Carsten Hoyer-Klick, Franz Trieb, Christoph Schillings (German Aerospace Center, Institute of Technical Thermodynamics – Germany), Lucien Wald (Mines ParisTech, – France), Thomas Huld (European Commission, Joint Research Centre – Italy) and Marion Schroedter-Homscheidt (German Aerospace Center, German Remote Sensing Data Center)
The Earth is endowed with abundant renewable energy resources, especially solar. These resources are however not fully exploited at the moment despite their recognized benefits. In order to establish appropriate instruments and strategies for the market introduction of renewables, well founded information on demand and resources, technologies and applications is essential. As a first step, an in-depth analysis of the available solar resources is needed. This can be delivered by remote sensing methods.
Sustainable development of the world economy needs a sustainable energy supply. This means that energy has to be available at affordable and stable prices, with low environmental impact and giving everyone everywhere a fair opportunity for development.
International and national policies must establish appropriate framework conditions for the expansion of renewable energies. Only then will industry and investors support such projects and provide the necessary large investments, as demonstrated by the success of the German and Spanish renewable energy acts. Remote sensing can bring valuable information to support market introduction of renewable energy technologies.
Concepts for Market Introduction
In order to establish appropriate instruments and strategies for the market introduction of renewables, well-founded information on demand and resources, technologies and applications is essential.
Figure 1 shows the necessary steps of a cascade for the successful development of renewable energy investments. Investors need appropriate and stable political and economic framework conditions that guarantee their return of investment. The cascade starts with the analysis of the available resources. These are, for example, maps of wind speeds, annual solar irradiance or available biomass. These results can be combined with data of available area for the different technologies (e.g. roofs and land area for PV, land area with suitable irradiance levels or wind speed for concentrating solar power and wind power) to determine the technical feasible potential of the different technologies. The level of the available resources can then be used to calculate where these technologies become economically viable, in order to determine their economic potential. This number shows which technologies are feasible to contribute to the national energy system at an economic level.
This selection of suitable technologies is the first important step in strategy development. The next step is the development of scenarios, which show possible pathways to the future, how different technologies can grow, what their energy price will be and what share they can contribute to the energy system under certain boundary conditions. The scenarios are the basis for strategic decisions: which technologies to support first, how and where to develop them. Based on these strategic decisions, instruments for market introduction can be designed, such as feed-in tariffs, soft loans, clean development mechanisms, etc. Those instruments will set the political and economic framework conditions for the investors to invest into renewable energy. Remote sensing can be used to derive the information about the available resources and usable land area in a high spatial resolution.
Once the framework conditions are set, investors can support such projects and provide the necessary large investments, as demonstrated by the success of the German and Spanish renewable energy acts.
Solar radiation is the “fuel” of solar energy systems. Its availability directly determines the economic income of solar energy power plants; knowledge of the level of resources is therefore crucial.
The direct normal irradiance is the amount of solar radiation arriving directly from the sun (ignoring radiation from the rest of the sky), and falling onto a plane perpendicular to the direction of the sun. It can be used for electricity generation via concentrating solar thermal power plants or concentrating PV. Direct irradiance has the advantage that it can be concentrated with mirrors to reach high temperatures or high radiative fluxes, and the disadvantage that it is only available in cloud free situations. Therefore energy systems that use only the direct irradiance are only practical in sunny regions where cloud-free conditions are common.
The available solar irradiance is mapped from remote sensing data. It is based on images from geostationary meteorological satellites, which are used to map the amount of clouds. The methodology is described in detail for direct normal irradiance in (Schillings et al., 2004) and for global irradiance in (Hammer et al., 2003) and (Rigollier et al, 2004). The global irradiance is the important parameter for non-concentrating photovoltaic systems and flat plate collectors.
Figure 2 shows the annual sum of the direct normal irradiance in the Mediterranean Region in kWh/m2 for the year 2002. The countries in the south of the Mediterranean show an especially high solar energy potential: The resources approach 3000 kWh/m2 in many areas. Considering a conversion efficiency of 10% from radiation to electricity, a Concentrated Solar Power (CSP) plant covering one km2 of land can deliver about 250 GWh per year (Trieb and MÌ_ller-Steinhagen, 2007).
