Coordination by Armines, 60, boulevard Saint-Michel, 75272 Paris cedex 06. Contact : lucien.wald (at) mines-paristech.fr

Project ENDORSE Energy Downstream Service

Providing energy components for GMES Grant Agreement no 262892

Deliverable D 405.1: Report on the product Development S5 “CSP-GIS” for Morocco

Type of document:

Project report delivery

Deliverable Number: D405.1

Version Number and Revisions v0.1: first draft (17.11.2011) v0.2: updated draft (02.02.2012) v0.3: updated draft (22.08.2012) v1: submitted version (01.10.2012) v1.1: submitted version (08.10.2012)

WP Number: 405

Authors and Affiliation: Christoph Schillings (DLR) Thomas Wanderer (DLR) Thomas Huld (JRC)

Dissemination Level: Public

Estimated Indicative Person-Months:

9

Activity Type: RTD

Planned Delivery Date: M21, September 2012

Actual Delivery Date: M21, September 2012

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SUMMARY Finding suitable regions for future Concentrating Solar Power (CSP) plants is a crucial task in the early stage of project development or in the field of potential assessment. Within this work package a web-GIS based tool (CSP-GIS) is developed to perform such spatial analyses. The user-friendly and freely available internet tool gives everybody the chance to perform a spatial analysis which usually needs a Geographical Information Systems to be installed on your PC. CSP-GIS is a web-GIS with all necessary spatial data included to analyze suitable regions for CSP. The user can define criteria that lead to an exclusion or inclusion of areas for future CSP plants. Within this work package spatial data on e.g. solar resource (direct normal irradiation – DNI) and geographical information on land use, protected areas etc. are provided for Morocco. This report describes the tool and the spatial data. In addition, a case study for Morocco is performed to validate the results of the exclusion analysis with the location of future planned CSP sites.

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TABLE OF CONTENTS 1 Introduction .................................................................................................................... 4 2 Development of a “CPS-GIS for Morocco” ...................................................................... 6

2.1 Identification of suitable areas / Development of exclusion ...................................... 9 2.2 The assessment of the available solar resource .....................................................17 2.3 GMES services ......................................................................................................18

3 Validation of prototype and outcomes............................................................................19 4 References ....................................................................................................................22

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1 Introduction To identify the potential for future deployment of Concentrating Solar Power (CSP) in a region or country, a spatial analysis taking into account the available solar resource, the suitable areas and the existing infrastructure is a common accepted procedure. Such a spatial analysis is performed using a Geographical Information System (GIS) which is a software that easily handles and processes digital spatial data. A GIS can be used on a local PC to perform spatial analyses. New approaches develop methods to use such a GIS via a web portal which gives many users the chance to perform such spatial analyses without having a GIS to be installed on the local PC and without the need of having input data. The used data are provided by the web-portal itself or are embedded via geo-web services from other data providers. Spatial analysis performed by Geographical Information System (GIS) is a common way to identify potential for renewable energies in a country or region. For Concentrating Solar Power (CSP) technologies a potential analysis tool using a GIS was developed by DLR. The applied method in this tool is widely used by many research institutions and project developer. In the following the approach is described shortly. The potential analysis is divided in several steps:

1 Identification of suitable areas / Development of exclusion map 2 The assessment of the available solar resource 3 Ranking analysis with respect of existing infrastructure and solar resources costs

etc.) 4 Power plant simulation for best sites

Within the ENDORSE project, a web-based CSP-GIS is developed to assess the potential of Concentrating Solar Power Technologies for the example region of Morocco. This Web-GIS covers the two first two steps of the potential analysis, (1) development of exclusion maps and (2) solar resources assessment. Aim of the development of the CSP-GIS is a user-friendly tool that covers above mentioned two analysis steps via a web-interface. The tool has to provide all relevant spatial data to analyze the overall potential of CPS-technology for a user-defined region. The service which covers the functionality of the analysis itself is described in D6.5 “Report on the pre-market service S5 “CSP-GIS””. During the development of the tool it was find out that the product and the service of the tool is very strong connected to each other. This makes a separation of the description into two different reports quite difficult. The prototype of the tool is available at stbgis.dlr.de/client/endorese.html. To use the full functionality of the tool, the user has to be registered for a login. The following chapters describe the development of this product “CSP-GIS for Morocco”. The used input data, the development of the underlying web-GIS application and a comparison with existing assessments are described in detail.

