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2 Analysis

2.1 Approach

The four renewable energy resources studied were onshore-wind, offshore-wind, wave and tidal-current, as they are the most abundant (Garrad Hassan 2001a) and the most variable. Existing hydro-generation and projected new hydro and biomass resources were not included in this investigation. They are likely, however, to make an important contribution to meet the forty-percent target set for 2020.

The study area was chosen to cover all of the component Scottish landmasses and surrounding sea and ocean areas. The maps in this report all depict the same study area which is defined in terms of the British National Grid. The south-west corner is at easting 0 km and northing 500 km, and the north-east corner is at easting 500 km and northing 1,250 km. The basic unit of area was 1 km2 and is generally referred to as a 'cell'. The study area thus comprised an array of 750 rows by 500 columns, a total of 375,000 1 km2 cells.

This study presents scenarios relating to a range of possible future portfolios of renewable energy generation in Scotland. Renewable generation was compared with demand on an hour-by-hour basis. Tidal currents are predictable over long periods of time, however, wind and waves vary throughout the year as well as inter-annually. Therefore the resource data should ideally be taken from as many years as possible so that generation siting decisions reflect long-term average conditions. The notional year for the study was 2020, but the input data was drawn from the years 2001, 2002 and 2003 as these were the most recent years for which concurrent time-series of meteorological resource data and electrical demand data could be obtained. The demand was scaled up by one-percent per year to allow for anticipated growth in electricity consumption by 2020, but renewable resource data was not modified to anticipate any possible effects of climate change.

2.2 Model Outline

The process that was used to model renewable electricity generation used ArcGIS, a Geographical Information System ( GIS), along with a number of other computer applications. The GIS program produced maps and was used to establish spatial relationships between resource, generation and electrical load datasets. The spatial units used within the GIS were the 1 km2 cells that make up the study area. Datasets from various sources were assembled in the GIS database. Figure 2.1 illustrates the conversion of GIS datasets to a common raster (image) format with a resolution that corresponds to the 1 km2 cells of the study so that logical and numerical operations can be carried out.

Figure 2.1 Vector and raster data processing in a GIS.

By overlaying datasets and performing mathematical and logical operations on them it is possible to create new spatial datasets. One dataset often contains the primary information with others acting as constraints. For example, the marine renewable resource could be the feature which is constrained by water depth. Figure 2.2 shows a number of constraint datasets that were used within the study. These maps are also included in a larger format in the Appendix.

Figure 2.2 Examples of GIS datasets.
(a) Scotland political; (b) Scotland physical; (c) Natural and cultural heritage areas.

Figure 2.3 is a diagrammatic representation of the first part of the modelling process: the production of time-series of renewably generated electricity. The logical flow illustrated in Figure 2.3 involved the progressive creation of a resource map, a cost map and a generation map. These maps are 2-dimensional arrays of numbers that correspond to the 1 km2 cells of the study area.

Figure 2.3 Modelling of costs and generation.

Resource maps were prepared from wind, wave and tidal data. They indicate the average long-term strength of the resource within each cell. The cost map was created from the resource map using various constraints and costing formulae to create relative rather than absolute costs for the selection process. The procedure for creating the cost map was as follows:

• Remove cells with absolute constraints (e.g. land slope too steep, water too deep, urban area);
• Calculate the annual energy output for the remaining cells in the map;
• Calculate the initial lifetime production costs per cell, excluding the cost of grid connection;
• Remove cells that are relatively too expensive;
• In consultation areas, where there is a defined limit to the allowable generating capacity within a certain area, remove cells that exceed the limit, starting with the most expensive ones;
• Calculate the density of occupied cells to determine the optimum grid connection option;
• Estimate final lifetime production costs including grid connection.

As an example, Figure 2.4a shows a map of areas with absolute constraints and with consultation status for developing onshore wind projects. Lifetime production costs, shown in Figure 2.4b, were calculated for unconstrained areas and, in the case of onshore-wind, for the best ten percent of cells in consultation areas.

Figure 2.4 Examples of constraint and cost maps for onshore-wind.
(a) Absolute constraint and consultation areas; (b) Lifetime production costs.

With additional selection criteria (e.g. cheapest 750 MW) the generation map was created. For the selected cells, time-series of resource (e.g. wind speed or wave height and period) were transformed into time-series of electrical power using the electricity generation characteristic of a particular machine.

The final output (lower right of Figure 2.3) consisted of one file for each of the four renewable technologies. Each file contains generation time-series for any 1 km2 cell that has generators assigned to it. The time-series list the hourly averages of the total power generated within that cell. One hour was used as the time step throughout the study because longer time series with higher sample rates are generally not available. The time-series modelling of energy conversion and the exploration of scenarios was largely done within Matlab, Visual Basic and Excel.

