Spatial and Temporal Scales of Solar Variability: Implications for Grid Integration of Utility-Scale Photovoltaic Plants

Transcription

1 Spatial and Temporal Scales of Solar Variability: Implications for Grid Integration of Utility-Scale Photovoltaic Plants Andrew Mills and Ryan Wiser Lawrence Berkeley National Laboratory Electricity Markets and Policy February 24,2009 1

2 Overview Current LBNL Research Questions: What are the spatial and temporal scales of variability in solar insolation and PV output with varying levels of geographic diversity? What impact does geographic diversity play in reducing the average variability of multiple dispersed photovoltaic (PV) plants relative to a single concentrated PV plant? Research Links to Broader DOE Lab Efforts: How can we model the output of large utility-scale PV plants from existing PV plant and satellite data? What are the operational integration impacts of utility- scale photovoltaic plants? 2

3 Status and Future Directions Current Status (Focus of Presentation): Collected 1-min solar insolation from 23 measurement sites in the Southern Great Plains Began analysis of spatial and temporal scales of variability in solar insolation (preliminary results presented later in this presentation) In the process of collecting PV production data (15-min) from sites in regions where large utility-scale PV plants are expected to be developed Future Directions and Next Steps: Translate understanding of variability of solar insolation and PV production into integration cost metrics Compare cost metrics for geographically concentrated PV plants to geographically dispersed PV plants Following slide broadly outlines a research path and opportunities for collaboration between Labs 3

4 Broad Sketch of Research Required to Assess the Operational Integration Impacts of Utility-Scale Photovoltaic Plants Performance of existing PV plants, insolation data, satellite data Time-synchronized PV and insolation data across different spatial and temporal scales High-time-resolution load data, hourly reserve requirements, hourly reserve prices NREL Focus for WWSIS Model of Utility-Scale PV Plant Comparison to Wind Geographic Smoothing: Time and Space Scales Current LBNL Focus Variability of Dispersed Utility-Scale PV Plants Variability of Concentrated Utility-Scale PV Plants Potential for NREL/ Sandia/ LBNL Collaboration Impact of PV on Reserves (Regulation and Load Following) and Costs Comparison to Wind and Solar Thermal 4

5 Geographic Diversity Smoothes Rapid Variations in Output Information about the benefits of geographic diversity need to be incorporated into the analysis of utility-scale PV grid integration impacts Source: Hoff et al Source: Weimken et al

6 Temporal and Spatial Scales for Correlation of Changes in Wind Power are Well Understood Correlation of changes in wind power output is a function of distance and time-scale. 5- min variations in wind plants over 20 km apart are statistically uncorrelated. 6 Source: Ernst, Wan, and Kirby 1999

7 Similar Analysis of Spatial and Temporal Scales for Correlation of Solar is Needed Some analysis of solar is available but unlike earlier analysis of wind variability, nearest paper presents only correlation of total clearness index not changes in clearness index: Source: Barnett et al 1998 Power (MW) Power (MW) Why is the correlation of changes in output useful? Example below shows two time series, both with total correlation >0.9. The correlation of the 1-min changes in output is >0.9 for the top figure and <0.1 for the bottom figure Plant 1 40 Plant 2 Regulation (Total Output - 10 min Moving Average) Total correlation of 1-min Data : 0.99 Correlation of Changes in 1-min Data: Time (min) Plant 1 Reg = 5.1 MW 40 Plant 2 Regulation (Total Output - 10 min Moving Average) Total correlation of 1-min Data : 0.92 Correlation of Changes in 1-min Data: Time (min) Reg = 7.2 MW Regulation burden is not reduced by diversity when changes in 1-min output are correlated Regulation burden is lower (as % of peak output) when changes in 1-min output are uncorrelated 7

8 Analysis of Temporal and Spatial Scales of Geographic Diversity: ARM Program 8 Use 1-min data from 2004 to estimate correlation coefficient for changes in: - global insolation, - direct insolation, and - clearness index * between pairs of stations in the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Program. Calculate histograms of changes in solar insolation over different timesteps for a single site (C1) and all 23 available sites (C1 & E1-27 excl. E-14 & E-26) *Clearness Index: Ratio of measured insolation to clear sky insolation (i.e. insolation in absence of clouds). SGP data includes clear sky insolation

