Case Study PV energy yield in Paraguay - an illustrative assessment

Transcription

1 Case Study_PV energy yield in Paraguay an illustrative assesment_v1_ Case Study Image source: Google Earth Contact Solar Engineering Decker & Mack GmbH Johannssenstr. 2-3 / D Hanover Tel.: +49 (0) PV Modelling & Assessment ertrag@solar-engineering.de

2 Content (1) Introduction & Calculation of Energy Yield and Overall Uncertainty Page 3 (2) Locations & Data sources Page 5 (3) Results Part A: Expected average energy output Page 7 (4) Results Part B: Uncertainty & Variability Page 8 (5) Results Part C: Probability of Exceedance Page 9 (6) Final remarks Page 10 Page 2

3 Introduction For the financing of utility-scale PV projects, usually external finance is required, which typically asks for independent expertise or professional PV energy yield assessments, respectively. Among other things, such an expert study has to provide the following: Expected average energy output of the PV system Overall uncertainty of the expected energy yield In order to provide these information, first the site-specific climatic conditions (energy meteorology) have to be assessed. Then, the results of this assessment together with the project-specific properties of the PV Power Station under consideration are used to calculate the required data applying a professional PV simulation program. The present case study presents potential results for a typical utility-scale PV project in Paraguay, South America, received by a professional PV energy yield assessment. Calculation of Energy Yield and Overall Uncertainty The calculation of the energy yield of a PV system starts with the assessment of reliable, sitespecific climatic data. There, most relevant as it determines the energy output is the irradiation, which can be obtained from several data banks. Unfortunately, these sources usually feature information, which vary of origin, measurement / derivation method or averaging period. Due to this, the selection of a reliable data set requires a detailed and critical assessment of the available irradiation data taking into account metrological / methodical uncertainty and long-term changes ( global dimming / brightening (see, for instance, [1]). Similar facts apply for the remaining meteorological parameters required for the calculation of the energy yield. Besides the identification of a reliable data set, a comprehensive study of the available climatic data leads to estimates of the uncertainty associated with the meteorological parameters. There, the standard deviation found on basis of the different data sets may be considered as the resulting uncertainty. For the assessment of reliable site-specific climatic data, Solar Engineering Decker & Mack GmbH established the Climate Data Study approach, which takes into account all the before mentioned issues in a systematic manner. Once a reliable, site-specific climatic data set has been found, the average expected energy output of the PV system can be calculated on this basis and by applying a PV simulation program. There, losses associated with the layout, the components and so on are determined, which arise between the incident irradiation on the PV modules / PV generator and the grid connection or metering point. Furthermore, aging degradation of the PV modules during the period under consideration is accounted for. Further losses, for instance, for soiling of the PV modules may be included, too. [1] Wild, M.: Global dimming and brightening: A review; JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, D00D16, doi: /2008jd011470, 2009 Page 3

4 PV simu. Energy meteorology The uncertainty of the PV simulation is usually assessed for each individual loss regime. Applying Gaussian error propagation then leads to the corresponding total uncertainty. For the calculation of the energy yield Solar Engineering Decker & Mack GmbH uses it`s ad hoc developed PV simulation program PR-FACT [2]. In it`s latest version, this tool offers a method to calculate the project-specific uncertainty temporally resolved accounting for layout-, component- and method-related variations (such as production tolerances). The overall uncertainty required in the context of PV energy yield assessments usually consists of the uncertainty associated with the long-term energy meteorology as well as the PV simulation and the uncertainty resulting from the variability of the weather in the years to come. Typical values for each effect are presented below. Origin Uncertainty ( energy yield assessment ) Global horizontal irradiation G hor (methodical / metrological) Global horizontal irradiation G hor (long-term variation) Global plane-of-array irradiation G k (translation G hor G k ) Typical range Variability ( operation of PV power plant) ±2 to ±10 % Meteorology G hor, D hor, T amb, FF ("inner-annual") ±0 to ±5 % Global horizontal irradiation G hor ("year-to-year") ±1 to ±2 % Meteorology RR - Soiling ("inner-annual") Typical range ±0 to ±1 % ±2 to ±8 % ±0 to ±1 % Meteorology RR - Snow coverage ("year-to-year") ±0 to ±5 % Energy yield calculation (initial value) ±3 to ±6 % Annual degradation ±0 to ±1 % Uncertainty (total) ±4 to ±13 % Variability (total) ±2 to ±10 % "OVERALL UNCERTAINTY" ±4 to ±16 % Table I: Uncertainty & variability (Note: total and overall values calculated applying Gauss law of error propag.) At present, typically only the effects written in normal typing are taken into account in PV energy yield assessments. Furthermore, the overall uncertainty is usually assumed as normal distributed. On basis of the overall uncertainty, the probability of exceedance (the level of confidence that a PV Power Plant`s actual energy output will be at least a certain values) is typically also provided with PV energy yield assessments. [2] General Description of PR-FACT - A calculation tool to determine the Performance Ratio of a PV system - Page 4

