Distributed solar variability

One of the common arguments against solar as an energy source is that it’s just too variable.  You can never count on it when you need it.  What if clouds roll in and out? [1]

One counter-argument might be – well, you never know when anyone will turn on their AC, either, at least not minute-by-minute.  The grid is a balancing act; unpredictable, random loads have the same effect as unpredictable, random generators.

To which one might then counter yes, but there are so many AC units out there, they average out, more or less, turning on and off at random times and smoothing things out in aggregate.

To which the solar advocate might reply OK, then with enough solar the peaks and valleys of generation should cancel out too, as clouds move out of one area into another.  Does this seem likely out in practice?

To find out, I grabbed 5 minute data from about 40 Enphase systems in the twin cities on a highly variable, sporadically cloudy day.  Because we don’t yet have a whole lot of solar here, and I didn’t want the one or two large commercial systems in the group to swamp the smaller residential systems, first I normalized them all to a % of their max output.  (This might be cheating a little, but with a lot more systems randomly distributed in size and geography, the swamping-out effect should be minimiized.)  Here’s what just 4 of those systems looks like; each is indeed pretty messy and unpredictable at the 5-minute range:

solar_junkThen I averaged all of the systems.  Here’s what the average looks like, compared to one of the individual systems:

solar_smoothingIt appears that things certainly do smooth out when we look at geographically distributed systems.  If I were a grid operator, I might feel a lot better about that.

The caveats might be that this is a very wide geographic range – I grabbed systems from all of the twin cities and suburbs.  And that’s probably larger than the various sub-grids within the cities; what the variability is within those subgrids is, or how this solar variability affects them, I’m not sure.  And of course my initial normalization of all systems to the same size could be argued with.

There have been much more rigorous papers and presentations written on this as well, see for example “Quantifying PV Power Output Variability” by Thomas E. Hoff and Richard Perez in 1999, and “Implications of Wide-Area Geographic Diversity for Short- Term Variability of Solar Power” by Andrew Mills and Ryan Wiser at LBNL in 2010.  But with the advent of 5-minute monitoring from systems like Enphase, I wonder if even better results could be found from this wealth of data.

[1]  I’ll submit that a sporadically cloudy day is more trouble to a grid operator than a generally cloudy day.   We often know if a day will be cloudy well ahead of time, and that doesn’t yield the minute-to-minute variations of a sporadically cloudy day.  The grid is better, I think, at responding to these longer-term variations.

6 thoughts on “Distributed solar variability

  1. So if your demand was somehow related to the supply … like your cooling you’d be fine (if you assume cooling demands would be related to solar heating effects then demand would lag slightly), however if it was in out of phase e.g. nigh time heating you’d need a battery or other storage device. Alternatively you might have some base load capacity to buffer it – your geographical diversity might supply some of this, as well as non-solar sources.

    There are ideas which would see electric vehicles as a mobile battery system which would be located near correlated demand. The power companies could pay customers for storage as well as generation and charge for usage etc.

    Of course large scale storage could be something other than chemical batteries, e.g. pumped water hydro, ice storage, compressed gas, etc.

    I don’t believe that any single power generation source is 100% reliable/available so power companies are already relying on multiple generators to supply enough capacity, as well as storage systems to smooth out some of the peaks.

    • I think most of the things you address here are best suited to smooth out longer variations, although I suppose local storage could smooth the short-term variations as well. What I’m wondering, though, is whether the most significant short-term variations from individual distributed generators smooth themselves out based on the random nature of sporadic cloud cover, without the need for extra technology.

      The EV-to-grid scenario is interesting, but if I had an EV I’d need the utility to pay me a fair bit to sacrifice my battery life…

  2. I think the partially cloudy vs. generally cloudy thing is relevant to different concerns; partially cloudy is a question of whether generation spikes are going to melt the grid; generally cloudy is a question of whether we need to build some coal plants to stand by in case of a week of rain. They do complain about needing to have a bunch of generation capacity that they rarely use in order to deal with foreseeable events, since they would have to pay rent, maintenance, etc., but rarely produce anything.

    • Right, I think day-to-day variations are a different problem vs. minute-to-minute variations.

      But regarding day-to-day variations & standby generation capacity, we almost always have generation capacity standing by idle. On the load side, day-to-day variations come about based on weather, for example. A cooler day requires less AC and less load, therefore less generating capacity, and peaker plants sit idle. A hotter day generally fires them up. Within that hotter day, there are minute-to-minute load changes on the grid as individual ACs click on & off.

      I don’t doubt that there are challenges when integrating PV into the grid, but I’m proposing that with enough distributed PV generation, the grid balance variations it may cause could be mitigated in the same way that random, distributed, unpredictable loads are mitigated – average enough random noise, and you get something which looks more constant.

  3. Hi Eric,

    Thank you for taking the time to do the distributed generation analysis exploring solar PV variability and presenting it so comprehensively. I am now an avid follower of our Blog and linking my readership to your Blog through mine: http://ecodomusconsulting.blogspot.ca/

    Regarding my Blog: I am, unfortunately, behind on updating the entries associated with the 4.24 kW solar PV demonstration system in Hamilton, ON Canada.
    These are interesting times for those of us following the renewable energy industry here in Ontario. Incentivisation strategies for renewable energy – based distributed generation have come to bare and continue to evolve through the Ontario Power Authority’s Feed-in Tariff Program, which has been in effect since Sep 2009 or so.

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