Once the generation time-series for each of the four technologies had been produced, the aggregate renewably generated power could then be compared with the concurrent electrical demand. This is further described in Section 5. In fully realistic studies that take account of the network characteristics, power-flow restrictions would reduce the number of renewable generators that could be connected at any time. To evaluate most freely the coincidence between the renewable energy and demand for electricity, the simplified case of an idealised network with zero impedance was used. This allowed all renewable energy generators to form an idealised 'best' mix of technologies to match the 40% demand target.

2.3 Input Data

A large amount of data was used within the modelling process and this section gives an overview of the datasets employed. Further details of the renewable energy sources are given in Section 3 and on the electrical load demand in Section 4.

2.3.1 Resource Data

The resource datasets are the most crucial part of the study and Table 2.1 summarises the principal sources for this information.

Table 2.1 Resource datasets.
Key: v used; - not used.
BWEA: British Wind Energy Association, DTI: Department of Trade and Industry, UKHO: UK Hydrographic Office.

2.3.2 Machine Selection

Once the resource time-series had been prepared they were transformed into time-series of electrical power from groups of wind, wave and tidal-current generators, by means of generic power conversion characteristics. Details of the generic machines are given in Section 3.

2.3.3 Geographical Information

The energy resource depends on the location. Wind blows more strongly at high altitudes or over a flat sea, waves are more powerful in deep water and tidal-currents are accelerated in narrow channels. Height (altitude) and water-depth (bathymetry) contours describe this topography within the GIS. Major geographical datasets are listed in Table 2.2.

Elevation data was converted for input to wind-modelling software and combined with bathymetry data to construct a digital terrain model ( DTM) of Scotland with 100 m resolution. The main source for bathymetry was the 'DigBath250' vector dataset from the British Geological Survey ( BGS). This has contour intervals of 10 m for depths from 0 to 200 m, 20 m for depths between 200 m and 400 m and 100 m for depths greater than 400 m. Certain areas (east of England, north of Ireland and south of the Faeroe Islands) were not covered by the purchased DigBath250 bathymetry data and were filled in from the British Oceanographic Data Centre's ( BODC) '1 minute' raster dataset.

In certain areas, there are differences of depths in shallow water between the bathymetry datasets and the Admiralty charts, which can exceed 10 m (for example in Yell Sound in Shetland and in the Pentland Firth). This could have caused some inaccuracies in the placement of offshore-wind turbines and tidal-current converters. There are further differences between the positions of coastlines between BGS (World Vector Shoreline) and Ordnance Survey (Strategi), but these are generally less than 1 km.in hart Datum refers to the ether with bathymetry to construct a digital terr

Table 2.2 Geographical datasets.
Key: v used; - not used.
BODC: British Oceanographic Data Centre, OS: Ordnance Survey, UKHO: UK Hydrographic Office, SRTM: Shuttle Radar Topography Mission, BGS: British Geological Survey.

New information can be derived from the GIS datasets. To evaluate potential marine energy sites economically, the distance to the shoreline and the distance to the nearest grid supply point were calculated in the GIS. Ground slopes and radar viewsheds around airfields were calculated from the 100 m digital terrain model. An additional model with a reduced resolution of 1 km as shown on Map 02 was used for further processing.

2.3.4 Natural and Cultural Heritage

Renewable energy developments can have a major impact on landscape and biodiversity. The 'National Planning Policy Guideline 6' issued by the Scottish Executive (2000) states that projects should not be developed when they compromise the interests of a protected site. In some areas developments are prohibited or undesirable, in others only a certain maximum number of generators may be acceptable. The available datasets are listed in Table 2.3, with some of them shown on Map 03.

In accordance with publications from Scottish Natural Heritage, in particular SNH (2001), the environmental designations were separated into high and medium sensitivity. The high sensitivity areas were treated as absolute constraints, with no renewable developments allowed within them. This interpretation is more strict than SNH (2001) which does not completely rule out developments in National Park and certain SSSI areas. The high sensitivity natural and cultural heritage areas which were treated as absolute constraints include:

• Natura 2000 sites: Special Areas of Conservation ( SAC), including candidate sites, and Special Protection Areas ( SPA);
• Ramsar sites, Sites of Special Scientific Interest ( SSSI), National Nature Reserves ( NNR), Local Nature Reserves ( LNR), Marine Consultation Areas ( MCA);
• National Parks and Regional Parks;
• World Heritage Sites;
• National Scenic Areas ( NSA);
• Scheduled Ancient Monuments.

Table 2.3 Natural and cultural heritage areas.
Key: • absolute constraint area; o consultation area; - not used.
SNH: Scottish Natural Heritage, SEGIS: Scottish Executive Geographic Information Service, Landmark: Landmark Information Group, HS: Historic Scotland.

Garrad Hassan (2001a) included Green Belts in the high sensitivity category. However, it was not possible to source a consistent dataset for this designation and SNH (2001) does not recommend classifying Green Belts as wind farm exclusion zones.