Characteristics of Solar Energy
Solar radiation is highly variable in time and space. The annual sum of incoming solar radiation can change significantly from year to year due to natural weather variations. Everybody remembers hot or very rainy summers. Figure 3 shows the annual variability of global horizontal and direct normal radiation at two sites in Germany and the USA. The small figure on top shows a time series of annual sums of global horizontal radiation in Potsdam from 1937 to 2000. The lower graph shows the maximum deviations of moving averages from 1 to 15 years compared to the long term average of all years in the data sets. It can be seen that at least 10 years of observations are necessary to stay within the limit of å±5% of the long term average. This means on the other side, taking only one or two years they may be far off the long term expectations and may lead to wrong conclusions on economics and design of the solar energy system. This has nothing to do with the uncertainty of measurements or models, this is just natural variability. This curve shows that if a project is based on a short term measurement of only a year of two, the estimation of the resource may be far away from what can be expected at this site in the long term.
Figure 4 shows the spatial variability of solar radiation in Spain. The left figure is a five year average of direct normal irradiation. The right figures show the annual differences in each year. The patterns are quite different each year and the deviation changes over short distance. This means if one knows the deviation of data for the current year to a long term average on one site, one cannot transfer this result to the next site. Resource assessments have to be site specific.
These two examples show two important features of a good resource assessment: it needs to be based on long term data (at least 10 years) and must have a high spatial resolution of a few kilometres. Satellite based resource assessments can provide both: satellite raw data is archived for many years and data from meteorological satellites in geostationary orbits have a very high spatial resolution.
Access to solar resource data
Creating high quality resource data is the first task in creating a database for the successful deployment of solar energy. Making it available to the users is the second task. Europe has several examples on how the data can be brought to its users–two recent examples are PVGIS and the SoDa/MESoR portals.
PVGIS is a web service for global radiation in Europe and Africa. It brings an easy to use Google maps type interface. Figure 5 shows a sample of this interface. The user can select a site of interest by moving through the maps. On the right hand side, (s)he can select which solar radiation quantities (s)he is interested in. PV-GIS also includes simple performance models for the estimation of PV-yields. Users wanting to install PV systems can very quickly get a first estimate of what they can expect from a PV system at their site of interest.
The SoDa and its successor prototype, the new mesor.net portal, use a different approach in gaining access to solar resource data. They act as an agent that connects different sources and applications through the internet. The data source and applications are hosted by their providers. The portals connect different sources and models to derive user specific results. A user can e.g. select one source of data and a performance model of another provider to calculate the expected yield of a system. Figure 6 shows a sample of the MESoR portal. Data sources can be selected from a menu. In this way the user has the same look and feel in accessing the data for different sources. The window on the right shows a sample of an extracted time series. The connected sources use standard internet protocols; therefore an automated access to the data source from software is possible.
High quality resource assessments are the base for successful deployment of renewable energy sources. Their availability will lower the investors’ uncertainty and risk about the availability of solar radiation. Investors would then be able to calculate lower surcharges and make their investment cheaper or optimize their systems better on the available resource and increase their revenues. Policymakers designing market introduction policies can better assess the level of necessary support in the beginning of market introduction. With this they can optimize the support schemes by fine-tuning the necessary amount of financial support, or by adding support for more systems and therefore speeding up the market introduction of these technologies.
Hammer, A., Heinemann, D., Hoyer, C., Kuhlemann, R., Lorenz, E., MÌ_ller, R., Beyer, H.G., 2003. Solar energy assessment using remote sensing technologies, Remote Sensing of Environment 86, 423-432.
Rigollier, C., LefÌ¬vre M., Wald L. 2004. The method Heliosat-2 for deriving shortwave solar radiation data from satellite images. Solar Energy, 77(2), 159-169.
Schillings, C., Mannstein, H., Meyer, R., 2004. Operational method for deriving high resolution direct normal irradiance from satellite data. Solar Energy 76, 475-484.
Trieb, F., MÌ_ller-Steinhagen, H., 2007. Europe ÛÒ Middle East ÛÒ North Africa cooperation for sustainable electricity and water. Sustainability Science, 2, 205-219.
The MESoR project: http://www.mesor.net