Recommendations for Bankable Meteorological Site Assessments for Solar Thermal Power Plants

Transcription

1 Recommendations for Bankable Meteorological Site Assessments for Solar Thermal Power Plants Richard Meyer 1 1 Suntrace GmbH, Brandstwiete 46, Hamburg (Germany) Phone: , richard.meyer@suntrace.de Abstract Financing solar thermal power plants shall be based on thorough analysis of local conditions at the site. Meteorological conditions - in the first place knowledge of beam irradiation - have dominant influence on potential power output of a plant. Nowadays there are no standards for assessments to support due diligence of solar thermal power plants. This paper gives recommendations for proper preparation of expert opinions on meteorological site assessments for such investments. The main points are: A long-term record of direct normal data is required for well representing the climatological average. A minimum of 10 years of data shall be taken, but more years help to reduce the uncertainty related to the high inter-annual variability of beam irradiation. For most projects measurements only cover a few years. Therefore, site-specific satellite-derived irradiance values are required to obtain long-term history. Today, such data still does not have sufficient quality to be used alone. Their site-specific quality must be proven by reliable measurements. Those measurements allow enhancing time resolution into the minute range. Measurements should have an overlap of at least one year with satellite data. To allow more realistic performance simulations it is recommended to measure also humidity, air temperature, wind speed and direction. With statistical methods the experience from the short but highly resolved measurements shall be transferred to the long time-series derived from satellites. For proper risk analysis, it is recommended to use not only a single characteristic meteorological year but simulate a multitude of years to quantify the variability of the power output. Keywords: Direct normal irradiance (DNI), auxiliary meteorological parameters, performance simulation, financing concentrating solar power plants (CSP), due diligence. 1. Introduction Solar thermal power plants due to their utility scale capacity require large investment volumes. Money for debt and equity as well as costs for plant operation and maintenance (O&M) must be gained by the yields produced by the plant during its amortization period. Energy yields are closely related to the direct normal irradiance (DNI) at the plant site. For a reliable estimate of potential revenues, various aspects of DNI availability are need to be evaluated thoroughly for due diligence (DD) of concentrating solar power (CSP) plants. As auxiliary meteorological parameters (e.g. air temperature, humidity, wind speed and direction) influence energy yields, these parameters also need to be considered in such expert opinions. Such data shall be coincident with DNI and is required as input for reliable performance simulation of a CSP plant. NREL [1] is now providing a handbook for collecting and using various solar resource data, which gives a good overview of DNI measurements on the ground, satellite-derived solar radiation products and covers methods for fusion of both worlds. The combination with auxiliary meteorological data remains open. The

2 method [2] to derive Typical Meteorological Years (TMY) requires at least 15 years of measurements, which are rarely available for CSP sites. Another shortcoming is that usually only hourly time-series are provided, but [3] shows that variability of DNI is often much higher. [4] points out that hourly resolution of meteorological time-series is not sufficient to calculate properly transient effects of CSP plants, which have significant effect on energy yields. According to [5] the year to year variability of DNI is around a factor of 3 higher than that of global irradiance. Thus, following [6] the time period required to get the same accuracy of the long-term average for CSP is almost a factor 10 higher than for PV. [7] and [8] propose methods how to take advantage of short term but high resolution measurements to scarify the hourly satellite data and remove bias for the specific location. An open question to be addressed is the volatility and uncertainty of the meteorological data and its impact on yields and how this should be dealt with in risk analysis. This paper reviews methods for deriving DNI from ground-based measurements and satellites. It gives practical advice for selection and fusion of such data sources, in order to generate gap-free data sets of DNI and auxiliary meteorological parameters in high time resolution, which are suitable as input to performance simulations. Quality issues and uncertainty of the resulting data sets are discussed to provide input for financial risk analysis. Finally, it points out which additional work is recommended to further improve the quality of such assessments in the future. 2. Review of methods for assessing DNI Site-specific beam irradiance either may be measured at the ground or can be derived from radiation transfer models. To model DNI, the actual concentration of atmospheric constituents and their particle size and form along the path from the Sun to the site needs to be known. Numerical weather prediction (NWP) models, as those used for forecasting, or climatological studies represent the full path through the atmosphere and in principle could do the job. However, to keep the computational effort feasible they need to apply fast, nonexact radiative transfer code, have a reduced set of atmospheric constituents and must reduce spatial resolution, so that the results are not very site-specific. Aerosols today often are not considered in NWP models and clouds - the most important atmospheric constituent for the radiation transfer are not well represented especially in respect to DNI. But clouds can be well retrieved from satellite observations in relatively high spatial and temporal resolution, suitable for site specific analysis of DNI Satellite-derived irradiance data Meanwhile, several data providers are providing DNI-maps and DNI-time-series data. Spatial and temporal resolution of such data sets varies strongly. As DNI is much more sensitive to actual conditions of the atmosphere than global irradiance, it is much more difficult to achieve acceptable data quality. Worldwide DNI-data sets such as NASA-SSE or DLR-ISIS have poor spatial resolution of 1 x 1 or 280 km x 280 km. As there is at most places strong variability within such wide boxes, DNI usually is not representative for the site to be assessed. An advantage is that they cover more than 20 years. This allows the calculation of inter-annual variability, which differs from region to region. The lower the variability the fewer the years needed to be averaged to achieve low uncertainty. Thus, acc. to [6] the annual average of each single year may be used to estimate the uncertainty from taking only a limited number of years into account.

