IRENES Land Use Tool Data Documentation

1. Introduction

This document describes the data sources, definitions and assumptions incorporated in the IRENES Land Use Tool (ILUT) available within the XMAP platform (https://irenes.xmap.cloud/login). It outlines the resources available for the territory covered by the Greater South East Net Zero Hub (GSENZH) as of June 2024.

A PDF version of this document is available at the bottom of this page

2. Defining Renewable Generation Potentials

There is an extensive literature on the assessment of potential energy generation from renewables (e.g. de Vries et al. 2007, Angelis-Dimakis et al., 2011; Moriarty & Honnery, 2012; Oakleaf et al., 2019; Sochi et al., 2023). A common approach is to first consider the conversion efficiencies from the input source (e.g. wind speed or solar radiation) to an output such as electricity, followed by the definition of areas actually suitable for generation. For instance, Angelis-Dimakis et al. (2011, p.1183) distinguish:

Potential Energy - the gross energy of the source (e.g. from wind at a given location).
Theoretical Energy – the fraction of potential that can be harvested by the energy conversion system (e.g. the electricity output from a set of solar panels).
Exploitable Energy – the fraction of theoretical output that can be used, taking into account criteria regarding environmental, economic and logistical issues (e.g. areas excluded by incompatible land cover/use or costs of grid connection).

A further consideration is to identify the renewable resources under assessment. The Land Use Tool focuses on onshore wind and ground-based solar photovoltaics due to their importance in current national generation and the availability of spatially detailed data on siting factors.

Roof-mounted solar panels are not considered because they are mainly associated with built-up urban areas and consequently their interaction with other land uses is limited. Other renewables such as biomass crops or offshore wind could be added to ILUT, but would require consideration of additional siting factors so have not been assessed in this implementation (e.g. see Gove et al., 2016; Delafield et al., 2024).

Figure 1: Stages in developing an evidence base for renewable energy potential (SQW Energy, 2010).

3. Assessing Potential and Theoretical Energy

There are now many online atlases or databases that provide details of renewable potentials or energy infrastructure. An excellent catalogue to many open data sources is provided by the https://energydata.info/ platform (funded by the World Bank Group). For the purposes of the Land Use Tool it was important to identify data that were as recent as possible and could provide resource estimates at a resolution of approximately 1 km. The following sources were used in the analysis.

Wind Speed – Data were extracted from the New European Wind Atlas available at https://map.neweuropeanwindatlas.eu/. Meso scale (~3 km resolution) details at a 100 m height and 30 minute time step were downloaded for 2016-18. R scripts (https://www.r-project.org/) were written to automate this process and derive monthly averages. These values were further summarised in MS Excel. The point data were then imported into the ArcGIS software (https://www.esri.com/en-us/arcgis/about-arcgis/overview), projected from longitude/latitude co-ordinates to the British National Grid, and subsequently interpolated using a natural neighbours method to produce average wind speeds (metres per second, hereafter m/s) on a 1 km raster grid.

ii.

Solar Radiation – Data for the UK were obtained from the Global Solar Atlas (published by the World Bank, funded by ESMAP) available at https://solargis.com/maps-and-gis-data/overview. Long-term annual averages of daily totals for 1994-2018 at 0.0025 degree grid resolution (~ 300 m) were downloaded for Global Irradiation at Optimum Tilt (GTI), measured in kWh/m². These surfaces were imported into ArcGIS and projected to the British National Grid at 250 m cell resolution using bilinear interpolation.

Further comparison of these resource maps in terms of energy potentials involved standardising them to power densities. This concept is widely used in the assessment of energy systems and refers to the quotient of power output and land area (Smil, 2016; Cheng & Hammond, 2017). Calculating such densities requires a number of assumptions (e.g. regarding conversion efficiencies or spacing of infrastructure) and these are documented in Table 1. The assumptions were derived from a review of relevant literature and are deliberately conservative in terms of potential electricity output.

