PV Performance Modeling

Transcription

1 PV Performance Modeling Joshua Stein (Sandia National Laboratories, USA)

2 Background Solar fuel is free most cost of PV is spent building plant prior to any revenue being generated. Accurate PV performance modeling is critical for lowering financing costs and perceived risks. PV performance modeling helps to answer three questions: How much energy will a specific PV system produce? Which PV technology should I choose for my project? Is my plant performing as expected given the measured irradiance and weather conditions?

3 PV Performance Modeling Situation 2010 Sandia National Laboratories held a workshop on modeling: Lesson learned: Greater consistency and transparency is needed

4 PV Performance Modeling Collaborative 2010 Workshop conclusions: Models do not agree (lots of inputs) (uncertainty ignored) Models are quite different (PVWatts to PVsyst) and are not well documented ( Black Boxes ) Few standards or best practices are available Non standard data sources (module and inverter databases) PVPMC started to address these factors by: Providing detailed and accessible information about PV models and algorithms Providing open source tools for learning about and validating modeling applications Hosting workshops, tutorials, and other events to promote best practices

5 pvpmc.sandia.gov Website ~200 webpages Model and algorithm descriptions Past workshop presentations Download links for tools PV research projects from users

6 pvpmc.sandia.gov Website Modeling Steps Webpage Categories

7 PVLIB Toolboxes Freely available in Matlab and Python versions High level functional programming language to model PV components and systems. Weather data importing Irradiance translations, transpositions, Sun position, atmospheric functions Reflection, module and cell temperatures IV models, including parameter estimation methods Inverter models ~50 functions with extensive documentation and examples User community (GitHub, meetings, webinars, etc.) Over 1,000 downloads in last year User contributions are encouraged

8 Workshops 2010 Albuquerque, NM USA 2013 Santa Clara, CA USA 2014 Santa Clara, CA USA 2015 Cologne, Germany (TÜV Rheinland) May 9-10, 2016 Santa Clara, CA October 24-25, 2016 Freiburg, Germany (ISE) March 30, 2017 Lugano, Switzerland

9 2015 PV Performance Modeling Workshop TÜV Rheinland, Cologne, Germany, October, Number of attendees: 220 from over 30 countries Sessions Solar Resource and Uncertainty Effects of spectrum on PV performance Soiling Bifacial PV Modeling tools Monitoring PV

10 PV Performance Modeling Steps 10

11 Workshop Highlights Solar Resource Karel De Brabandere (3e): Validation and comparison of four satellite irradiance products across Europe show that these datasets are becoming more accurate and that differences between different products are becoming smaller. Marcel Suri (GeoModel): Presented detailed uncertainty analysis of GeoModel product. Satellite Irradiance Comparison GeoModel Uncertainties

12 Workshop Highlights A number of new spectral irradiance datasets are being developed from satellite data sources. The general availability of such data has promise to reduce uncertainty in PV performance modeling since spectral mismatch is one of the major sources of uncertainty in current tools. Annette Hammer

13 Workshop Highlights Two new spectral mismatch models (Lee ; Duck) were introduced at the workshop that utilize readily available meteorological data such as precipitable water, clearness index, and air mass to better estimate the spectral mismatch in PV performance. Mitchell Lee (First Solar) proposed a surface fit to for spectral shift, air mass, and preciptable water. PVsyst is implementing a new spectral mismatch model based on results from workshop Mitchell Lee

14 Workshop Highlights Soiling from dust and snow continue to be major causes of energy loss for PV systems, especially in certain regions. Estimating these losses in detail in performance predictions continues to be a challenge. Results from field studies were reviewed (Werner Hermann, TÜV) Composition and angular effect of dust was reported (Bruce King, Sandia) Werner Hermann Bruce King

15 Workshop Highlights Modelling and field data for bifacial PV modules and systems were presented. Significant performance gains are available from bifacial technologies (J. Castillo, Prism Solar) ; however current modeling tools are unable to accurately predict energy production from bifacial modules. Uncertainty in how performance results from single modules and small systems will scale to larger arrays (C.Reise, Fraunhofer ISE) Sun position influences bifacial gains (shadows move) (G. Corbellini, SUPSI) J. Castillo G. Corbellini

16 Workshop Highlights Field monitoring of PV systems is still in need of standardized methods to ensure high quality data is collected. Several experts in this area presented examples of good practice, but these examples are not typical of system monitoring in general. Best in Class services should include: rapid identification and classification of performance problems with recommended solution. Measuring performance is no longer sufficient. Monitoring systems in the near future will include advanced analytical capabilities that help operators manage PV assets efficiently.

17 Summary and Conclusions PV performance modeling benefits greatly from transparency. PVPMC aims to enhance transparency through education, documentation, outreach, and open source tools. Recent workshop highlights Satellite irradiance improvements Spectral irradiance datasets and models being developed Soiling studies improve our understanding of this important loss mechanism Bifacial PV modeling opportunities PV Monitoring technologies and methods are poised to offer significant new capabilities Future workshops being planned in US and Europe. Contribute and Participate in the PVPMC!