3 For DNI-data, which is representative for a CSP-project site, it is recommended to use data which have a spatial resolution of 10 km or better. Where strong micro-meteorological effects are appearing, such as proximity to mountains or shore-lines, even higher resolution down to the 1 km scale can be recommended. Several data suppliers offer now DNI-data in 3 km or even 1 km resolution. But for deriving real 1 km data sets it is required to have all relevant underlying data sets available in 0.5 km in order to fulfill the sampling theorem. Today this requirement can only be realized for the underlying digital elevation models, which are needed to calculate the elevation-dependent atmospheric attenuation and shadowing by surrounding topography. All other input parameters have much worse resolution. E.g. aerosol optical depth often is derived from global models with resolutions of 100 km or wider. Fortunately clouds, which usually hinder DNI strongest today typically are derived by the full resolution of geostationary satellites. For the GOES and Meteosat Second Generation (MSG) satellites the resolution of the high resolution channel in the visible spectrum reaches 1 km at nadir position. But towards the satellite view s horizon the resolution diminishes and the high resolution channel is not available for all sites. More often, the lower resolution channels with 3 to 5 km nadir resolution are applied. Also, channels of weather satellites usually have a smooth point spread function. Thus, to prevent aliasing effects the true resolution is around 25 % higher than the nominal. The combined effect of instrument response smoothing and viewing geometry for 1 km nominal pixel sizes leads to true spatial resolutions of the satellite pixels in the range of 2 km to 3 km or around 5 to 10 km for the coarser 3 km resolution, which is still mostly applied. On top of these dilution effects, pixel navigation errors appear, which typically lead to a random jitter of around 1.5 pixels. This can explain some of the mismatches between instant satellite-derived and ground-measured DNI. Luckily on average the navigation errors are not systematic and usually average out already for daily averages. Most methods to derive DNI from satellite data ignore the parallax effect. As clouds usually do not touch the ground, they have significant vertical extent and often appear in several layers of different altitude, the cloud shadow is mismapped under most viewing geometries. As cloud bottom and cloud top height are hard to derive, this mapping error remains uncorrected. Depending on the sun and satellite position this mismatch often is in the 10 km range or above. Mainly due to the parallax effects, most DNI-time-series show lower root mean square deviations (RMSD) when the high resolution results are averaged to around 10 km. To date, only focus solar, University of Oldenburg and 3TIER [9] are known for developing methods, which try to correct the parallax effect. Cloud top height could be derived from the infrared channels and cloud bottom from adiabatic lapse rate, which may be inferred from NWP. Multi-layer cloud situations are not resolved, but it is expected that statistically the impact on DNI is low. To date, DNI-time-series products in higher resolution may be obtained from various providers typically in 60 min time resolution, e.g. from 3TIER, IrSOLaV, Meteotest, NREL/SUNY or Satel-Light. Higher timeresolution in the range of few minutes would be needed to account for non-linear effects reducing the yield of CSP plants. Organizations applying MSG like DLR, focus solar, geomodel, SoDa/HelioClim, or University of Oldenburg may deliver 15 min time-resolved data. As validation is missing in this scale it remains open whether the RMSD will be reduced or enhanced due to the missing average effect of hourly values. Satellite-derived DNI-products still differ strongly from each other [10], but a clear reference for benchmarking is missing. Most data providers attach grey papers to indicate validation of their products. An inter-