Table 1: Assumptions made in the calculation of theoretical energy potentials.

Calculation Steps	Calculation Results	References

Wind Speed
Example wind speed	7.5 m/s
Power density = (0.5 * Density of Air) * (Wind Speed)³	(0.5 x 1.3) x (421.9) = 274.2 W/m²	MacKay (2009) p.264
Maximum extractable power (59%)	0.59 x 274.2 = 161.8 W/m²	MacKay (2009) p.264
Power of turbine 80 m diameter and 100 m hub height	161.8 x (3.1416 x 40²) = 813,297 W
Turbine minimum spacing (5 diameters)		MacKay (2009) p.265
Power density (power of turbine / land area of turbine)	813,297 / (5 x 80)² = 5.1 W/m²
Theoretical energy (as power density)	5.1 W/m²

Solar Radiation
Example Global Irradiation at Optimum Tilt (GTI)	140 W/m²
Assumed panel efficiency (17.5%)	0.175 x 140 = 24.5 W/m²	microgen-database.sheffield.ac.uk
Ratio of panel area to plant area (40%)	0.4 x 24.5 = 9.8 W/m²	Solar Trade Association data
Theoretical energy (as power density)	9.8 W/m²

Table 1 includes example calculations based on typical values for the East of England. These results indicate a theoretical power density for solar PV that is approximately double that for onshore wind. Similar contrasts are apparent in the findings presented by Smil (2016, p.203). It is important to note, however, that these are estimates of potential capacity and do not incorporate load factors that will also influence the amount of electricity that can be generated over a year.

The parameters from Table 1 were used in the Land Use Tool to produce estimates of theoretical electricity generation for a mesh of 1 km resolution cells. Figure 1 shows the resulting spatial variations in electricity power density for solar PV and onshore wind.

4. Determining Exploitable Energy Potentials

To assess the exploitable energy it is necessary to identify factors that could prevent the use of land for renewable energy generation. Within England there are planning regulations that restrict developments in the vicinity of certain facilities (e.g. airfields), but currently no national strategic or zoning framework. However, many studies have examined suitability criteria for different renewables (e.g. SQW Energy, 2010; Watson & Hudson, 2015; Gove et al., 2016; Palmer et al., 2019; McKenna et al., 2022). For the purposes of the IRENES Land Use Tool a two-stage approach was adopted. The first of these involved the compilation of a series of spatial data layers on land use characteristics based on criteria discussed in previous studies. Secondly, a data-driven assessment was carried out to examine the influence of these layers on the existing distribution of generation sites. This involved extracting details of grid references and generation capacity for all operational or under construction onshore wind or ground solar PV installations from the Renewable Energy Planning Database (REPD, https://www.gov.uk/government/publications/renewable-energy-planning-database-monthly-extract). The ArcGIS software was then used to overlay these points on the polygons for the different land use factors. This made it possible to assess the extent to which the distributions coincided and therefore the degree to which different factors appeared to exclude current generation facilities. These results were then used to group siting factors into categories of likely development constraint.

Spatial data to represent the different land use factors were obtained from the sources listed in Table 2. These layers were then imported into ArcGIS and combinations of buffer and overlay operations used to identify the extent to which they coincided with the locations of existing generation sites.

Table 2: Data sources used to define the land use factors.

