Variable Renewable Energy and the Electricity Grid Jay Apt Tepper School of Business and Department of Engineering & Public Policy Carnegie Mellon University May 23, 2016 CEDM Annual Meeting

Renewable Market Share in the USA 2

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Wind and solar plants’ variability is not white noise. If it were, the grid would need a lot of very fast- adjusting power to compensate. But, the fluctuations are 30 times larger at long periods than at short, so slow fossil fuel plants can compensate, and very few batteries are needed. Wind Solar PV

5 2.6 Days 30 Seconds Fourier Transform to get the Power Spectrum

6 Frequency -5/3 I’ve written that this is a Kolmogorov spectrum. That is not the right explanation. Kraichnan, Phys. Fluids 7, 1723 (1964) and Tennekes, J. Fluid Mech. 67, 561 (1975) showed that an assumption of Kolmogorov’s is violated: large and small scale turbulence are not statistically independent. Eddies of all sizes feel the influence of large scale eddies.

7 Frequency -5/3 Mahesh Bandi (now at Okinawa Institute of Science and Technology Graduate University) has shown that the turbine spectrum can be explained by the random sweeping hypothesis (also known as the sweeping decorrelation hypothesis). The Doppler effect due to the mean air velocity broadens out the spectrum to produce a f -5/3 dependence. G. Bel, C. P. Connaughton, M. Toots, and M. M. Bandi, Grid-scale Fluctuations and Forecast Error in Wind Power, New Journal of Physics 18 (2016) doi: / /18/2/023015

We can learn some important things from the power spectrum 8

9 Smoothing by Adding Wind Farms

Katzenstein, W., E. Fertig, and J. Apt, The Variability of Interconnected Wind Plants. Energy Policy, (8):

Professor Bandi has looked at smoothing In a manuscript he is about to submit for review, “The Spectrum of Wind Power Fluctuations” he finds that there is a theoretical limit to the smoothing in the frequency domain of f -7/3. Mahesh found that 224 wind plants in EIRGRID (from his earlier paper in New J. Physics) do not further steepen the spectrum we found with adding the 20 wind plants in Texas. 12

Smoothing by geographic diversity 1.When we talk about smoothing, the time scale is very important: 12 hours has much less smoothing than 1 hour. 13

2. The point of diminishing returns from connecting wind plants together is quickly reached. Katzenstein, W., E. Fertig, and J. Apt, The Variability of Interconnected Wind Plants. Energy Policy, (8):

Larger areas 15 BPA CAISO ERCOT MISO

Does interconnecting regions provide additional smoothing at high frequency? 16 Less Smoothing More Smoothing Fertig, E., J. Apt, P. Jaramillo, and W. Katzenstein, The effect of long-distance interconnection on wind power variability. Environmental Research Letters, (3):

Interconnection does reduce the worst hour-to-hour step changes 17

Similar analysis for utility-scale PV 18

We examined generation data from 50 power plants in Gujarat (western India) Green = Plants used MW of installed capacity; changed over time. Data downloaded from company website every minute. Klima, K. and J. Apt, Geographic Smoothing of Solar PV: Results from Gujarat. Environmental Research Letters 10 (2015)

The amount of smoothing achieved with 20 plants is almost the same as with 10 plants. Magnitude of generation variability Black = 1 plant Red = 5 plants Green = 10 plants Magenta = 20 plants

21 6 hours 1 hour PVWind That is probably because PV’s deep power fluctuations lead to variability at many frequencies.