4 comparison of the various data sets remains difficult for the user, because the validations are done with varying spatial and temporal averaging. A first independent benchmarking with consistent methodology including high resolution DNI products was executed by [11]. The drawbacks here are: most measurement sites therein are situated in high latitude regions, where it is more difficult to gain proper DNI from satellite and are not representative for good CSP spots; and in many cases the ground-measurements used for validation were also used to develop and tune the satellite procedure. Due to this data incest situation, today validation of DNI data are of limited value. Currently a new activity is started within SolarPACES to execute an independent benchmarking of satellite-derived DNI data. The goal is to apply truly independent measurements, which may come from CSP project developers to allow a fair inter-comparison. It is recommended to use two independent satellite data sets for DD. It is assumed that a combination of independent overlapping time-series further reduces the uncertainty of average DNI at the site. To be truly independent, the satellite-derived irradiances should be based on separate satellite instruments, on individual satellite platforms with differing viewing angles, independent auxiliary data like water vapor, aerosol, or ozone and best also be retrieved from independent algorithms. Several providers for site-specific DNI data exist today, but for DD, none should be trusted without additional site-specific measurements Measurements at the site Ground-based methods to derive DNI are directly measuring beam irradiance like pyrheliometers or measuring in parallel the total and the diffuse irradiance. This can be executed either by two thermal pyranometers one shaded and one unshaded - or a single instrument like a rotating shadowband pyranometer (RSP). Fig. 1. Shaded and un-shaded pyranometer are mounted together with a pyrheliometer on a tracker with an attached shadow-ball (left). Pyrheliometer and a rotating shadowband radiometer (right). The classic way to gain DNI values is to apply pyrheliometers (Fig. 1). They gather the sun beam within a 5 -cone and always need to be tracked well to the apparent position of the Sun. Through a window the radiation heats a thermopile, which converts it to electricity. First class pyrheliometers according to ISO maintain an absolute accuracy of better than 1 %. However this requires regular calibration by an absolute cavity radiometer, which is traced to the world radiometric reference [12] and daily cleaning [13]. If this kind of instrument is not very frequently cleaned it looses its sensitivity [14]. According to Pape et al. [15] within a period of one month the difference from a cleaned reference instrument can reach 25 %. Therefore such instruments are only recommended when it is possible to clean the measurement station daily.

5 The combination of shaded and unshaded pyranometers with thermal sensors must be realized by two separate instruments because of the high inertia of thermal sensors. Both instruments shall be calibrated following [16]. The shading can be realized either by a shadow-ball, which is mounted on a solar tracker similar (Fig. 1 left), or by a shadow ring, which needs to be adjusted manually to sun height changes in the course of the year. Due to blocking, a relatively large portion of the sky the latter procedure does not provide sufficient quality of the diffuse irradiation and is not recommended. The measurement with the shadow-ball is of higher accuracy, but DNI derived from the difference of diffuse and global irradiance measured with two separate instruments is still of moderate quality. It is estimated that the absolute accuracy of DNI derived from well calibrated and regularly cleaned shadowball and un-shaded pyranometers can reach 4 %. Application for qualification of CSP plants is only recommended as redundant measurement, when the effort of setting up and operating a pyrheliometer can be realized, which already makes a solar tracker necessary. Due to the large effort in maintenance and high costs for a pyrheliometer station, today many CSP project developers apply rotating shadowband pyranometers for project qualification. Such radiometers make use of the fast response time of semiconductor sensors. A shadowband sweeps over a silicon sensor at least twice every minute. During the brief period of shading the direct sunlight is blocked allowing measurement of the diffuse component. During the rest of the time global horizontal irradiance is measured. The accuracy of DNI data, which can be reached by RSP-instruments depends on proper calibration, synchronous logging of the temperature for correction purposes, thorough operation with good maintenance, and a qualified postprocessing of the readings. According to [14] and [15] RSP instruments show much lower susceptibility to dirt than pyrheliometers. In clean environments monthly cleaning can be sufficient, while in regions, where aerosol load is heavier weekly cleaning can be necessary. An international standard for this type of instruments does not yet exist. From experiences of [6] and [15] it is assumed that for averages of at least one month, it can be expected that the absolute accuracy can be in the range of 2 % to 3 %, when the instrument is properly calibrated, cleaned and all corrections are applied. However individual values during times of untypical aerosol contents can deviate much stronger from this average. 3. Methodologies for gaining auxiliary meteorological parameters Auxiliary meteorological data for CSP are weather-related parameters which have significant influence on design and operation of solar thermal power plants. Essential are the following: dry air temperature for plants with dry cooling, dry air temperature and humidity to calculate the wet bulb temperature, which is the dominant parameter for efficiency of plants with evaporation cooling systems, wind speed and direction, which have influence on solar field layout, collector design, optical efficiency due to distortion of collectors or loss of earnings by cut off at high wind speeds. To gain representative data such measurements should be recorded in standard heights. For wind, it is strongly recommended to measure at the standard height of 10 m above ground. Measurements at lower height are disturbed by small scale ground features and therefore hardly representative.