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2 Development of a “CPS-GIS for Morocco” The layout of the tool itself is structured as a common GIS which is illustrated in Figure 2-1: On the left hand, the layer tree lists all available layers that will be displayed in the map on the right hand.

Figure 2-1: Opening/Welcome screen of the CSP-GIS tool. Based on existing analyses performed for former projects (DLR, 2003; IBERINCO et al. 2005; DLR, 2005; DLR & FICHTNER, 2011) following data are developed and provided in the tool that are needed for a reliable identification of suitable areas and for a potential analysis for CSP on a regional scale:

• direct normal irradiation • terrain (slope) • protected areas • infrastructure (streets, power grid) • hydrology • population density • land cover • geomorphology

As default all data that are developed during this work are available at the layer tree as single layers. Some of the data used in the tool are only available for Morocco, some have a larger spatial extent. All these data are stored in the “Endorse Data Catalogue”. If the user wants to create a new map, she/he can add or remove the layers from this existing Endorse Data Catalogue to compose the map. In a final version of the tool, the user

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can add additional data to the Endorse Data Catalogue that are not yet covered, or the user can add complete new Data Catalogues from different servers or single layers from a WebMapService (WMS). For example, the layers of the “geoland2 Data Catalogue” can be added as WMS. Figure 2-2 shows the example dialogue for add a new layer from the Endorse Data Catalogue to the actual map.

Figure 2-2: Example dialogue for the map management, here “Add layer”. The user can select the different layers to be displayed in the map view. To give a smooth design of the map, all layers can be manipulated with respect to opacity/transparency. With respect to the exclusion analysis a very important feature is developed. Each layer can be filtered based on user defined values. For an example, Figure 2-3 shows the creation of a filter for the layer population density. Here, the population density layer will be only displayed for all areas with a population density higher than 100 and lower than 1000 habitants/square kilometers.

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Figure 2-3: Creation of a filter, here population density. The user can filter all available data to compose its own map in the same way as described above. The tool provides a map management to create new, open, save, import and export maps. Figure 2-4 shows the example dialogue for “Save map”. The maps are stored on the server.

Figure 2-4: Example dialogue for the map management, here “save map”. The first process step for the potential analysis is the creation of the exclusion map. An exclusion map is a composition of several layers, showing suitable and non-suitable sites based on the exclusion assumptions. This functionality is developed within the CSP-GIS tool.

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The next subchapter gives an overview about the applied exclusion parameters and the used data for the tool.

2.1 Identification of suitable areas / Development of exclusion A very important part of the potential assessment is the determination of suitable sites for the construction of concentrating solar power plants. Relevant information like information on land use, land cover, elevation, protected areas etc. are combined to find suitable areas. The first step is the identification of potential sites. As shortly described above, this has to be done by developing an exclusion map. This exclusion map combines geographical information on existing land use and other criteria like elevation, land use, geomorphology etc. Which criteria should be defined for each of the parameter (land use, elevation etc.) to identify suitable or non-suitable sites for CSP plants? Table 1 shows an example of the definition of exclusion criteria. Some of the criteria are optional, some are compulsive. Exclusion Criteria Compulsive Optional

Solar Resource

Annual DNI > 2000 kWh/m² x Terrain

Slope > 2,1% x Land Cover

Post-flooding or irrigated croplands (or aquatic)

x

Rainfed croplands x Mosaic cropland / vegetation x Forest (>5m) x Mosaic forest, shrubland or grassland x Shrubland (<5m) x Herbaceous vegetation x Sparse vegetation x Permanetly or regularly flooded areas x Artificial surfaces and associated areas (Urban areas >50%)

x

Bare areas x Water bodies x Permanent snow and ice x No data (burnt areas, clouds, …) x

Population Density

Population density > 50 persons per km² x

Hydrology

Lake x Reservoir x River x Freshwater Marsh, Floodplain x Swamp Forest, Flooded Forest x Coastal Wetland x Pan, Brackish/Saline Wetland x Bog, Fen, Mire x Intermittent Wetland/Lake x

