Utility-Scale PV Projects: First US Statistical Performance Analysis

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Utility-scale photovoltaic plant (emp.lbl.gov)

Deployment of utility-scale PV power in the United States in recent years has been rapid and noteworthy. It has resulted very dissimilar photovoltaic operating projects whose empirical AC capacity factors differ by more than a factor of two, however. Understanding the performance of utility-scale PV power is important because profitability depends directly on how well projects perform over time. The sector can only raise investment capital if the long-term profitability factors are favorable.

Under the title ”Maximizing MWh,” scientists from the Lawrence Berkeley National Laboratory and the University of California at Berkeley’s Goldman School of Public Policy contributed a valuable statistical analysis of American utility-scale photovoltaic projects this month. The report is the first known used of multivariate regression techniques to analyze empirical variation in project-level performance.

The 128-project (3201 MW) sample split almost evenly between fixed-tilt (63 projects, 1776 MW) and tracking (65 projects, 1776 MW) projects. Its findings give solar project developers and investors a good indication of what they can expect from the different project configurations used in different regions of the country. Also, through this model’s tight relationship between actual and fitted capacity factors, investors can gain confidence that the projects in this sample have largely performed as expected.

Buildup of robust multivariate regression model (emp.lbl.gov)

Authors Mark Bolinger, Joachim Seel, and Manfei Wu analyze the independent variables and other factors responsible for the variations:

“The regression models developed for this analysis find 92% of this variation caused by only three highly significant independent variables”:

  • Solar resource strength, in terms of average annual global horizontal irradiance (“GHI”) estimates (note: GHI alone explains 71.6%);
  • Tracking, which increases “plane of array” irradiance; and
  • Inverter Loading Ratio (“ILR”), which boosts AC capacity factor.

Adding a fourth independent variable (project vintage, or COD Year) and 3 interactive terms (Tracking x GHI, Tracking x ILR, GHI x ILR) improves the model further. Good data on power temperature coefficients and module operating temperatures might also improve calculations.

Model 5 summary graphic (emp.lbl.gov)

The study did not examine orientation (tilt and azimuth) and temperature because of limited reliable data on coefficients and temps. It’s also worth noting that 83% of MW and 66% of projects are in the “top 5” states for NCF and GHI; California alone accounts for 49% of capacity and 32% of projects; and single-axis tracking prevails in the high-GHI states.

The research was supported by funding from the U.S. Department of Energy’s SunShot Initiative. You can read the full report here or watch this youtube video summary.

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16 thoughts on “Utility-Scale PV Projects: First US Statistical Performance Analysis

  • The number that stands out, (if I am reading this mess correctly) is 35.68% capacity factor for solar trackers. Amazing!

    • As I read it, each item can add several percentage points to the capacity factor. Trackers add 4 to 5 percentage points. Good sun resource in the first place (GHI) adds 5 or so. All together, these variables can make the difference as to whether a system produces at 20% of its nameplate capacity up to 35%. This all leads to a better predictive model so investors can optimize their design before it’s built. Is it more cost effective to add mechanical frackers or to spend that money on more panels instead? – as one example.

      • “Frackers”, ha, got the wrong idea for a second there

        • Oops – typo! 🙂

    • Amazing, but pretty much identical to what manufacturers like FirstSolar claim in their marketing material: that a tracker improves output by 25%.

      There’s a reason why tracking technology is starting to dominate the industry in high solar areas. It’s incredibly simple technology (a small electric motor is both cheap and reliable) and adds hugely to output.

      • That’s not all. It’s output is spread out more. In particular, you get further into the evening peak. I remember reading about some incentive or something trying to get people to face their pannels west. Well, if you have a tracker, you are facing west when it makes sense to.

      • I wish residential-level tracking was widely available. But it isn’t. You can do it with pole-mount or ground-mount but not really with roof-mount.