5 Locations In order to present mean values most representative for the climatic conditions of Paraguay, South America, in total six locations spread over the entire country are selected. The geographical coordinates as well as the climate zone classification are presented below. Latitude [ ] (North positive) Longitude [ ] (East positive) Altitude [m] (above sea level) Climate zone (Troll and Paffen [3]) Nueva Asuncion Mariscal Estigarribi Concepcion Asuncion Ciudad del Este Encarnacion -20,720-22,017-23,433-25,250-25,450-27,317-61,920-60,600-57,433-57,517-54,600-55, V, 4 V, 3 V, 2 V, 1 V, 1 V, 1 Table II: Locations considered for the determination of mean values The locations presented above have been selected on the basis of available, ground-measured data as indicated by METEONORM 6.1 [4]. Besides the potential availability of temporal resolved meteorological data especially irradiation for further analysis, the presence of a weather station may indicate the importance of these places in the Paraguayan territory, the density of population or the energy demand, respectively. Data sources For Paraguay, South America or the locations presented above, in total five climatic or meteorological data bases are available or have been considered here. The most relevant details of these sources are displayed overleaf. All data sources taken into account provide global horizontal irradiation data on a monthly base. Unfortunately, all but one on averaged basis only, i.e. one average value for each month of the year. In consequence this means that the site-specific variability is assessed in the following accounting for the information available from one data source only. The ratio of diffuse to global irradiation (D/G) is available in four of five data bases. Here, ambient temperature and precipitation in two of five, however, information from close-by meteorological stations, which are taken into consideration by default within the Climate Data Study, are accounted for, too. [3] Troll, C.; Paffen, K.: Jahreszeitenklimate der Erde (Reduced scale reproduction of wall chart 1: ); Berlin 1969; 1980 [4] METEOTEST; Department meteonorm; Fabrikstrasse 14; 3012 Bern; Switzerland; Page 5

6 Basis Averaging period Spatial resolution METEONORM 6.1 Measurement at weather stations depends on data available at given weather station Weather station specific value NASA SSE 3TIER NREL CSR INPE LABSOLAR Data derived from satellite obs. Data derived from satellite obs. Data derived from satellite obs. Data derived from satellite obs app. 100 km 100 km app. 3 km 3 km app. 40 km 40 km app. 40 km 40 km Reference [4] [5] [6] [7] [8] Table III: Data sources considered The data banks considered here include typically available and used sources for the area of interest and with regard to professional PV energy yield assessments. Further information may be available. For PV energy yield assessments, Solar Engineering prepares by default a Climate Data Study, i.e. a detailed and critical analysis of available data for the site under consideration including information from close-by weather stations. Aim of such a study is to determine a climatic data set suitable for the calculation of expected energy output of the given PV system. There, as mentioned before, the most relevant parameter is the site-specific average irradiation, which is selected from the information available applying a cross-comparison approach (c.f. [9]) taking into account longterm changes, i.e. global dimming and brightening. Following the approaches implemented in Solar Engineering`s Climate Data Study, for each Paraguayan location under consideration a data set is prepared, which then respectively consists of suitable values on global horizontal irradiation G hor, diffuse-to-global horizontal irradiation ratio D/G, ambient temperature T amb and precipitation RR. [5] NASA: Surface meteorology and Solar Energy (SSE) Release 6.0; [6] 3TIER; [7] [8] Solar and Wind Energy Resouce Assessment (SWERA) A United Nations Environment Programme facilitated effort; [9] Egler, M.: Global irradiation in north-western South America - A comparison of long-term datasets of average annual irradiation taken from five different data sources; 28th European Photovoltaic Solar Energy Conference and Exhibition; Parc des Expositions Paris Nord Villepinte, Paris, France; September 30th, 2013 to October 04th, 2013 Page 6

7 Results Part A: Expected average energy output On basis of the methods typically applied in Solar Engineering s PV energy yield assessments, i.e. among others the outcome of the Climate Data Study and the PV simulation program PR-FACT, the following values result: Global horizontal irradiation G hor [kwh/(m²*a)] Ratio Diffuse to Global irradiation D/G [-] Global plane-ofarray irradiation G k [kwh/(m²*a)]* Performance Ratio PR [-]** Nueva Asuncion Mariscal Estigarribi Concepcion Asuncion Ciudad del Este Encarnacion 1,798 1,790 1,827 1,790 1,847 1, ,874 1,873 1,943 1,913 1,989 1, Specific electric energy yield y ac [kwh/kwp] 1,513 1,519 1,583 1,568 1,631 1,616 Table IV: Selected global horizontal irradiation and ratio of diffuse-to-global irradiation as well as resulting global plane-of-array irradiation, Performance Ratio and specific electric energy yield *Note: The PV module inclination / tilt and orientation chosen are 20 and 180 (North) for Nueva Asuncion, Mariscal Estigarribi and Concepcion as well as 25 and 180 (North) for Asuncion, Ciudad del Este and Encarnacion. **Note: The Performance Ratio determined / presented does not include effects due to aging degradation of the PV modules / PV system, effects due to soiling of the PV modules and reduced availability of the PV system. Page 7