That’s for large plants Kelly is now examining rooftop PV, for warehouses and drugstores in California. These installations are kW. Her initial findings show much more geographic smoothing than for the MW plants in Gujarat. 22 Green: 5 MW Blue: 1 rooftop Red: 19 rooftops

What about solar thermal? 23 Source for images: Acconia-na.com

24 Lueken, C., G. Cohen, and J. Apt, The Costs of Solar and Wind Power Variability for Reducing CO 2 Emissions. Environmental Science & Technology, (17):

Wind and solar thermal are less expensive to integrate than large PV Concentrating solar thermal systems have much lower variability than do solar PV systems, and so compensating for their fluctuations is less expensive. Photo: Solar Industries Association 25 Lueken, C., G. Cohen, and J. Apt, The Costs of Solar and Wind Power Variability for Reducing CO 2 Emissions. Environmental Science & Technology, (17): Using CAISO Prices

Hydroelectric Power has Droughts 26

Steve Rose has used the Climate Forecast System (CFS) Reanalysis data 27 Locations of the commercial (> 10 MW) wind plants in the U.S. Great Plains and Eastern Interconnect as of December The diameter of the circles is proportional to wind plant capacity. The largest site in this group is Fowler Ridge, Indiana, with a capacity of 600MW

Aggregate annual wind energy fluctuations 28 Red – airport data Blue – Reanalysis Data Rose, S. and J. Apt, What Can Reanalysis Data Tell Us About Wind Power? In Review at Renewable Energy.

Wind compared to Hydro Source: Katzenstein, W., E. Fertig, and J. Apt, The Variability of Interconnected Wind Plants. Energy Policy, (8):

Annual energy generation is clustered 30

So is variability 31 Mean Coefficient of Variation (COV) of annual energy generation for EWITS wind plants in the U.S. Great Plains and the Eastern Interconnect.

Observed wind forecast errors are dependent whether the forecast is for weak or strong wind 32 Mauch, B., J. Apt, P.M.S. Carvalho and M. Small, An Effective Method for Modeling Wind Power Forecast Uncertainty. Energy Systems, (4):

33 Forecasts of wind power under-predict wind during periods of light wind, and over-predict when the wind blows strongly.

Wind and load both have forecast errors. But neither distribution is Gaussian. 34 DAH Load Fcst Error DAH Wind Fcst Error (data courtesy of MISO and AWS TruePower) 34

Most commercial studies of reserves assume Gaussian changes in wind 35 BPA wind output data for 2010 (10,000 MW of wind capacity) This means that studies that compute the standard deviation of step changes* significantly underestimate the probability of large changes in wind that are the primary reliability risks – the main reason that reserves are required. *Examples: EnerNex 2011; GE Energy 2005a, 2010; NYISO 2010 The probability of absolute 5-minute step changes that are at least as large as the value on the x axis Dowds, J., P. Hines, T. Ryan, W. Buchanan, E. Kirby, J. Apt, and P. Jaramillo, A Review of Large-Scale Wind Integration Studies. Renewable & Sustainable Energy Reviews :

Towards improved studies Methods such as the one proposed by Charles River Associates (2010), which use the magnitude of low- probability ramping events rather than standard deviations, are likely to produce balancing resource estimates that more accurately predict what will be needed to maintain system reliability. An even more useful improvement would be to build on the methods developed by KEMA (2010), which used a dynamic power system model to simulate the effect of different amounts and types of balancing resources. 36 Charles River Associates: SPP WITF Wind Integration Study, CRA Project No. D14422, Boston, MA (2010) KEMA: Research Evaluation of Wind Generation, Solar Generation, and Storage Impact on the California Grid, Public Interest Energy Research Program, California Energy Commission, CEC (2010).

Wind occasionally fails for many days Sum of ~1000 turbines Source: BPA 37

38 Mauch, B., J. Apt, P.M.S. Carvalho and P. Jaramillo, What Day-Ahead Reserves are Needed in Electric Grids with High Levels of Wind Power?. Environmental Research Letters 8 (2013) Using ERCOT’s requirement to cover 95% of DAH forecast errors: 38

My view on wind power in the USA Wind+solar can certainly grow from its present ~5% market share, to perhaps ~35%. Their growth may make up for the decrease in nuclear’s market share, or perhaps more than make up for that. Wind’s market share is likely to be limited not by electric engineering, but rather by land use issues and (for offshore wind) cost. PV and wind maintenance and replacement are likely to be a growth industry. 39

40 Thank You!