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Geomorphology

Shifting Sand, Dunes x Security Zone for Shifting Sands 10km x Salt Pans x Glaciers x Security Zone for Glaciers 10km x

Protected Area

IUCN Ia x IUCN Ib x IUCN II x IUCN III x IUCN IV x IUCN V x IUCN VI x Others x

Table 2-1: Exclusion criteria for CSP plants (DLR & Fichtner, 2011) Finally, the spatial information derived from the different data sets and the applied exclusion criteria are combined to yield a mask of exclusion criteria for a complete region or country. Figure 1 shows an example of such an exclusion map. On the left, information on slope, land use and geomorphology are given. The chosen exclusion criteria (e.g. slope >2.1% excluded) are applied on the corresponding dataset. The resulting exclusion maps for each parameter are combined to a final exclusion map (one the right in Figure 1). This map is based on an analysis performed within a former study (DLR, 2003) and is only an example.

Figure 2-5: Developing on an exclusion map to detect potential sites in Morocco for concentrating solar power plants. (DLR, 2003) Table 2 shows the data used for the exclusion analysis within the CSP-GIS as an overview. In the following, each of the data is explained in detail. Administrative Criteria

Name of Data set Spatial Resolution Source

Country border Global Administrative Unit Layers (GAUL) unknown (vector) FAO, 2008 Country mask Global Administrative Unit Layers (GAUL) 30 x 30 arc-sec FAO, 2008 Exclusion Criteria Name of Data set Spatial Resolution Source

Geomorphology FAO

Slope GLOBE

Land use USGS

potential sites in Morocco

black: excluded sites white: potential sites etc.

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Terrain (elevation + slope)

The Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model V1.0

30 x 30 arc-sec Hastings & Dunbar, 1999

Land Cover ESA Globecover V2.2 10 x 10 arc-sec Bicheron et al., 2008 Population Density LandScan Global Population Database,

2003. 30 x 30 arc-sec Oak Ridge National, 2003

Hydrology DCW provided by DIVA-GIS unknown (vector) Digital Chart of the World Geomorphology Digital Soil Map of the World (DSMW) V3.6 300 x 300 arc-sec FAO/UNESCO, 2007 Protected Area The World Database on Protected Areas

(WDPA) 300 x 300 arc-sec protectedplanet.net, 2010

Resource Criteria Name of Data set Spatial Resolution Source Solar Radiation SOLEMI 30 x 30 arc-sec DLR, 2012

Table 2-2: Used data for CSP-GIS Terrain CSP plants, especially parabolic trough power plants, require flat terrain. The land slope can be derived from a digital elevation model shown in Figure 2. For the analysis, the Global Land One-km Base Elevation Digital Elevation Model – GLOBE (Hastings & Dunbar 1999) for the determination of the slope is used. A slope higher than 2.1% is excluded for the building of CSP plants. The value of 2.1% is determined by the slope-function based on the elevation, the error of the GLOBE-data and the error propagation (Kronshage, 2001). Figure 3 shows the slope derived from the dataset.

Figure 2-6: Global Land One-km Base Elevation Digital Elevation Model in meters above sea level (GLOBE) from (Hastings & Dunbar, 1999).

Figure 2-7: Slope in percent derived from GLOBE Digital Elevation Model as shown in Figure 2.

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Landcover Information about land cover is taken from the European Space Agency’s (ESA) Globcover Database (Bicheron et al., 2008). The ESA Globcover map has a spatial resolution of 10 x 10 arc-seconds (~ 300 x 300 m²) and is classified in 23 categories. Figure 4 presents the land cover for the Morocco. Within the “Total” case, only areas with herbaceous vegetation (grassland, savannas or lichens/mosses), sparse vegetation and bare areas have been considered as suitable for potential CSP sites. This represents a very conservative approach.

Figure 2-8: Land cover classification and exclusion criteria for CSP. The new classification is based on Globecover (Bicheron et al., 2008) Population Information about population density has been taken from the Global Rural-Urban Mapping Project - GRUMP (GRUMP, 2004). Figure 5 presents the population density in Morocco. The spatial resolution of the data set is 30 x 30 arc-seconds (1 x 1 km²).

Figure 2-9: Population density in Morocco. (GRUMP, 2004) Hydrology

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The information on rivers and lakes has been taken from the Digital Chart of the world dataset.

