV system with respect to the energy consumption profile of citizens trying to optimize their PV self-consumption with the help of PV system developers.

GIS-like tools for existing rooftop PV systems in on specific urban area, to monitor and nowcast:

the temporal and spatial variability of the injected electric energy for grid operators and aggregators for solar energy trading;
the temporal variability of the energy yield of a specific rooftop PV system for the citizen and the PV developers being contracted by the PV system owner.

Mean of access:

A Jupyter frontend (Registration required) using a OGC WPS as the backend process.

https://notebook.oie-lab.net/hub/login

Nature of the outcome:

Two types of outputs is provided:

Time series in NetCDF format of historical and short term (intra-hourly) forecast PV yields simulation at urban scale
Expert analysis embedded in the Jupyter Notebook environment of:
1. Energy self consumption level
2. PV variability injected in the grid
3. Nowcast / Forecast

Partners:

ARMINES, DLR-DFD, DLR-VE, TSV

Key Datasets:

(S) satellite-based decametric DEM, (S) airborne- or satellite-based sub-metric DSM, (S) building cadastral 2D plans, (S) Copernicus Sentinel-1 and -2 for timescan analysis of Normalized Difference Vegetation Index (NDVI), Normalized Difference Built Index (NDBI) and Normalized Difference Water Index (NDWI) statistics, (S) Satellite-based global urban footprint imperviousness data (A) atmospheric optical state (aerosol optical depth, aerosol mixtures (black carbon, sea salt, mineral dust, sulphate, etc.), total column water vapour and ozone contents) from Copernicus Atmospheric Monitoring Service (CAMS), (A) surface solar radiation from CAMS, using Meteorological geostationary satellite (Meteosat Second Generation, MSG), (A) cloud structures as post-processing product from CAMS, (AS) in-situ measurements (pyranometric, air-pollution, PV metering, …).

Acknowledgment:

The e-shape project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 820852

GitHub Master Branch of the Solar Energy Pilot

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