8 Results Part B: Uncertainty & Variability I. Uncertainty - Global horizontal irradiation G hor on basis of long-term data for and from the before presented six locations and data sources Annual values of SD: MEAN: ±6.7% MIN: ±5.1% MAX: ±7.9% II. Variability - Global horizontal irradiation G hor on basis of long-term data for and from the before presented six locations and data sources Annual values of SD: MEAN: ±3.0% MIN: ±2.7% MAX: ±3.5% III. Uncertainty Performance Ratio PR on basis of the PR-FACT uncertainty calculation for the before presented six locations Annual values of uncertainty: MEAN: ±5.1% MIN: ±5.5% MAX: ±4.7% Page 8

9 PV simu. Energy meteorology Results Part C: Probability of Exceedance On the basis of the before presented uncertainty & variability associated with the global horizontal irradiation G hor and the Performance Ratio PR, a (mean) overall uncertainty for the Paraguayan territory of 9.2% can be determined. Origin Uncertainty ( energy yield assessment ) Global horizontal irradiation G hor (methodical / metrological) Global horizontal irradiation G hor (long-term variation) Global plane-of-array irradiation G k (translation G hor G k ) - estimate - Typical range Variability ( operation of PV power plant) ±6.7% Meteorology G hor, D hor, T amb, FF ("inner-annual") incl. above Global horizontal irradiation G hor ("year-to-year") ±2.0% Meteorology RR - Soiling ("inner-annual") Typical range - ±3.0% Meteorology RR - Snow coverage ("year-to-year") - Energy yield calculation (initial value) ±5.1% Annual degradation - Uncertainty (total) ±8.7% Variability (total) ±3.0% - "OVERALL UNCERTAINTY" ±9.2% Table V: Mean uncertainty & variability as well as overall uncertainty for the Paraguayan territory Again, please note that the before presented figure(s) do not include all potential effects, especially regarding variability. Seen the fact that data sources with different averaging periods are accounted for, the uncertainty related to the global horizontal irradiation G hor may be considered including both relevant effects (methodical / metrological deviation and long-term variation). Probability of Exceedance on basis of above presented overall uncertainty (and the corresponding results for MIN / MAX) P-values: P50 long-term average P75: 93.8% (app. ±0.9%) P90: 88.2% (app. ±1.7%) P95: 84.9% (app. ±2.2%) Page 9

10 Final remarks Among other things, irradiation data from five different sources have been compared in the present case study. There, deviations of the long-term, annual global horizontal irradiation G hor have been found, which are above the results observed for Europe (c.f. [10]). With regard to the uncertainty associated with the PV simulation or Performance Ratio, respectively, the values received here correspond with the upper end of the results found for locations in Brazil [11]. The mean overall uncertainty determined in the present case study illustratively calculated from the mean uncertainty and variability obtained from the corresponding single values for six locations in Paraguay leads to P-values approximately at the upper end of the ones usually found for utility-scale PV projects in Germany / Europe. The results presented here are solely indicative, i.e. they give a first impression on what to expect when considering entering the Paraguayan PV market. In order to emphasize this, the probability of exceedance calculation has been extended accounting for the maximum and minimum uncertainty and variability found when evaluating the information available for six locations in Paraguay, leading to a variation affecting, for instance, the P90 value with ±1.7%. [10] Šúri, M.; Remund, J.; Cebecauer, T.; Dumortier, D.; Wald, L.; Huld, T.; Blanc, P.: First Steps in the Cross-Comparison of Solar Resource Spatial Products in Europe; Proceeding of the EUROSUN 2008, 1st International Conference on Solar Heating, Cooling and Buildings; Lisbon, Portugal; October 7th to October 10th, 2008 [11] Egler, M.: Bewertung von PV-Projekten in Brasilien standortspezifische Unsicherheit und Variabilität der Energiemeteorologie und PV-Simulation ; 29. Symposiums Photovoltaische Solarenergie; Kloster Banz, Bad Staffelstein; 12. bis 14. März 2014 Solar Engineering s PV Modelling & Assessment department offers several services focusing on reliable electric energy output figures for PV project worldwide. Besides the things presented here, this also includes qualified comparisons of potential PV components, such as PV modules or inverter, and PV systems, e.g. fixed vs. tracking installation. Our experts are looking forward to support you! For contact and further questions: ertrag@solar-engineering.de Solar Engineering Decker & Mack GmbH Johannssenstr Hanover / Germany Page 10