Feature	Source	URL

Motorways, A & B Roads	Ordnance Survey VectorMap® District	https://osdatahub.os.uk/downloads/open
Railways	Ordnance Survey VectorMap® District	https://osdatahub.os.uk/downloads/open
Rivers, Canals, Lakes & Reservoirs	Ordnance Survey VectorMap® District	https://osdatahub.os.uk/downloads/open
Populated Areas	Census 2021 LSOA Centroids	https://geoportal.statistics.gov.uk
Airports & Airfields	Civil Aviation Authority, MoD, Ofcom	https://www.caa.co.uk/
		https://www.raf.mod.uk/our-organisation/stations/
		https://www.ofcom.org.uk
Ministry of Defence Land	Ordnance Survey OpenMap - Local	https://osdatahub.os.uk/downloads/open
Airspace Restrictions	Met Office and NATS	https://www.metoffice.gov.uk
		https://nats-uk.ead-it.com
Slopes	Ordnance Survey Terrain® 50	https://osdatahub.os.uk/downloads/open
Peaty Soils	Natural England	https://naturalengland-defra.opendata.arcgis.com/
Monuments & Heritage Sites	Historic England	https://historicengland.org.uk/listing/the-list/data-downloads/
Ancient Woodland	Natural England	https://data.gov.uk
Managed Woodland	Forestry Commission	https://data-forestry.opendata.arcgis.com/
Designated Nature Reserves	Natural England	https://naturalengland-defra.opendata.arcgis.com/
National Landscapes and Parks	Natural England	https://naturalengland-defra.opendata.arcgis.com/
Green Belt	DLUHC	https://data.gov.uk
Green Spaces	Ordnance Survey	https://osdatahub.os.uk/downloads/open/OpenGreenspace
Agricultural Land Classification	Natural England	https://data.gov.uk
Risk of Flooding from Rivers & Sea	Environment Agency	https://data.gov.uk

Table 3 lists the hectares covered by different land use factors within the GSENZH territory. Several of these had buffer zones defined around them based on suggestions in the literature. For each factor the table also lists the number of solar PV sites (as of January 2024) and their total electricity generation capacity (in MW) that occurred within the polygons. It is clear from this that some factors completely or largely excluded solar sites (e.g. National Parks), whilst in other cases they were quite common (e.g. Grade 1 or 2 agricultural land). Since the sites also varied in capacity an overall measure of likely absence was calculated as the ratio of the percentage of total regional capacity within the factor polygons divided by the percentage of the GSENZH area they occupied. With this measure a low value (e.g. at or close to zero) implies that few existing sites occurred where the factor was present, whilst a higher value (e.g. above 1.0) suggests that it was not an important constraint. From these results it is clear that proximity to infrastructure, populated area, various designations and high flood risk were important siting constraints, whilst higher quality agricultural land and peaty soils had less influence. These capacity/ha ratios were used alongside discussions in policy documents to produce a three-level classification of siting constraint as shown in the right-most column. As a general rule if the ratio was < 0.1 then the classification was ‘Very Unlikely’, if it was between 0.1 and 0.5 it was ‘Unlikely’ and if above 0.5 then ‘Potentially Available’. However, a small number of exceptions to this approach were made, particularly where the number of sites involved was small.

Table 3: Land use siting assessment for solar PV.

Solar Siting Factor	Area in Hectares	Solar Sites	Capacity in MW	% Capacity / % Ha Ratio	Classification
Main Roads + 15 m Buffer	58,181	0	0.0	0.00	Very Unlikely
Railway Tracks + 15 m Buffer	14,650	0	0.0	0.00	Very Unlikely
Main Water Courses + 150 m Buffer	306,059	18	112.9	0.39	Unlikely
Lakes and Reservoirs + 150 m Buffer	38,366	3	14.7	0.40	Unlikely
Airports/Airfields + 500 m Buffer	92,044	6	13.0	0.15	Unlikely
Military Land	4,629	1	1.5	0.34	Very Unlikely
Designated Nature Reserves	245,366	1	13.0	0.06	Very Unlikely
Ancient Woodland	166,154	1	2.8	0.02	Very Unlikely
Managed Woodland	470,102	6	87.8	0.20	Unlikely
Monuments & Heritage Sites	17,854	0	0.0	0.00	Very Unlikely
Registered Parks & Gardens	73,413	0	0.0	0.00	Very Unlikely
OS Open Greenspace	120,940	4	39.4	0.34	Unlikely
National Parks	195,829	0	0.0	0.00	Very Unlikely
National Landscapes (AONBs)	585,991	7	34.8	0.06	Very Unlikely
Green Belt	566,453	26	265.7	0.49	Unlikely
Slope (>5%)	788,605	21	163.5	0.22	Unlikely
Agricultural Land Grade 1 or 2	947,477	115	1,164.3	1.30	Potentially Available
High Flood Risk	163,478	2	1.0	0.01	Very Unlikely
Medium Flood Risk	235,627	13	91.7	0.41	Unlikely
Peaty Soils	231,559	20	225.9	1.03	Potentially Available
Census Centroids + 200 m Buffer	468,152	8	69.6	0.16	Unlikely
Census Centroids + 500 m Buffer	1,090,850	48	367.0	0.36	Unlikely
Census Centroids + 1 km Buffer	2,230,859	165	1,312.2	0.62	Potentially Available

