IRENES Land Use Tool Data Documentation

IRENES Land Use Tool Data Documentation


1.   Introduction

 This document describes the data sources, definitions and assumptions incorporated in the IRENES Land Use Tool (LUT).

Please note: this functionality is currently only available to selected users as part of an initial working group.

 



·         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). 

In a similar manner, SQW Energy (2010, p.4) identify a series of steps (shown in Figure 1) that progressively reduce the theoretical opportunity down to what is practically achievable.  This approach has been used in a number of UK assessments (e.g. AECOM & The Landscape Partnership, 2011; Camco, 2012), though only the first four stages are relevant for the purposes of the Land Use Tool.



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


A further consideration is to identify the renewable resources under assessment.  The Land Use Tool focuses on three, namely onshore wind, ground-based solar photovoltaics and biomass cops (specifically Miscanthus).  These were selected for the following reasons:

·         Their importance in current regional and national generation.

·         The availability of spatially detailed data on input resource.

·         They represented different types of siting requirements and conversion characteristics.

Roof-mounted solar panels and heat pumps were not considered because they are mainly associated with built-up urban areas and consequently their interaction with many types of ecosystem services (a particular focus of IRENES) is limited.  Offshore wind is a key renewable for the East of England,  but the manner in which the rights to develop marine sites are awarded and managed by the Crown Estate (https://www.thecrownestate.co.uk/en-gb/what-we-do/on-the-seabed/) is completely different from land-based renewables in terms of a much higher degree of central control.   Consequently, any assessment of generation potential would need to consider a rather different set of factors (e.g. see Gove et al., 2016).

3. Assessing Potential and Theoretical Energy for Three Renewables


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.

          i.             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 (https://www.microsoft.com/en-us/microsoft-365/excel) to generate descriptive statistics for each grid point over 2016-18.  The point data were then imported into the ArcGIS software (https://desktop.arcgis.com/en/), 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 Horizontal Irradiation (GHI) and Global Irradiation at Optimum Tilt (GTI), both measured in kWh/m2.  These surfaces were imported into ArcGIS Desktop and projected to the British National Grid at 250 m cell resolution using bilinear interpolation.

       iii.            Miscanthus Yields – Estimates of yield from the MiscanFor bioenergy model (Hastings et al., 2009; Shepherd et al., 2020) were kindly provided by Dr Astley Hastings, University of Aberdeen.  MiscanFor uses gridded climate and soil data to estimate yield, with the results in this instance representing tonnes of harvested dry matter per hectare (based on average climate conditions during 2000-16).  The supplied grid was at 0.0083 degree resolution (~1 km), though there were underlying block patterns reflecting variations in the detail of the climate and soil data.  Bilinear interpolation was again used to create a 1 km resolution output with British National Grid co-ordinates.

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, the discussion by MacKay (2009) proving particularly useful in this respect.  

 

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

Miscanthus Yield

Example yield (tonnes dry matter harvested)

12.0 tonnes/hectare

Energy content (18 GJ per dry tonne, 1 GJ = 277.78 kWh)

12 x 18 x 277.78 = 60,000 kWh

Morandi et al. (2016) p.314

Convert to density (divide by hours per year)

60,000 / 8,766 = 6.845 kW/hectare

Rescale to W/m2

0.68 W/m2

Conversion efficiency of biomass to electricity (32%)

0.32 x 0.68 = 0.22 W/m2

Lovett et al. (2009) p.24

Theoretical energy (as power density)

0.22 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, both being an order of magnitude greater than the estimate for Miscanthus.  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 parishes or user-defined geographical areas.


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 the UK there are planning regulations that restrict developments in the vicinity of certain facilities (e.g. airfields), but no national framework of zoning for renewables.  However, several guidance documents regarding siting have been published.  A particularly influential example is that by SQW Energy (2010) which was commissioned by the UK Government to provide a methodology for assessing opportunities and constraints for deploying renewables on a regional scale.  This report presents criteria for a wide variety of renewables, while other studies have examined the suitability of land for particular sources (e.g. Lovett et al., 2014; Watson & Hudson, 2015; Palmer et al., 2019).  For the purposes of the IRENES Land Use Tool, the criteria in these reports were reviewed and the most cited factors were grouped into the following three categories (derived from discussion in AECOM & The Landscape Partnership, 2011, p.78).

