I have noticed the impact of traversing clouds on solar power output: Immediately after a cloud has passed, power surges to a record value. This can be attributed to the focusing effect of the surrounding clouds and/or cooling of the panels. Comparing data for cloudless days in May and June, I noticed a degradation of power – most likely due to higher ambient temperatures in June.
We had a record-breaking summer here; so I wondered if I could prove this effect, using data taken at extremely hot days. There is no sensor on the roof to measure temperature and radiation directly at the panels, but we take data taken every 90 seconds for:
- Ambient air temperature
- Global radiation on a vertical plane, at the position of the solar thermal collector used with the heat pump system.
I was looking for the following:
- Two (nearly) cloudless days, in order to rule out the impact of shadowing at different times of the days.
- These days should not be separated by too many other days, to rule out the effect of the changing daily path of the sun.
- Ideally, air temperature should be very different on these days but global radiation should be the the same.
I found such days: August 1 and August 12 2015:
August 12 was a record-breaking day with a maximum temperature of 39,5°C. August 1 was one of the ‘cool’ but still perfectly sunny days in August. The ‘cold day’ resulted in a much higher PV output, despite similar input in terms of radiation. For cross-checking I have also included August 30: Still fairly hot, but showing a rather high PV output, at a slightly higher input energy.
August 2015 in detail:
Overlaying the detailed curves for temperature and power output over time for the three interesting days:
The three curves are stacked ‘in reverse order’:
The higher the ambient air temperature, the lower the output power.
Note that the effect of temperature can more than compensate for the actually higher radiation for the middle curve (August 30).
I have used global radiation on a vertical plane as an indicator of radiation, not claiming that it is related to the radiation that would be measured on the roof – or on a horizontal plane, as it is usually done – in a simple way. We measure radiation at the position of our ribbed pipe collector that serves as a heat source for the heat pump; it is oriented vertically so that it resembles the orientation of that collector and allows us for using these data as input for our simulations of the performance of the heat pump system.
Our house casts a shadow on the solar collector and this sensor on the afternoon; therefore data show a cut-off in the afternoon:
Yet, if you compare two cloudless days where the sun traversed about the same path (thus days close in the calendar) you can conclude that solar radiation everywhere – including the position on the roof – was the same if these oddly shaped curves are alike.
This plot shows that the curves for these two days that differed a lot in output and temperature, August 1 and 12, were really similar. Actually, the cooler day with higher PV output, August 1, even showed the lower solar radiation due to some spikes. Since the PV inverter only logs every 5 minutes whereas our system’s monitoring logs every 1,5 minutes those spikes might have been averaged out in the PV power curves. August 30 clearly showed higher radiation which can account for the higher output energy. But – as shown above – the higher solar power could not compensate for the higher ambient temperature.
- Temperature and solar radiation have been measured using sensors attached to universal control UVR1611 by Technische Alternative and C.M.I. as data logger, logging interval 90 seconds. Temperature sensor – PT1000, radiation sensor. Log files are exported daily to CSV files using Winsol.
- PV output power has been measured by Datamanager 2.0 included with PV inverter Fronius Symo 4.5-3-M, logging interval 5 minutes, logged to USB stick.
- CSV log files are imported into Microsoft SQL Server 2014 for analysis and consolidation. Plots are created with Microsoft Excel as front end to SQL Server.