Lab: Sun-Shine-Energy

On a global scale, solar energy drives our planet's climate 'engine'. More locally, it makes plants grow, generates electricity and thermal energy.

Solar 'shortwave' energy input depends on numerous factors, including latitude, time of day and year, topography / relief and the current state of the atmosphere.

Modeling the local potential for solar energy therefore requires considering these and other factors and constraints.

Municipalities increasingly are fostering the use of renewables, with solar energy being the most local and distributed option, equally directly supplying citizens and feeding electricity into the grid.

Solar energy potential cadastres like the example on the right require geospatial analytics. In this module, we want to explore some of the key steps towards such results.

Solar analysis essentially is based on visibility analysis - from the sun's perspective. Generally, we are interested in three types of questions:

1. Sun-exposed and shaded areas for a particular date and time (=position of the sun), sometimes differentiating between shade and (cast) shadows.
2. Sunshine duration at one location, or over a study area, for a defined period of time.
3. Amount of (shortwave) solar radiation for a point or study area over defined periods.

(2) and (3) require not only the definition of a timespan (e.g. one day, a specific season, or year), but also specifying temporal resolution. Keeping in mind that the sun moves 15° per hour, depending on the analysis purpose time steps between 15min and 1hr are usually chosen - processing times will reflect finer resolutions, though!

• What areas and other locations (buildings) will be affected by a new construction project?
• What are the hours of sunshine to be expected in a particular location over a specific period of time (e.g. season)?
• What is the solar energy potential, e.g. on the rooftops of a village?

As long as specific or average weather situations are not taken into account, sunshine duration and solar energy would be best-weather estimates, for many regions not reflecting typical weather patterns. Thus this type of enquiry is within the domain of 'theoretical topoclimatology' ;-)

Solar analysis frequently is done on larger, more detailed scales. In particular when analyzing built-up areas, we will not only be interested in the ground surface elevation, but want to take into account the built environment - working either with a 3D feature model, or a raster Digital Surface Model (DSM) instead of a DEM. In this unit we will (again) experience working with a DSM.

Detailed analysis anywhere in the province of Salzburg can be achieved with a 1m resolution DEM as well as DSM. For analysis performed in ArcGIS Pro, data sources need to be connected from there:

• Insert > Connections > Server > New ArcGIS Server: https://image2001.zgis.at/arcgis 
• Map > Add Data > Data > Project > Servers > select from elevation (for insolation analysis in built-up areas the DSM is required) 
• (Anywhere in the world the by now well known 'Terrain' DEM can be used for this purpose, as long as not a DSM would be preferable due to a focus on detail and built-up areas) 

For visualisation based on a hillshade across Austria, an (at least) 1m resolution DEM as well as DSM are available. In ArcGIS Pro, data sources to be connected are:

• Insert > Connections > Server > New WMTS Server: https://basemap.at/wmts 
• Map > Add Data > Data > Project > Servers > select from basemap.at 'Gel' (DEM) and 'Oberfl' (DSM, for insolation analysis in built-up areas this is required) 

For visualisation of this DEM/DSM in ArcGIS Online -required for generating maps to be included in eg Storymaps- Add > Add Layer from Web > A WMTS OGC Web Service > URL: https://basemap.at/wmts > GET LAYERS Gelände and Oberfläche.

! For analysis with this high resolution terrain service make sure you work on a limited study area, and/or reduce cell size through Environments settings.

A valuable source for worldwide open LiDAR data sets useful for solar radiation and other hi-res topographic modeling is the OpenTopography site:

As a first step, we employ the ArcGIS Pro ' Hillshade ' function to simulate the illumination of a DSM from one specific sun angle, corresponding to a particular date and time of day.

To calculate the angle to the sun, we use the  NOAA Solar Calculator . For the village of Anif, we starting with setting lat = 47.75 and long = 13.07, time zone to +1 and no DST. For e.g. Dec 21 and 11:00am, we get an approximate aspect at 165° and 18° elevation.

Enter these values into the hillshade tool, and select 'Model Shadows'. Running the tool should result in the display somewhat like this - demonstrating the long shadows on this particular day in winter.

 To get a better feel for suntracks across any (Austrian) topography, you might want to experiment with the 'Geoland' >>> solar trajectory calculator ! (>>>  Doku und Parametrisierung  - that's all in German language only).

