Modeling Energy Losses Due to Snow on PV Systems

Transcription

1 Modeling Energy Losses Due to Snow on PV Systems 4 th PV Performance Modeling and Monitoring Workshop Janine Freeman October 22, 2015 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

2 Why Include a Snow Model? Image Credits: Becker, Gerd, et al. "An approach to the impact of snow on the yield of grid connected PV systems." Bavarian Association for the Promotion of Solar Energy, Munich (2006). 2

3 Snow Loss Model by Marion et al Estimates the amount of snow still covering a row of modules Sliding is the dominant removal process Model requires: System tilt POA Irradiance Ambient temperature Ground snow depth Some module/string layout parameters Image credit: Measured and modeled photovoltaic system energy losses from snow for Colorado and Wisconsin locations, Bill Marion et al, Solar Energy 97 (2013) pg

4 4 Implementation in SAM Also accessible through SDK as part of pvsamv1 or as separate compute module for post-processing

5 Demonstration for Two Systems Annual Error: W/o Snow Model: 9.9% With Snow Model: -1.9% Annual Error: W/o Snow Model: 7.3% With Snow Model: -0.1% Figure 2. Results from the validation study using Forrestal system in Washington, D.C. and the RSF2 system in Golden, Colorado 5

6 Modeled Snow Losses for the U.S. System Advisor Model Figure 4. Results from a national study modeling PV production losses due to snow coverage using both a fixed-tilt tilt-equalslatitude and a tilt equals 20 tilt system design, the NSRDB data set, and the newly implemented snow model in SAM Note: Tabulated results by location available as an appendix to the full report 6

7 7 Snow Loss Estimates for the U.S. Figure 5. General trends in average snow losses as a percentage of annual energy production Note: Like-colored regions have similar loss percentages and are labeled in the figure. The specific region around Nevada and the Four Corners states is special (indicated by an *) in that high altitude regions, such as Flagstaff, Arizona and Ely, Nevada should be considered to be in the next higher tier of snow losses. This plot is a broad enough generalization that it may apply to either a tilt=latitude or a tilt=20 system.

8 8 Snow Loss Estimates By Snow Depth Figure 6. Correlation between the sum of the hourly snow depth array and the resulting percent loss for each year of each location in the tilt-equals-latitude national study

9 Other Considerations/Future Work Model application on AC vs. DC side (demonstrated < 2% difference) Temporal resolution of snow depth data (daily, hourly, subhourly) Snow losses for one-axis tracking systems How to improve model accuracy at shorter timescales? (monthly, hourly) 9

10 Thank you! Questions? Full report:

11 Supplemental Slides

12 Snow Model Flow Chart Begin Go to next time-step Deduct the appropriate amount of energy from the previously calculated energy production according to current coverage Set snow coverage on PV array to 100% SAM calculates expected energy production during current time-step without accounting for snow Was a new snow-fall detected during this time-step? Maintain the previously calculated snow coverage No Decrease snow coverage by the snow sliding amount (calculated from tilt angle) Go to next time-step Do not deduct from previously calculated energy production No Was there a non-zero snow coverage during the previous time-step? Yes Do the current Temp. and POA allow for snow sliding? 12

13 13 PV Array Diagram Figure 1. Simplified diagram of a PV array

14 Modifications to Marion s Model Check to prevent snow coverage from going below 0% If measured snow depth is zero, snow coverage also reset to zero Snow depth threshold: 1 cm Snow delta threshold: 1 cm Extend to accept hourly/sub-hourly snow depth data 14

15 15 Snow Model Applied to 1-Axis Tracking Figure 3. Results from applying the implemented snow model to the Mesa Top one-axis tracking array in Golden, Colorado

16 16 Tabulated Monthly Errors