  • Not a bad result. No doubt the solar panels are “over performing” in most US solar farms as I’m sure full sunshine at their locations averages greater than the 1,000 watts per square meter panels are rated on and the solar panels are still pretty new.

    Australia recently competed some utility scale solar farms which have tracking and are inverter limited to a fair degree. The reason appears to be due to lower electricity prices during the middle of the day thanks to our relatively large amount of rooftop solar. Yesterday throughout most of Australia there was an early morning spike in electricity prices, followed by lower prices during the day and a higher spike in prices in the evening. And this tendency is only going to get stronger with increased solar capacity. Tracking boosts output early in the morning and late in the afternoon, while being inverter limited means potential output is lost during the middle of the day when rooftop PV tends to be producing more. Being inverter limited also means the difference in output between sunny and cloudy days is not as great as it otherwise would be.

    • Ronald, Just wondering. Who sees these higher and lower prices? Does the consumer see a TIME OF DEMAND factor on their bill or are you referring to utility to utility billing? Lou Gage, USA

      • I was referring to the wholesale electricity prices that utility scale solar receives for their electricity. For households it all depends on what sort of tariff they are on. Some people are on time of use tariffs that have a off-peak, shoulder, and peak rates. These are fixed time periods and don’t change according to what the wholesale price is. They just rely on the fact that electricity is usually expensive, and transmission capacity more likely to be close to maxed out in the late afternoon and evening and cheap early in the morning. Other households are on simpler tariffs where what they pay doesn’t change with the time of day, but they do pay more in summer when electricity demand is higher than in winter. Just what sort of tariff people have varies depending on location and the type of electricity meter they have.

        Large users of electricity can pay electricity prices that are based on the wholesale price. Thus they have an incentive to use less when wholesale electricity prices are high and more when wholesale electricity prices are low. Large users of electricity may have demand management contracts where they agree to cut electricity use if it is required to keep the grid operating properly in return for lower electricity rates.

        If renewables result in lower electricity prices in an area these savings have to be passed on to consumers. Eventually. Retail electricity prices in my state have been reduced thanks to the effects of wind and solar power.

        • Thank you for the details. Most interesting. Will have to look into my state’s practice. Lou Gage

      • California has now passed that on at the retail level too. Used to be peak pricing was in the middle of the day. Now it’s 3pm to 9pm and mid day is off-peak pricing. 7GW of solar on a 26GW grid makes a big difference in generation.

        • Your comment made me smile for two reasons. When solar is pushing the price of electricity around, it’s no longer insignificant. Those gigawatts are starting to add up. Second, if we want things to be efficient, we need to be honest about what things cost. The better retail prices reflect wholesale, the cheaper electricity can be overall.

          • Yeah it’s made me smile too. 25% of the 6th largest economy on earth ain’t hippie fringe anymore. 7GW is real power. Oh, and that’s undercounted by the way because a lot is invisible and not metered.

    • If the price spread is “good” then sounds like the farm would/should look at batteries, and correctly size inverter. I think what stops that is risk/fear, after all who know what will happen with those daily price highs/lows over 10 years.

      • In Australia wholesale electricity prices are generally so low it make it impossible for grid battery storage to pay for itself without very large decreases in its cost. However, we still have grid battery storage in special cases. For example, population growth and rooftop solar in Hervey Bay means that it can be difficult for the grid to meet demand for a short period of time in the late afternoon and evening in the summer due to air conditioner use. So rather than pay the expense of building an extra transmission line they have/are installing lithium ion batteries. They are also looking at providing subsidies for home energy storage in the area.

        Also, grid battery storage can provide ancillary services, as is currently being done in Germany, and that might enable it to pay for itself. But we’re also looking into wind and solar to provide ancillary services.

        But in Australia due to our high retail costs of electricity, low cost of solar, and low solar feed-in tariffs, we are closer to having home energy storage pay for itself than in any other country. A few more years maybe now that the Powerwall hasn’t turned out to be as low cost as we were promised.

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