Leader in Investment, Management and Engineering in the Renewable Energy Industry. Irradiation data in yield predictions Tokyo 24/6/2015

Transcription

1 Leader in Investment, Management and Engineering in the Renewable Energy Industry Irradiation data in yield predictions Tokyo 24/6/2015 1

2 Index of contents 1. Introduction 2. Comparison of Data Sources (Japan) 3. Data bases and operational data 4. Adjusting calculations with real data 2

3 1. Introduction Site weather conditions (ambient temperature and irradiation) are one of the most relevant factors to assess PV projects. Typical Meteorological Year (TMY): the average of several years (usually 25 years) of onsite measurements is used. However, on-site measurements are not usually available and it is required to use meteorological databases: satellite and/or ground based Irradiation considerations Importance of considering the diffuse (scattered) portion. Importance of estimating the irradiation over the plane of array (specific tilt and azimuth) as most databases only provide horizontal surface. For this, transposition models are used (Hay s Model and Perez Model) 3

4 2. Comparison of Data Sources (Japan) Type of Database Terrestrial JMA weather algorithm to obtain irradiation for 836 spots in Japan Terrestial and Satellite For Japan: <30km: Terrestial 30<d<200km: Mix >200km Satellite (MTSAT) Satellite data by NASA SSE (Surface Meteorology and Solar Energy Programme) Satellite Data from Japanese Meteorological satellites Himawari, MTSAT 6&7 Satellite - Data from MTSAT satellites Available Data Period imaps climdata Spatial Resolution Limited to location of station Continuous data (through interpolation) 1ºx1º 5x5 km based on internal 1km mesh 3x3 km (250 m with SRTM-3 Model) When choosing among the available databases the following should be taken into account: The period considered to conform the TMY The proximity of the nearest measured point to the PV plant location or the spatial resolution The data processing or models implemented The uncertainty of the data provided Private & Confidential 4

5 2. Comparison of Data Sources (Japan) NEDO Data locations Vector Cuatro has conducted several comparison studies including: point comparison study of NEDO, Orel and Meteonorm on locations where pyranometer is not available (Baseline: NEDO) Meteonorm Between -7% and +18%, with an average of +5% Important correlation observed between location type and deviation amount. Coastal places: + 2.0%, Landlock flat: + 4.9% Landlock mountain: +11,5% OREL Between -11.3% and 23% with an average of +6% No clear correlation observed point benchmarking study of SolarGIS Comparison between SolarGIS data and real pyranometer measurements SolarGIS, on average +3.8% for same period data (+7% for historical period) Private & Confidential Meteonorm Stations 5

6 3. Benchmarking of data bases with operational data Key issues when comparing operational data with irradiation data provided by databases Period length: databases data (10-30 years) vs on-site measurements (few years) On-site measurement: data gaps, communication problems, measurement equipment problems Plant availability As a result 2-3 years of on-site data cannot be considered representative Vector Cuatro has conducted studies comparing the irradiation data measured in its 950 MW operational portfolio Observations that may be highlighted: Higher differences are observed when comparing irradiation in monthly terms In annual terms, the measured values are within the uncertainty range of the databases considered The higher the number of years considered the lower the difference to the annual yearly values Private & Confidential 6

7 4. The pitfalls of utilizing irradiation data to project yields and revenue Both over- and under-estimating the yield can have a negative impact on the financing conditions and in the project cash-flows. The uncertainty of the irradiation data has a significant impact on the percentile calculation Lenders usually consider the p90/p99 value instead of p50 Other parameters to consider: - Gain and tilted irradiation - Losses due to irradiance level - Thermal losses - Inverter overload losses - Shading losses - Incidence Angle Losses - etc Irradiation uncertainty and percentiles - Higher uncertainty, lower p75, p90, p99 - Lower uncertainty, lower p75, p90, p99 Other parameters that add uncertainty to the assessment are the transposition model, the system losses and the degradation assumptions. The results of the pre-construction assessments help determine important parameters such as the production guarantees of the EPC and O&M contracts. 7

8 5. Project experiences with re-assessing calculations using actual data The yield assessments are usually conducted in a preliminary stage of the development. Thus, some of the losses are based on standard assumptions rather than in the real conditions of the site. When re-assessing the calculations after some years of operation the following can be taken into account: Irradiation and temperature recordings on-site and combination with the long-term databases to obtain an adjusted TMY Testing of the PV modules installed on-site to obtain an adjusted annual degradation Re-assessing the technical assumptions regarding electrical losses based on the detailed as-built characteristics of the Plant Re-assessing the assumption of snow and soiling related losses Study of the actual conditions of the solar PV project in order to verify if it is feasible to extend the expected lifespan Note that re-assessments in practice are done on older facilities as data from young ones is not as representative Module Measurements Estimated vs Actual irradiation analysis 8

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