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.
2. Defining Renewable Generation Potentials
There is an extensive literature on the assessment of
potential generation from renewables (e.g.
Vries et al. 2007,
Angelis-Dimakis et al. 2011, Moriarty & Honnery 2012). 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).
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.
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:
- Individual Constraints. All layers split out individually so the user can switch on one at a time.
- 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).
- 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.
- 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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