The medium sensitivity areas included:

• Country parks and Historic Gardens;
• Local landscape designations, including some further Areas of Great Landscape Value ( AGLV) in the Highlands;
• 'Search areas for wild land'.

At the time of writing, the 'wild land' areas ( SNH 2001) had not yet been further refined, otherwise they would certainly be highly sensitive. Listed buildings were not used as they are believed to be mostly in urban areas which were not available for development. The local landscape designations compiled by the Landmark Information Group, and referred to in Table 2.3, replaced the outdated AGLV dataset with the exception of the Highlands (in accordance with information from collaborators and partners).

2.3.5 Aviation Interests

Wind farms may impact on aviation as physical obstructions, and rotating blades may affect communication, navigation and surveillance ( DTI 2002). Generally a 30 km consultation radius applies to civil airfields, while military try of Defence ( MoD)no impan Aberdeenshire) and thrtechnical sites will be examined on a case-by-case basis. Developers need to consult the Civil Aviation Authority ( CAA) and the Ministry of Defence ( MoD) with regard to wind farm proposals.

Table 2.4 shows the datasets used within the study. Locations of airports and radars were used for GIS viewshed calculations while the surveillance radar maps and the low flying system mostly describe consultation areas.

Table 2.4 Aviation interest datasets.
Key: • absolute constraint area; o consultation area; - not used.
OS: Ordnance Survey, DE: Defence Estates, NATS: National Air Traffic Service.

Civil Airfields Airfields are issued with safeguarding maps, extending to radii of 15 km and 30 km, within which consultations must be made regarding developments. Radar viewsheds were calculated for all civil airfields in Scotland, assuming a radar height of 15 m and turbine tip-heights between 80 m and 120 m (with the latter being chosen for the final analysis). As an example, Figure 2.5 shows the results for Edinburgh Airport with a 100 m horizontal resolution. Areas within a 15 km radius seen by the radar are treated as absolute constraints while those within a 30 km radius are treated as consultation areas.

Figure 2.5 Radar viewshed calculation.
(a) Principle of viewshed calculation; (b) Edinburgh airport sample calculation, turbines in the coloured areas are visible to the radar. Map based on Ordnance Survey data.

En route (or area) radars are operated by the National Air Traffic Service ( NATS) who provides maps indicating areas where there is a probability or a potential that turbines will interfere with the radar. Within the study, areas of high sensitivity were assigned consultation status while those with lower sensitivity were not used (see Map 04). The dataset corresponding to 120 m tip height was used throughout as no data was available for the greater tip heights of offshore turbines.

Military Airfields According to the Defence Estates there are four military airfields in Scotland (Benbecula, Lossiemouth, Leuchars and West Freugh). As no further information was available, they were treated as civil airfields with the same safeguarding radii.

Air Defence There are three ground-based Air Surveillance and Control System ( ASACS) radars in Scotland (at Saxa-Vord on Shetland, South Clettraval on South Uist and Buchan in Aberdeenshire) and one in Northumberland (Brizlee Wood) whose operation could be affected by wind-turbine developments in Scotland. DTI (2002) describes a consultation radius of 74 km within which the developer has to prove that there will be no adverse impact. As absolute constraints have not been published, 74 km radius viewshed areas were assigned consultation status.

Low Flying System The UK Low Flying System ( LFS) includes the open airspace over land and extends up to 3 nautical miles (5.6 km) offshore. Low flying of military fixed-wing aircraft covers levels between 250 and 2,000 feet ( DTI 2002). Within tactical training areas ( TTAs) the range goes down to 100 feet. Two of the three TTAs are in Scotland, one in the Highlands (area 14 T, see Map 04) and one in the Scottish/English border region (area 20 T). There are already wind-turbine developments within these areas, but projects will be carefully scrutinised before approval is given. Within the study, these areas are considered to be consultation areas. According to the Defence Estates there are some regions within area 20 T which are less desirable for low-flying. For these the consultation constraint was lifted within the study. A further Electronic Warfare Tactics Range ( EWTR) can be found in the Scottish Borders region (area 13). As it is likely that no permission will be granted for wind-power developments in this area, it was considered an absolute constraint.

Met Office Radars Aviation safety depends on accurate weather forecasting for which the Met Office uses weather radars and wind-profiling radars. The former scans a narrow airspace between the horizon and one-degree of elevation, hence wind-turbines may interfere with operation. There are two weather radars in Scotland (at Stornoway in the Western Isles and Hill of Dudwick in Aberdeenshire). A third (Corse Hill in East Renfrewshire) is under decommissioning and will be replaced by two others which may be located in Stirling and Fife. Wind-profiling radars determine the variations in direction and speed with altitude. There is one in operation on South Uist in the Western Isles. The Met Office will most probably object to wind-turbine developments within a radius of 5 km and possibly object within 10 km radius. Within the study, these viewshed areas were assigned absolute and consultation constraint, respectively.