Totals	4,139,096	356	3,922.6	-

Table 4: Land use siting assessment for onshore wind.

Solar Siting Factor	Area in Hectares	Solar Sites	Capacity in MW	% Capacity / % Ha Ratio	Classification
Main Roads + 150 m Buffer	519,979	2	3.3	0.04	Very Unlikely
Railway Tracks + 150 m Buffer	135,901	1	9.0	0.38	Unlikely
Main Water Courses + 150 m Buffer	306,059	1	20.7	0.38	Unlikely
Lakes and Reservoirs + 150 m Buffer	38,366	1	1.5	0.22	Unlikely
Airports/Airfields + 5 km Buffer	329,192	1	2.2	0.04	Very Unlikely
Rader Sites + 5 km Buffer	25,256	0	0.0	0.00	Very Unlikely
Military Land	4,629	0	0.0	0.00	Very Unlikely
Designated Nature Reserves	245,366	0	0.0	0.00	Very Unlikely
Ancient Woodland	166,154	0	0.0	0.00	Very Unlikely
Managed Woodland	470,102	0	0.0	0.00	Very Unlikely
Monuments & Heritage Sites	17,854	0	0.0	0.00	Very Unlikely
Registered Parks & Gardens	73,413	0	0.0	0.00	Very Unlikely
OS Open Greenspace	120,940	2	2.5	0.12	Unlikely
National Parks	195,829	0	0.0	0.00	Very Unlikely
National Landscapes (AONBs)	585,991	0	0.0	0.00	Very Unlikely
Green Belt	566,453	2	2.5	0.03	Very Unlikely
Slope (>15%)	76,460	0	0.0	0.00	Very Unlikely
Agricultural Land Grade 1 or 2	947,477	27	307.4	1.84	Potentially Available
High Flood Risk	163,478	2	11.5	0.40	Very Unlikely
Medium Flood Risk	235,627	21	196.9	4.75	Potentially Available
Peaty Soils	231,559	9	84.1	2.07	Potentially Available
Census Centroids + 200 m Buffer	468,152	0	0.0	0.00	Very Unlikely
Census Centroids + 500 m Buffer	1,090,850	5	18.1	0.09	Unlikely
Census Centroids + 1 km Buffer	2,230,859	20	120.3	0.31	Unlikely

Totals	4,139,096	76	727.9

Figure 2: Classification results for solar PV and onshore wind.

Further refinements to the classifications were to incorporate variations in power density for solar radiation and wind speed as well as proximity to electricity substations. The power density estimates were such that very little of the region was excluded on the basis of unviable generation potential. An analysis of proximity to electricity substations (primary, bulk or grid supply locations from network operator data portals) indicated that the average distance to solar PV sites was 2.4 km and for onshore wind turbines 2.9 km. A buffer distance of 3 km around existing substations was therefore used to identify areas that would be more suitable for renewable energy developments. No attempt was made to define available connection capacity at individual substations since this is complex to determine and any estimate would be best obtained from the relevant network operator. Using a buffer distance does, however, provide a simple screening regarding the scope for network connection.