Figure 2-10: Hydrology of Morocco (DCW, 1992) Geomorphology Certain areas and soils are not suitable to be used as foundation for concentrating solar collector fields due to their geomorphologic features. Salt areas because of their heavy corrosive features belong to it. But also dynamic structures like shifting sands form an exclusion area, which additionally is extended by a safety zone for the duration of operating (here 50 years). As the flow velocities can amount to 200 m/y this safety zone has to be at least 10 km width (Kronshage & Trieb, 2002). Sand dunes are unsuitable for the erection of pylons as the sand corns do not form a strong compound. Here the exclusion area also contains a safety zone which considers the mobility of certain dune types. Spatial information about sand dunes, salt areas and glaciers are taken from the ‘Digital Soil Map of the World’ (DSMW) of the FAO (FAO, 2007). The DSMW is based on the ‘Soil Map of the World’ (1:5 Mio.) of the FAO/UNESCO from the year 1978. The spatial resolution of the digital map amounts to approximately 10 x 10 km² (300 arc-sec). Altogether the DSMW identifies in 26 groups of soil types 106 soil types and additional non-soil features, which include the dunes, salt areas and glaciers of interest. As there exist neither extensive sand dunes nor glaciers, no geomorphological exclusion exists for Morocco based on the used data set.

Figure 2-11: Geomorphology exclusion criteria for Morocco

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Protected Areas The information on the protected areas is based on the 2010 World Database on Protected Areas WDPA (IUCN and UNEP, 2009). The WDPA is a joint product of IUCN and UNEP prepared by UNEP-WCMC and the IUCN- WCPA working with Governments, the Secretariats of Multilateral Environmental Agreements, collaborating Non-Government Organizations and individuals. IUCN has defined a series of six protected area management categories, based on primary management objective. In summary, these are: CATEGORY Ia: Strict Nature Reserve: protected area managed mainly for science defined as an area of land and/or sea possessing some out-standing or representative ecosystems, geological or physiological features and/or species, available primarily for scientific research and/or environ-mental monitoring. CATEGORY Ib: Wilderness Area: protected area managed mainly for wilderness protection defined as a large area of unmodified or slightly modified land, and/or sea, retaining its natural character and influence, without permanent or significant habitation, which is protected and man-aged so as to preserve its natural condition. CATEGORY II: National Park: protected area managed mainly for ecosystem protection and recreation, defined as a natural area of land and/or sea, designated to (a) protect the ecological integrity of one or more ecosystems for present and future generations, (b) exclude exploitation or occupation inimical to the purposes of designation of the area and (c) provide a foundation for spiritual, scientific, educational, recreational and visitor opportunities, all of which must be environmentally and culturally compatible. CATEGORY III: Natural Monument: protected area managed mainly for conservation of specific natural features, defined as an area containing one, or more, specific natural or natural/cultural feature which is of out-standing or unique value because of its inherent rarity, representative or aesthetic qualities or cultural significance. CATEGORY IV: Habitat/Species Management Area: protected area managed mainly for conservation through management intervention, defined as an area of land and/or sea subject to active intervention for management purposes so as to ensure the maintenance of habitats and/or to meet the requirements of specific species. CATEGORY V: Protected Landscape/Seascape: protected area man-aged mainly for landscape/seascape conservation and recreation, defined as an area of land, with coast and sea as appropriate, where the interaction of people and nature over time has produced an area of distinct character with significant aesthetic, ecological and/or cultural value, and often with high biological diversity. Safeguarding the integrity of this traditional interaction is vital to the protection, maintenance and evolution of such an area. CATEGORY VI: Managed Resource Protected Area: protected area managed mainly for the sustainable use of natural ecosystems, defined as an area containing predominantly unmodified natural systems, managed to ensure long term protection and maintenance of biological diversity, while providing at the same time a sustainable flow of natural products and services to meet community needs. Besides protected areas which are categorized by the IUCN and thus are designated as protected areas, the utilized dataset includes also a reasonable number of protected areas which are just proposed, recommended or voluntary-recognized (referred as “Others”).