·         Level 1: hard constraints based on known physical or regulatory limitations.

·         Level 2: soft constraints associated with certain habitats or anticipated public opposition.

·         Level 3: considerations linked to heritage or biodiversity designations.

The results of this exercise are summarised in Table 2 which lists the criteria identified for each of the three types of renewable under consideration. 

 

Table 2: Factors restricting the suitability of land for renewable energy developments.

Onshore Wind Turbines

Solar Photovoltaic Arrays

Biomass Crops

Level 1

Level 1

Level 1

Motorways, A & B Roads (150 m Buffer)

Motorways, A & B Roads (15 m Buffer)

Motorways, A & B Roads (15 m Buffer)

Railways (150 m Buffer)

Railways (15 m Buffer)

Railways (15 m Buffer)

Rivers, Canals (150 m Buffer)

Rivers, Canals (150 m Buffer)

Lakes & Reservoirs (150 m Buffer)

Lakes & Reservoirs (150 m Buffer)

Lakes & Reservoirs (150 m Buffer)

Airports & Airfields (5 km Buffer)

Airports & Airfields (500 m Buffer)

Airports & Airfields (500 m Buffer)

Airspace Restriction Zones

 

 

Ministry of Defence Land

Ministry of Defence Land

Ministry of Defence Land

Slopes > 15%

Slopes > 5%

Slopes > 15%

Residential Areas

Residential Areas

Residential Areas

Level 2

Level 2

Level 2

Residential Areas (600m Buffer)

Residential Areas (300m Buffer)

Organic & Peat Soils

Ancient & Managed Woodland

Ancient & Managed Woodland

Ancient & Managed Woodland

Public Rights of Way (15 m Buffer)

Level 3

Level 3

Level 3

Scheduled Monuments & Battlefields

Scheduled Monuments & Battlefields

Scheduled Monuments & Battlefields

Registered Parks & Gardens

Registered Parks & Gardens

Registered Parks & Gardens

World Heritage Sites

World Heritage Sites

World Heritage Sites

SPAs, SACs, RAMSARs, SSSIs, NNRs

SPAs, SACs, RAMSARs, SSSIs, NNRs, LNRs

SPAs, SACs, RAMSARs, SSSIs, NNRs, LNRs

 

 

Biodiversity Action Plan Priority Habitats

 

Some factors in Table 2 are identical in all three cases (e.g. excluding residential areas), while in others the size of buffer zones around features varies.  In general, the buffer distances are larger for onshore wind turbines than solar arrays or biomass crops.  Other factors are specific to individual renewables, such as the exclusion of certain soil types and land very close to public rights of way (due to visual amenity considerations) for biomass crops.

Heritage and biodiversity designations are listed under Level 3 because, physically, they could be developed.  In practice, however, this is highly unlikely.  Further details of the various biodiversity designations are given at https://jncc.gov.uk/our-work/uk-protected-areas/.   Some sites are established under global agreements (RAMSAR), others originate from European (SACs, SPAs) or national (SSSIs, NNRs) legislation.  Local Nature Reserves (LNRs) are a statutory designation made by local authorities. 

It should be emphasised these criteria do not include important economic aspects that can further limit the potential for renewables-based generation.  These include the costs of grid connection and the value of agricultural production from the land.  In subsequent discussion with the project Steering Group it was decided to define two further levels of constraint.  These were Grades 1 and 2 land in the Agricultural Land Classification (Level 4) and land defined by the Environment Agency at medium or high risk of flooding by rivers or sea (Level 5).  In addition, details of generation headroom capacity at the surrounding substations were estimated using data from the UK Power Networks Open Data Portal (https://www.ukpowernetworks.co.uk/open-data-portal).

Spatial data to represent the different factors listed in Table 2 were obtained from the sources listed in Table 3.  These layers were then imported into GIS software and combinations of buffer, reclassification and overlay operations used to identify the areas excluded at the different levels.  The criteria were initially defined as a mixture of vector polygons and raster grids, so to facilitate comparison they were ultimately all amalgamated as fine resolution (10 m) raster grids.

 

Table 3: Data sources used to implement the land availability constraints.