Tables 5 and 6 indicate how the values of the % capacity / % ha ratios vary between the different classification categories when proximity to substations is incorporated. This is termed the ‘full’ classification below. As would be anticipated, there is a strong gradient, with the highest ratio in ‘Potential Areas’ closer to a substation and very low ones in a more distant area classed as ‘Very Unlikely’. It is also apparent that the influence of substation proximity is stronger of solar PV sites than onshore wind turbines. Overall the trends are sufficiently strong to help identify where additional capacity might be located based on current experience. The map in Figure 3 shows the spatial distribution of the different categories in the full classification.

Table 5: Capacity / ha ratio results for the full solar PV classification.

Classification	< 3 km from Substation	> 3 km from Substation	Total
Potential Area	4.71	1.03	2.31
Unlikely Area	0.65	0.48	0.59
Very Unlikely Area	0.10	0.01	0.04
Total	1.52	0.54	1.00

Table 6: Capacity / ha ratio results for the full onshore wind classification.

Classification	< 3 km from Substation	> 3 km from Substation	Total
Potential Area	5.73	2.81	3.69
Unlikely Area	1.26	0.76	0.99
Very Unlikely Area	0.02	0.07	0.05
Total	1.04	0.96	1.00

Figure 3: Full classification results for solar PV and onshore wind.

5. Implementation in the Land Use Tool

The approaches and data discussed above are incorporated in the IRENES Land Use Tool in several different ways. Within the tool the following folders of resources are available. Hovering your mouse over a layer name will display a pop-up with more information about how the layer is defi ned and sources.

· Prospecting Sidebar – this allows the user to define potential areas and then calculates their theoretical electricity generation using the power densities in Table 1. The total for all sites within a parish can also be calculated.

· Boundaries Sidebar – shows the GSENZH boundary, plus those of local government units.

· Solar Potential Sidebar – displays the areas covered by the different classification categories, either individually or in combination. The full classification incorporating substation proximity is also available.

· Wind Potential Sidebar – displays the areas covered by the different classification categories, either individually or in combination. The full classification incorporating substation proximity is also available.

· Designations Sidebar – includes individual map players for different categories of nature reserves, heritage sites or other planning zones.

· Infrastructure Sidebar – includes individual map players for roads, railways, airfield buffers and military sites.

· Land Use Sidebar – includes individual map players for the agricultural land classification, woodland and greenspaces.

· Populated Areas Sidebar – includes individual map players for buffer zones of different distances around census centroids.

· Resource Potential Sidebar – includes individual map players for solar radiation, wind speed and derived power densities.

· Sites and Substations Sidebar – includes electricity substations (from network operator data portals), and existing solar PV or onshore wind sites from the Renewable Energy Planning Database (January 2024).

· Soils and Geology Sidebar – includes map layers on peaty soils, subsidence risk (from British Geological Survey analysis) and slope steepness

· Water Sidebar – Includes map layers on rivers, lakes, reservoirs and flooding risks.

In terms of how these resources might be used we envisage that the classifications in the Solar Potential and Wind Potential sidebars would provide a quick way of screening a region of interest for possible development sites. Individual factors from other sidebars could then be added to refine this assessment. Alternatively, the user could build up their own evaluation by combining individual factors as they wished. In either case, if a particular locality was identified then the tools in the Prospecting sidebar could be used to define individual sites and assess their electricity generation potential. Further capabilities may be added in subsequent versions of the tool.

Acknowledgements

Several spatial data sets used in the analysis are © copyright Ordnance Survey. Others are used under the terms of the Open Government Licence v3.0. Wind speed data were obtained from the New European Wind Atlas, a free, web-based application developed, owned and operated by the NEWA Consortium. For additional information see www.neweuropeanwindatlas.eu. Solar radiation data are © 2019 The World Bank, source: Global Solar Atlas 2.0, solar resource data: Solargis.