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Figure 2-12: Protected areas of Morocco Power grid An important figure for planning new power stations is the knowledge about existing/planned transmission/power grid and substations and about existing/planned power plants. This knowledge is required due to the need of a balanced distribution of power plants and due to costs in to respect to distance of the planned new power plant to the next transmission line and/or substation. Using a Geographical Information System (GIS) to assess potential new power plant sites, geo-referenced information on the grid, the substations and the power plants is needed. This means that not only the knowledge of the existing but also of the exact location in terms of geographical coordination is required. In the following, the work about the development of a geo-reference power grid map with information on transmission lines, substations and power plants is described. By now, the author could not get any geo-referenced maps of the power grid for Morocco. There exists a map which shows the “Electrical Network of Arab Countries” (Council of Arab Ministers of Electricity, 2011). Figure 2 shows the Moroccan subset of this map. Another map of the Moroccan Electricity Grid is available through the Global Energy Network Institute (GENI, 2011) which is given in Figure 3.

Figure 2-13: Electrical Network of Morocco (Council of Arab Ministers of Electricity, 2011)

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Figure 2-14: Map of Moroccan Electricity Grid (GENI, 2011) Both maps show schematically the distribution of the major electrical transmission lines and their voltage. The location of the lines is neither exact nor geo-referenced which means that the data cannot be simply implemented in and not be used by a GIS as the geographical coordination is missing. The knowledge on the exact location is absolutely necessary for site identification as described before. Therefore, there is a need for digitizing the required information, which means that each transmission line is provided with a geographical coordinate after the digitizing process. The information on the existing major transmission lines from the two maps showed in Figure 2-11 and Figure 2-12 is taken as base information. Together with a GIS and satellite/aerial data from Google Earth and Bing maps, the exact location of the transmission lines is identified. Figure 2-13 shows as example the identification of several transmission lines starting from a substation. As in many cases the pylons and the transmission lines itself can be easily identified with Google Earth, the transmission lines can be tracked and digitized. The information on the voltage can be taken from the map “Electrical Network of Arab Countries” (see Figure 2-11) which represents the transmission lines greater or equal 110kV.

Figure 2-15: Identification and digitization of transmission lines using Google Earth. Green and blue digitized lines indicate the existing transmission lines and are available for the analysis. For many transmission lines the exact location can be digitized using Google Earth or Bing Maps. If the exact location cannot be identified due to e.g. old satellite images or satellite

substation

pylons

digitized transmission line (225kV) with

estimated location

digitized transmission lines (green = 110kV, blue = 225kV) with

exact location

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images with a low resolution, the location is estimated with the information taken from (Council of Arab Ministers of Electricity, 2011) and GENI (2011). This work leads to a dataset which can be used in the GIS for further analysis. Figure 2-14 shows the map which was digitized as described above.

Figure 2-16: Digitized Electricity Map of Morocco based on the map shown in Figure 4.

2.2 The assessment of the available solar resource This used method models the optical transparency of the atmosphere to calculate the Direct Normal Irradiance (DNI) on the ground, by quantifying those atmospheric components that absorb or reflect the sunlight, like clouds, aerosols, water vapour, ozone, gases and other. Most of this information is derived from satellite remote sensing SOLEMI (2012).

Weather satellites like Meteosat-7 from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) are geo-stationary satellites at a distance of 36,000 km at a fix point over the globe that send half-hourly images for weather forecasting and other purposes. From those images, the optical thickness of clouds can be derived obtaining half-hourly cloud values for every site. Of all atmospheric components, clouds have the strongest impact on the direct irradiation intensity on the ground. Therefore, the very high spatial (5 x 5 km) and temporal (0.5 hour) resolution provided by METEOSAT is required for this atmospheric component.

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Figure 3-1: Original image from METEOSAT 7 (top left), aerosol content from GACP (top centre), water vapour content from NCAR-NCEP (top right) and resulting map of the hourly Direct Normal Irradiance (bottom) in W/m² for the Iberian Peninsula and the Maghreb Region on February 7, 2003, 12:00 (SOLEMI, 2012).