Feature

Source

URL

 

 

 

Motorways, A & B Roads

Ordnance Survey VectorMap® District

Railways

Ordnance Survey VectorMap® District

Rivers, Canals, Lakes & Reservoirs

Ordnance Survey VectorMap® District

Residential Areas

Ordnance Survey VectorMap® District

Airports & Airfields

Eurostat

Ministry of Defence Land

Ordnance Survey OpenMap - Local

 

Airspace Restrictions

NATS (National Air Traffic Services)

Slopes

Ordnance Survey Terrain® 50

Organic & Peat Soils

European Soil Data Centre

Monuments & Heritage Sites

Historic England

 

Public Rights of Way

Local Authorities

Ancient Woodland

Natural England

Managed Woodland

Forestry Commission

SSSIs, NNRs, LNRS

Natural England

SPAs, SACs, RAMSARs

Defra

Biodiversity Plan Priority Habitats

Priority Habitats Inventory

Agricultural Land Classification

Natural England

Risk of Flooding from Rivers & Sea

Environment Agency


 The approaches and data discussed above are incorporated in the IRENES Land Use Tool in several different ways:

Prospecting

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.




Once saved, pan the map, and the Megawatt potential will show.




Constraints Groups

This allows the user to toggle on relevant constraints to their prospecting work. Click to expand the group and select individual layers, or right-click and toggle all layers on.


These are the groups available:

  1. Individual Constraints. All layers split out individually so the user can switch on one at a time.
  2. Viable Land - Solar. Displays the areas excluded by the different combinations of factors for solar PV at Levels 1 to 5.  Note that the Levels are implemented in a cumulative manner so that, for example, Level 2 includes the Level 1 constraints as well.  On the map this means that the potentially viable areas are those remaining white (i.e. not covered by any of the constraint factors).
  3. Viable Land - Wind. Displays the areas excluded by the different combinations of factors for onshore wind at Levels 1 to 5.  The interpretation of the Levels is the same as that described above for solar PV.
  4. Viable Land - Biomass. Displays the areas excluded by the different combinations of factors for biomass at Levels 1 to 5.  The interpretation of the Levels is the same as that described above for solar PV.

Further capabilities may be added in subsequent versions of the tool.


 

Several spatial data sets used in the analysis are © copyright Ordnance Survey.  Others are used under the terms of the Open Government Licence (OCL) or are © European Soil Data Centre (ESDAC).  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.  We are also grateful to Dr Astley Hastings, University of Aberdeen, for kindly providing output from the MiscanFor model.


 

AECOM & The Landscape Partnership (2011) East of England Renewable and Low Carbon Energy Capacity Study.  Available at https://www.scambs.gov.uk/media/6374/1667-east-england-renewable-capacity-report.pdf.

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.

Camco (2012) Cambridgeshire Renewables Infrastructure Framework – Baseline Data, Opportunities and Constraints.  Available at https://scambs.moderngov.co.uk/documents/s62041/Full%20CRIF%20Report%20-%20online%20only.pdf.

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.

Committee on Climate Change (2019) Net Zero. The UK’s Contribution to Stopping Global Warming.  Available at http://www.theccc.org.uk/publications/.

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.

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.

Hastings A, Clifton-Brown J, Wattenbach M, Mitchell CP, Smith P (2009) The development of MISCANFOR, a new Miscanthus crop growth model: Towards more robust yield predictions under different climatic and soil conditions. Global Change Biology Bioenergy 1, 154–170.

Lovett AA, Sünnenberg GM, Richter GM, Dailey AG, riche AB, Karp A (2009) Land use implications of increased biomass production identified by GIS-based suitability and yield mapping for Miscanthus in England. Bioenergy Research 2, 17–28.

Lovett AA, Sünnenberg G, Dockerty T (2014) The availability of land for perennial energy crops in Great Britain. GCB Bioenergy 6, 99–107.

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

Morandi F, Perrin A, Østergård H (2016) Miscanthus as energy crop: Environmental assessment of a miscanthus biomass production case study in France. Journal of Cleaner Production 137, 313-321.

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

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

Shepherd A, Littleton E, Clifton-Brown J, Martin M, Hastings A (2020) Projections of global and UK bioenergy potential from Miscanthus × giganteus—Feedstock yield, carbon cycling and electricity generation in the 21st century. Global Change Biology Bioenergy 12, 287–305.

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

SQW Energy (2010) Renewable and Low-Carbon Energy Capacity Methodology – Methodology for the English Regions.  Available at 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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