6 Aerosol total column optical depth and aerosol type are nice to have additional parameters, which could be derived by sky radiometers or sun photometers. This would have the advantage to derive also the circumsolar ratio, which is of importance especially for highly concentrating systems such as power tower, which can harvest only the inner circle of the 5 -cone used for DNI measurements. For power tower systems the path from the mirrors to the absorber -- at least for more distant heliostats -- is much longer as for parabolic troughs, linear Fresnel or dish-sterling systems. As the path through the atmosphere at the Earth surface usually is optically densest, significant amounts of solar irradiance can be lost. To estimate this effect it is strongly recommended to install visibility sensors for qualification of a power tower. Installing a rain gauge would allow to better understand hydrology of the site. However, as rain is a rare event for typical CSP sites, varies strongly spatially and shows high inter-annual variability a single shortterm measurement is only of limited use. If hydrology and available water for cooling is an open issue, operation of at least three rain gauges around the plant site could be an option to gain better knowledge. During project development, it might not always be possible to install a station directly on the site. To have an off-site place for the station has the advantage that the measurements can be continued without interruption or influence of construction work on the readings. Important is to select a place for the station, which still represents well the site. Depending on micro-meteorology, some distance to the project site could be acceptable. In flat terrain without height differences of less than 50 m and homogenous land use it could be that a measurement station in 20 km distance still well represents the situation at the project site. In such a case however satellite data should be acquired for both for the project site as well as for the measurement station. In hilly terrain, less than 5 km is strongly recommended. In some cases with strong orographic effects 3 or more stations would be of benefit, to analyze micro-meteorological effects, especially for large CSPsites, which may extend more than 5 km 2. If auxiliary data is not measured at all, model output data may be taken. If there are measurements from other stations in the vicinity at places with similar climatic conditions, these may be interpolated by geostatistical methods like kriging. Such interpolation shall take into account elevation differences like the Meteonorm software [17] does. It is up to the expert to judge for its representativity depending on quality of the underlying data and the distance to the next stations. 4. Traceability of meteorological site assessments High accuracy and reliability of input data and results is the overall goal of such assessments and required for project financing. This implies high quality of instruments and satellite data, but also reliable documentation and meta-data to proof accuracy. It is recommended to have a report on the station set up and instrumentation including calibration protocols, etc. operation of the station shall be documented regularly in a station log book by the local station keeper. The expert responsible for the station needs to download and archive the data regularly in raw format. The received data shall be processed frequently and analyzed best daily - with automatic quality check (QC) algorithms. Additional visual inspection is recommended to identify undetected errors. Satellite products shall come along with proper documentation including validation - best in the region of interest. If acknowledged validation is not available, this needs to be part of the assessment. In this

7 case, reference to satellite and measurement data of other nearby measurements are the best solution. Proper documentation, QC and proven processes are important to avoid serious errors in the analysis. In case accuracy is insufficient, this could lead to delay of project development or worsen financing terms. For reliable and effective project qualification, well established processes for deriving meteorological parameters speed up analysis and lead to more accurate estimation of yields and thus support financing of a project. 4. Conclusions The paper reviews the current state of the art for analyzing meteorological parameters in view of the needs of CSP plants. Based on practical experience and analysis of various data products and methods, it proposes best practices to achieve high quality assessments with reasonable effort. Following [18] and the advices given in this paper the recommended procedure is the following: 1. Set up a suitable measurement station at the site as soon as possible during project development. 2. Determine the long-term best estimate by as many years as possible. For DNI take at least 10 years, because inter-annual variability is much higher. Usually this will be supplied by satellite data. 3. Search for several site-specific data sets from satellites and ground to get independent information, ideally with overlapping time-periods and combine the data sets. Use only reliable data sets - carefully check each individual satellite-derived but also ground-based data sets. Eliminate data which are erroneous or in doubt. 4. Estimate the uncertainty of the remaining data -- best individually for each data record. 5. If known, remove the bias of each individual data set. 6. Calculate a quality-weighted best average for each time step. 7. Calculate the resulting uncertainty by Gaussian error propagation. In case the uncertainty of the combined value is higher than the lowest individual value, remove the data source with the highest uncertainty and recalculate. Of course, the satellites are needed for the long-term history and need to be adapted during the overlapping time-period. Problems are that satellite-derived DNI values often are not very site-specific. At least around 10 km by 10 km spatial resolution are recommended. Their random and systematic error hardly is known and also many measurements are of poor quality. The current state of satellite-derived methods leaves room for improvement. Well inter-comparable benchmarking of satellite-derived DNI-products for sites in CSPregions is an important missing link. Further improvement of measurements seems feasible and clearer definition of processes will lead the way to standardization of the overall task. Currently, often rotating shadowband pyranometers are applied to derive DNI for CSP project qualification. Standardizing calibration and application of such instruments would be of benefit for the industry. Acknowledgements I thank the participants of the IEA Task Solar Resource Knowledge Management and the HGF virtual Institute of Energy Meteorology for many fruitful discussions in the fields covering this topic. Work for this