Aerosols, water vapour, ozone etc. have less impact on solar irradiation. Their atmospheric content can be derived from several orbiting satellite missions like NOAA and from re-analysis projects like GACP or NCEP/NCAR and transformed into corresponding maps/layers of their optical thickness. The spatial and temporal resolution of these data sets can be lower than that of clouds. The elevation above sea level also plays an important role as it defines the thickness of the atmosphere. It is considered by a digital elevation model with 1 x 1 km spatial resolution. All layers are combined to yield the overall optical transparency of the atmosphere for every hour of the year. Knowing the extraterrestrial solar radiation intensity and the varying angle of incidence, the direct normal irradiation can be calculated for every site and for every hour of the year. Electronic maps and GIS data of the annual sum of direct normal irradiation can now be generated The resulting map of the annual sum of DNI can be used for further GIS analysis.

2.3 GMES services One of the main features of the CSP-GIS is the option to easily implement and use addition data catalogues provided by other institutions also under GMES. By now, only the solar radiation database SOLEMI can be addressed via a MACC Rad service. As soon as further data services covering the needed input data for the CSP-GIS analysis are provided by other institutions these data catalogues can be included in the tool.

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3 Validation of prototype and outcomes Main objective of the first level of development of CSP-GIS is the exclusion analysis for CSP plants. In a second level of development, further analysis like potential assessment or ranking analysis will be provided. This will be described in the report “Report on the pre-market service S5 “CSP-GIS” (D6.5). The idea of the validation is to check whether the tool can give reasonable results with respect to suitable sites for future CSP plants based on an exclusion analysis. There exist several studies performed in the last years that analyzed the potential of CSP in Morocco. E.g. the German funded study SOKRATES (DLR, 2003), the EU-project INDITEP (IBERINCO et al. 2005) and the German funded study MED-CSP (DLR, 2005) or the newer study MENA Water Outlook 2010 funded by KfW (Kreditanstalt für Wiederaufbau) (DLR & FICHTNER, 2011). In these studies a GIS analysis was performed to identify suitable areas and the overall potential for several countries or a region. For the validation of the prototype the results of these studies will not be used as there exist better information on suitable regions/sites for future CSP plants. The Moroccan Agency for Solar Energy (MASEN) proposes five regions for future CSP plants. These regions are chosen by MASEN as prioritized locations for future solar plants according to the Moroccan Solar Plan that aims for 2000MW installed capacity based on solar energy in 2020. Following regions are selected: Ain Beni Mathar, Ouarzazate, Sebkhat Tah, Foum Al Ouad and Boujdour (see Figure 3-1). These five defined regions are very qualified for a validation task for this exclusion analysis.

Figure 3-1: Proposed regions for Solar Plants according to Moroccan Solar Plan (MASEN, 2012; Richts, 2012) According to Table 2-1 following criteria are applied for the validation analysis:

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Criteria Excluded Direct Normal Irradiation (DNI) < 2000 kWh/m²a Population > 50 Habitats/sqkm Slope > 2.1% Protected Areas All Inland Waterbodies All Landcover Waterbodies, Maritime Wetlands, Inland Wetlands,

Snow and Ice, Artificial Surfaces & associated areas, Mosaic: Cropland/Shrubs/Grass/Trees, Forest, Burnt Areas

Table 3-1: Applied exclusion criteria for the validation analysis. The result of the analysis is given in Figure 3-2. As the solar radiation data is only available for the Northern part of Morocco, only the three Northern CSP sites can be used for the validation. The CSP-GIS overlays all spatial information on the defined exclusion criteria an markes all suitable areas as white and all excluded areas as black. This is done for each exclusion criteria (for each layer) separately. In a final step, all layers are overlaid and a final map is created. This final map is shown in Figure 3-2.

Figure 3-2: Result of the exclusion analysis for the validation. Black pixels indicate areas that are excluded due to exclusion criteria as defined in Table 3-1. White pixels indicate areas that are generally suitable based on this analysis. Red triangles mark the proposed CSP sites according to the Moroccan Solar Plan. Following Figure 3-3 to Figure 3-5 show zoomed subsets covering the regions around the CSP sites proposed by MASEN. In all three cases the exclusion analysis provides reasonable results as all three CSP site proposed by the Moroccan Solar Plan are within areas that are identified as suitable by the exclusion analysis.

Ain Beni Mathar

Ouarzazate

Tarfaya

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Figure 3-3: approximate location of the CSP-site Ain Beni Mathar (white pixels indicate suitable areas, black pixels indicate excluded areas.)