The research underpinning the compilation of the data layers and tool creation was funded by a combination of the EU Interreg Europe Programme (IRENES), the UK Energy Research Centre Phase 4, the Greater South East Net Zero Hub, five county councils in eastern England (Cambridgeshire, Essex, Hertfordshire, Norfolk and Suffolk) and UK Power Networks. Chris Mewse (Geoxphere) was responsible for the implementation within the Parish Online platform. We are also grateful to our local government and regional stakeholders for their advice and feedback.

References

Angelis-Dimakis A, Biberacher M, Dominguez J, Fiorese G et al. (2011) Methods and tools to evaluate the availability of renewable energy sources. Renewable and Sustainable Energy Reviews 15, 1182-1200.

Cheng VKM, Hammond GP (2017) Life-cycle energy densities and land-take requirements of various power generators: A UK perspective. Journal of the Energy Institute 90, 201-213.

de Vries BJM, van Vuuren DP, Hoogwijk MH (2007) Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach. Energy Policy 35, 2590–2610.

Delafield G, Smith GS, Day B, Holland RA, Donnison C, Hastings A, Taylor G, Owen N, Lovett AA (2024) Spatial context matters: Assessing how future renewable energy pathways will impact nature and society. Renewable Energy, 220 (119385). https://doi.org/10.1016/j.renene.2023.119385.

Gove B, Williams LJ, Beresford AE, Roddis P, Campbell C, Teuten E, Langston RHW, Bradbury RB (2016) Reconciling biodiversity conservation and widespread deployment of renewable energy technologies in the UK. PLOS ONE, DOI:10.1371/journal.pone.0150956.

MacKay DJC (2009) Sustainable Energy – Without the hot air. UIT Press, Cambridge. Available at http://www.withouthotair.com/.

McKenna R, Mulalic I, Soutar I, Weinand JM, Price J, Petrovic S, Mainzer K (2022) Exploring trade-offs between landscape impact, land use and resource quality for onshore variable renewable energy: An application to Great Britain. Energy 123754. doi: 10.1016/j.energy.2022.123754

Moriarty P, Honnery D (2012) What is the global potential for renewable energy? Renewable and Sustainable Energy Reviews 16, 244– 252.

Oakleaf JR, Kennedy CM, Barush-Mordo S, Gerber JS, West PC, Johnson JA, Kiesecker J (2019) Mapping global development potential for renewable energy, fossil fuels, mining and agricultural sectors. Scientific Data 6(101). doi.org/10.1038/s41597-019-0084-8.

Palmer D, Gottschalg R, Betts T (2019) The future scope of large-scale solar in the UK: Site suitability and target analysis. Renewable Energy 133, 1136-1146. doi: 10.1016/j.renene.2018.08.109.

Smil, V (2016) Power Density. MIT Press, Cambridge, Massachusetts.

Sochi K, Oakleaf JR, Bhattacharjee A, Evans JS, Vennović I, Dropuljić KZ, Mileusnić D, Bevk T, Bjelić IB, Dedinec A, Doliak D, Gorin S, Pavolvić B, Zec M, Kiesecker JM (2023) Mapping a Sustainable Renewable Energy Transition: Handbook for Practitioners. The Nature Conservancy. https://www.nature.org/content/dam/tnc/nature/en/documents/Europe_En.

SQW Energy (2010) Renewable and Low-Carbon Energy Capacity Methodology – Methodology for the English Regions. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/226175/renewable_and_low_carbon_energy_capacity_methodology_jan2010.pdf.

Watson JJW, Hudson MD (2015) Regional Scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape and Urban Planning 138, 20–31.

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IRENES Land Use Tool Data Documentation

IRENES Land Use Tool Data Documentation

1. Introduction

2. Defining Renewable Generation Potentials

3. Assessing Potential and Theoretical Energy

4. Determining Exploitable Energy Potentials

5. Implementation in the Land Use Tool

Acknowledgements

References

Related Articles

Using the Data Extract tool

Requests for council data

Plotting your Owned Land

Using the Annotate Tool

Using the Print Tool