8 paper is related to lessons learnt in the following projects: UNEP s SWERA (Solar and Wind Energy Resource Assessment), and the BMU-founded projects SKAL-ET, Anda, SOLEMI-VALISAT (validation of satellite derived DNI), SESK (standardization of yield prognosis for solar thermal power plants; grant numbers A, B, C). References [1] T. Stoffel, D. Renné, D. Myers, S. Wilcox, M. Sengupta, R. George, C. Turchi (2010). Concentrating Solar Power Best Practices Handbook for the Collection and Use of Solar Resource Data. NREL-TP [2] S. Wilcox, W. Marion, W. (2008). Users Manual for TMY3 Data Sets. NREL Technical Report NREL/TP Revised May The National Renewable Energy Laboratory, Golden, CO. [3] C. A. Gueymard, S. M. Wilcox: Spatial and Temporal Variability in the Solar Resource: Assessing the Value of Short-Term Measurements at Potential Solar Power Plant Sites. American Solar Energy Society, Solar 2009 Conference, Buffalo, NY, May [4] T. Hirsch, H. Schenk, R. Meyer, N. Schmidt. (2010): Dynamics of oil based parabolic trough plants - Impact of transient behavior. Proceedings of the 2010 SolarPACES Symp., Perpignan, France. [5] S. Lohmann, C. Schillings, B. Mayer, R, Meyer (2006): Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data. Solar Energy, vol. 80, [6] R. Meyer, H. G. Beyer, J. Fanslau, N. Geuder, A. Hammer, T. Hirsch, C. Hoyer-Klick, N. Schmidt, and M. Schwandt: Towards standardization of CSP yield prognosis. Proc. of the SolarPACES 2009 conference, Berlin, Germany (2009). [7] H. G. Beyer, M. Fauter, K. Schumann, H. Schenk, R. Meyer (2010): Synthesis of DNI time-series with sub-hourly time resolution. Proceedings of the 2010 SolarPACES Symp., Perpignan, France. [8] C. Hoyer-Klick, F. Hustig, M. Schwandt, R. Meyer (2009): Characteristic meteorological years from ground and satellite data. Proc. of the 15th SolarPACES Symp., September 15-18, 2009, Berlin, Germany. [9] K. Kaku, W. Potter (2009): Creating high-resolution solar information from satellite imagery and numerical weather prediction modeling. Solar09, 47 th ANZSES Conf.. Townsville, Queensland, Australia. [10] Šúri, M., Remund, J., Cebecauer, T., Hoyer-Klick, C., Dumortier, Huld, T., Stackhouse, Jr., P.W., Ineichen, P. (2009): Comparison of direct normal irradiation maps of Europe. Proc. SolarPACES Symp., Berlin (Germany), Sep , [11] P. Ineichen: Independent global and beam validation on 21 ground data sets. Stakeholder-Meeting of the MESoR project at Intersolar 2009 in Munich. [12] ISO 9059:1990 Solar energy - Calibration of field pyrheliometers by comparison to a reference pyrheliometer. International Organization of Standards. [13] WMO (1998): World Climate Research Programme. Baseline Surface Radiation Network (BSRN). Operations Manual. Version 1.0 edited by B. McArthur WMO/TD [14] N. Geuder, V. Quaschning (2006): Soiling of Irradiation Sensors and Methods for Soiling Correction. Solar Energy. Vol. 80, Issue 11, p , doi: /j.solener [15] B. Pape, J. Batlles, R. Zurita Piñero, N. Geuder, B. Pulvermueller (2009): Assessment of Solar Resources with ground measurements for CSP project development. Proc. of the 15 th SolarPACES Symp., September 15-18, 2009, Berlin, Germany [16] ISO 9846:1993 Solar energy - Calibration of a pyranometer using a pyrheliometer. International Organization of Standards. [17] METEOTEST AG (2010): METEONORM Handbook part II: Theory. Version April 27 th [18] R. Meyer, J. Torres Butron, G. Marquardt, M. Schwandt, N. Geuder, C. Hoyer-Klick, E. Lorenz, A. Hammer, H. G. Beyer (2008): Combining solar irradiance measurements and various satellite derived products to a site-specific best estimate. 14 th SolarPACES Symp., March 2008 Las Vegas, USA