Figure 3-4: approximate location of the CSP-site Ouarzazate (white pixels indicate suitable areas, black pixels indicate excluded areas.)

Figure 3-5: approximate location of the CSP-site Tarfaya (white pixels indicate suitable areas, black pixels indicate excluded areas.) In this report, the product CSP-GIS was shortly described. As the tool is still under development, not all functionalities are implemented by now. Here, the exclusion analysis and the underlying data are presented. The full functionality will be described in the “Report on the pre-market service S5 “CSP-GIS” (D6.5).

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4 References

Bicheron, P., P. Defourny, C. Brockmann, L. Schouten, C. Vancutsem, M. Huc, S. Bontemps, M.

Leroy, F. Achard, M. Herold, F. Ranera and O. Arino. (2008): Globcover: products description and validation report. ESA Globcover Project led by MEDIAS France/POSTEL.

Broesamle, H. H. Mannstein, C. Schillings and F. Trieb (2001): Assessment of solar electricity

potential in North Africa based on satellite data and a Geographic Information System. Solar Energy 70(1), 1-12

Council of Arab Ministers of Electricity (2011): Electrical Network Arab Countries. Map produces by

Arab Union of Producers, Transporters & Distributors of Electricity. http://www.medemip.eu/Calc/FM/MED-EMIP/AUPTDE/Electicity_Interconnection_Maps/Arab_World.pdf (last access on 20th September 2012).

DCW (1992): Digital Chart of the World. http://www.diva-gis.org/gdata (last access on (last access on

20th September 2012). DLR (2003): SOKRATES – Solarthermische Kraftwerkstechnologie für den Schutz des Erdklimas. (in

German) DLR (2005): MED-CSP – Concentrating Solar Power for the Mediterranean Region. DLR & Fichtner (2011): MENA Regional Water Outlook, Part II Desalination Using Renewable Energy,

Task 2 – Energy requirements. Stuttgart, Germany. FAO (2007): Digital Soil Map of the WORLD (DSMW v3.6)

(http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116) (last access on 20th September 2012).

GENI - Global Energy Network Institute (2011): Map of Moroccan Electricity Grid.

http://www.geni.org/globalenergy/library/national_energy_grid/morocco/moroccannationalelectricitygrid.shtml (last access on 20th September 2012).

GRUMP (2004): Center for International Earth Science Information Network (CIESIN), Columbia

University; International Food Policy Research Institute (IFPRI); The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2004. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1). Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw. (last access on 20th September 2012)

Hastings, D.A. and P.K. Dunbar (Eds) (1999): Global Land One-kilomeer Base Elevation (GLOBE).

NOAA, National Geophysical Data Center, Boulder, Colorado. IBERINCO et al. (2005): INDITEP – Intergration of DSG technology for electricity production. Final

technical report. EC contract no. ENK5-CT-2001-00540. IUCN and UNEP. (2009): The World Database on Protected Areas (WDPA). UNEP-WCMC.

Cambridge, UK. www.wdpa.org (last access on 20th September 2012). Kronshage, Stefan (2001): Standortanalyse für solarthermische Kraftwerke am Beispiel des

Königreichs Marokko, Diploma Thesis, DLR Stuttgart and University of Osnabrück, Germany, 2001 Kronshage, Stefan and Franz Trieb (2002): Berechnung von Weltpotenzialkarten. Erstellung einer

Expertise für den Wissenschaftlichen Beirat der Bundesregierung Globale Umweltveränderung (WBGU). Internal Documentation, DLR, Stuttgart. (in German)

ENDORSE Product Development S5, “CSP-GIS” for Morocco

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MASEN (2012): Moroccan Agency for Solar Energy:

http://www.masen.org.ma/index.php?Id=15&lang=en#/_ Richts, Christoph (2012): The Moroccan Solar Plan – A comparative analysis of CSP and PV

utilization until 2020. Master Thesis, University of Kassel, Germany. http://www.uni-kassel.de/eecs/fileadmin/datas/fb16/remena/theses/batch2/MasterThesis_Christoph_Richts.pdf

SOLEMI (2012): Solar Energy Mining, http://www.solemi.com (last access on 20th September 2012).