2.3.6 Land and Sea Use

Renewable energy developments may interfere with other forms of land and sea space usage. Table 2.5 shows relevant data sources that were consulted during the study. In particular, marine devices may block navigational channels. The best information freely available was the study carried out by Anatec UK Ltd., as published in Garrad Hassan (2001a). In the present study the data was only used as a first indicator, as its resolution is only 10 km and areas of water depth greater than 100 m which may be of interest for wave-energy converters were omitted. Military activities take place in Practice and Exercise Areas ( PEXA), but no general guideline exists to date on sensitivities. MoD will have to be contacted on a case-by-case basis. Potential renewable energy sites do not lie within Scottish ammunition dumping grounds, although a study by Fisheries Research Services, Aberdeen, published in SNH (2004b), shows that hundreds of explosives per square kilometre are commonly found outside the charted areas, even up to 40 km away (see also Map 05).

The Eskdalemuir seismological station has often been quoted as an obstacle to onshore-wind developments in the Borders area. Based on the most recent information available, Map 05 shows a 10 km radius exclusion area and a 17.5 km radius consultation area around the station. For wind resource calculations, surface roughness information for Scotland was derived from the European land cover database CORINE CLC90.

Table 2.5 Land and sea use datasets.
Key: v used; - not used.
Anatec: Anatec UK Ltd., UKHO: UK Hydrographic Office, OS: Ordnance Survey, EEA: European Environmental Agency.

2.3.7 Power System Data

Power system information was taken from the Seven Year Statements ( SP 2001 - SP 2004 and SSE 2002 - SSE 2004) for the transmission network and the Long Term Development Statements ( SP 2003, 2004 and SSE 2002, 2004) for the distribution network.

Both Scottish Power and Scottish and Southern Energy supplied coordinates of the electricity substations in their area. These were used to calculate the distances from potential generation sites to the nearest grid supply point.

Both power companies supplied demand information for their respective area and for some selected grid supply points. The processing of this is described in Section 5.

2.3.8 Financial Parameters

Capital and Operation & Maintenance cost information for 2010 was taken from Garrad Hassan (2001a) and some more recent device-specific publications, and broken down into typical costs as shown by example in Figure 2.6. More detailed cost information is given in Section 3.

Figure 2.6 Example project cost breakdown for onshore-wind.

The costs were input into a discounted cash flow model. In line with previous studies, a project lifetime of twenty-years and a discount rate of 8% were assumed for all technologies. The latter reflects a public-sector rather than a private-sector rate of return which would require a discount rate of at least 15%. The effects of inflation were ignored, and costs are based on 2005 prices. From the discounted sums of energy production and annual expenses, the lifetime production costs ( LPC) were calculated as:

(2.1)

The figures calculated for lifetime production costs serve for comparison between sites (but not technologies) and for the ranking of sites in order of lowest cost first.

Grid connection costs from Garrad Hassan (2001a) were updated to 2005 levels. They include capitalised operation and maintenance (O&M) costs. In the cases of Shetland, Orkney, and the Western Isles it was assumed that all projects in each of these areas would share the capital costs of undersea cable connections to the mainland in a manner similar to that published by Sinclair Knight Merz (2004). The GIS calculated an internal non-monetary least cost distance between a project and a grid connection point to suggest the best route. Only approximations were possible for the friction surface describing relative costs for crossing a particular distance. As an example, for onshore wind a value of 1 was used for land and a value of 10 for water and sea areas and natural heritage areas with 'high sensitivity' designation. The GIS then favoured sites that are close to grid supply points and chose cable routes around protected areas.

Power stations in Scotland which are connected to the transmission network pay Transmission Network Use of System ( TNUoS) charges. These charges are determined annually by National Grid and vary regionally. The arrangement of the zones can be changed every five years. Within the study the selection of renewable energy sites was rank-ordered based on economic merit. This includes estimates of the capital costs of connection but excludes the annual TNUoS charges that may apply. The three reasons for this exclusion follow.

Firstly, TNUoS charges may change considerably over time due to network reinforcement or addition of new power plants ( SEEFet al 2005). Hence it is difficult to estimate what the future charges will be in every region for deployment of a particular technology. Secondly, TNUoS charges vary considerably for different zones across the UK but they are relatively flat in Scotland ranging from about 12 £/kW in South Scotland to about 23 £/kW in the Northern Highlands (National Grid 2005). Finally, the DTI is considering special dispensation for up to 10 years in areas of high renewable energy potential which would otherwise face comparatively high transmission charges. Such dispensation would effectively further flatten the charges across Scotland. Since these matters were not resolved at the time of writing it was agreed to exclude TNUoS charges from the study.

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