Instead of calculating sun-vs-shade only for one particular date and time,  modeling the hours of sunshine and the amount of solar radiation  over any defined period requires following the path of the sun for one day or any other period of time and aggregating the hours of solar exposure.

This is done with the  Area Solar Radiation  tool, using the DSM as Input Raster, setting the Time Configuration 'Within a day' (= for a single day, like the 355th day of the year), opening 'Optional outputs' and providing a name for 'Output direct duration raster'.

In this example an 'Hour interval' of 0.5 has been chosen, stepping along the path of the sun in half-hourly intervals and calculating sunny vs shaded spots for each step. After aggregating these steps, a value range from zero sunshine (blue) to a maximum of 8 hours (red) on this particular day shows up on this map of sunshine duration.

While we might enjoy the hours of sunshine - depending on climate zone, season and personal preference - for most physical processes the amount of solar  shortwave radiation  as a key component of the surface  energy budget  is more important. This includes processes like melting of snow and ice, or the photosynthetic and growth potentials of vegetation and specifically agricultural crops. 

In built-up areas, we are interested in the potentials for the generation of photovoltaic electricity or solar thermal energy on rooftops. For all these analyses, not only the duration of sunlight exposure, but rather the angle between the local surface and incident solar radiation is most relevant - sunrays and thus radiation hitting the surface at a perpendicular (90°) angle resulting in maximum energy reception.

In the previous step, a map of potential solar shortwave energy already was generated. It's units are W/m², in this sample ranging from zero to approx 760. We can therefore check for rooftops where solar panels would generate the highest yield, which would be the most favourable locations for placing panels and collectors. 

Of course we have to keep in mind that for limiting our processing time, this analysis was done only for a single day, not for an entire course of a year. In addition, we had implied 'perfect weather' and standard atmospheric conditions - these and other settings would have to be adjusted to more realistic assumptions under 'Radiation parameters'.

To dig a little deeper into the modeling of photovoltaics, explore and try to understand how the Global Solar Atlas is working:

In some instances, we are interested not in creating a 'computationally expensive' area solar radiation map, but aim at simulating shortwave solar input for a set of points. with the ' point solar radiation ' tool. These could be sample locations at agricultural test plots or bird nesting sites. 

In the context of solar panels on building roofs, a calculation of solar potential for one representative (e.g. centroid) point per planar roof facet might be enough, as long as there is no differential shading over the facet's area from e.g. nearby trees or other buildings. The resulting energy input then just needs to be multiplied with the desired or feasible panel area. 

Compared to the previously demonstrated raster approach of per-cell calculations, this vector point calculation obviously will be much faster, allowing for finer time increments, but does not take into account shading from environments.

As is clearly visible from all the options and parameters offered on the dialogs in this tool set, we barely have scratched the surface of solar radiation modeling. For well-founded practical applications it will be necessary to dig much deeper - not only with respect to Geoinformatics, but in particular to establish a suitable atmospheric and climatological parameterisation:

• The setting of time intervals needs to be adjusted to specific use cases
• Surface geometry characteristics need to be established, e.g. solar panels are not necessarily mounted parallel to a DSM surface - look for explicit angular adjustment in point solar radiation modeling
• Atmospheric parameters will change during simulation periods, this will require more sophisticated model construction (looping through a list of parameters)
• The interplay of direct and diffuse ('sky') radiation, and reflected radiation from surroundings can be quite complex in some scenarios
• Spatial resolution of DEM/DSM varies, and so do results which are severely scale dependent in particular over rough / varied surfaces
• There are few stations measuring global radiation components, making it difficult to properly calibrate models

Despite these and other issues, modeling solar energy potentials is an important domain of spatial analysis and serves as key component in many physical, biological and economic models. 

Typical tasks you can set yourself:

• Identify the ten buildings in the study area promising the highest energy yield from rooftop solar panels
• Estimate seasonal differences in solar energy panel potentials in specific locations, checking whether the weakest period still meets minimum demand thresholds
• Identify suitable areas for planting sun flowers ;-)
• Create a solar energy 'roof cadastre' for a community
• Model the solar shortwave radiation component for a regionalized snowmelt model

Based on the exercises and examples demonstrated in this unit, you are encouraged to choose your own study area and experiment with different parameterizations of a solar radiation model. E.g. compare the impact of different time interval settings or atmospheric parameters on model outputs.

G