9 Results: Correlation of Changes in Global Solar Insolation as a Function of Distance and Data Averaging Period 1 Correlation Coefficient Distance Between Sites (km) 1-min Delta Global Insolation 5-min Delta Global Insolation 15-min Delta Global Insolation 30-min Delta Global Insolation 60-min Delta Global Insolation 180-min Delta Global Insolation Each point represents the correlation coefficient of changes in global solar insolation measured at two stations. 1- to 5-min changes (the timescale of regulation reserves) are nearly uncorrelated for all stations in the SGP site

10 Diversity Decreases Probability of Large Swings in 1-min 1 Solar Insolation for Multiple Sites Comparison of Changes in Global Insolation at a Single Site to Multiple Sites Probability One Site (C1) Avg. of 23 Sites Standard Deviation of Changes in 1-min Global Isolation: C1 = 56.8 W/m 2 All 23 Sites = 12.5 W/m 2 Statistical theory predicts standard deviation of average of 23 uncorrelated sites similar to site C1 would be: 56.8 W/m 2 / sqrt(23) = 11.8 W/m min Delta Global Insolation (W/m 2 ) 10

11 Diversity Decreases Probability of Large Swings in Solar Insolation for Multiple Sites Jul.02, 2004 Event: 1-min Change in Avg. Insolation from 23 Sites >150 W/m Average of 23 Sites One Site (C1) 1000 Global Insolation (W/m 2 ) /03 6:00 07/03 12:00 07/03 18:00 CST Chart shows a day in 2004 with one of the most extreme 1-min changes in global solar insolation simultaneously measured at all 23 sites

12 Diversity Decreases Probability of Large Swings in Solar Insolation for Multiple Sites Sep.04, 2004 Event: 1-min Change in Avg. Insolation from 23 Sites > 150 W/m Average of 23 Sites One Site (C1) 1000 Global Insolation (W/m 2 ) /04 6:00 09/04 12:00 09/04 18:00 CST Chart shows a day in 2004 with one of the most extreme 1-min changes in global solar insolation simultaneously measured at all 23 sites

13 Diversity Decreases Probability of Large Swings in 5-min 5 Solar Insolation for Multiple Sites Comparison of Changes in Global Insolation at a Single Site to Multiple Sites Probability One Site (C1) Avg. of 23 Sites Standard Deviation of Changes in 5-min Global Isolation: C1 = 69.8 W/m 2 All 23 Sites = 17.8 W/m 2 Statistical theory predicts standard deviation of average of 23 uncorrelated sites similar to site C1 would be: 69.8 W/m 2 / sqrt(23) = 14.6 W/m min Delta Global Insolation (W/m 2 ) 13

14 Correlation Coefficient Results: Correlation of Changes in Direct Solar Insolation as a Function of Distance and Data Averaging Period Each point represents the correlation coefficient of changes in direct solar insolation measured at two stations. 1-min Delta Direct Insolation 5-min Delta Direct Insolation 15-min Delta Direct Insolation 30-min Delta Direct Insolation 60-min Delta Direct Insolation 180-min Delta Direct Insolation Distance Between Sites (km) Results are qualitatively similar to global insolation. Changes in direct insolation are slightly less correlated than changes in global insolation for a given distance 14

15 Results: Correlation of Changes in Clearness Index as a Function of Distance and Data Averaging Period Correlation of changes in global and direct insolation is driven in part by changes in the clear sky solar insolation (i.e. changes in the position of the sun) The clearness index separates the deterministic changes in solar insolation due to movement of the sun from changes due to cloud cover The correlation of the clearness index, (d,t), as a function of averaging time in min, T, and spatial scale in km, d, is reasonably approximated by an exponential function used in the wind literature * : (d,t) = exp(-c*(d b )/T) - C and b are constants used in a least-squares fit - * A similar functional form was used for examining the correlation of wind power by Nanahara et al. (2004) 15

16 Results: Correlation of Changes in Global Clearness Index as a Function of Distance and Data Averaging Period Correlation Coefficient Exponential Fit, C = 3.54, b = Changes in global solar insolation due to clouds, as captured by the clearness index, are much less correlated than changes in the global insolation 1-min Delta Global Clearness Index 5-min Delta Global Clearness Index 15-min Delta Global Clearness Index 30-min Delta Global Clearness Index 60-min Delta Global Clearness Index 180-min Delta Global Clearness Index Distance Between Sites (km) Note: To minimize spurious data, the clearness index was only calculated during times when the cosine of the solar angle exceeded 0.15

17 Correlation Coefficient Results: Correlation of Changes in Direct Clearness Index as a Function of Distance and Data Averaging Period Exponential Fit, C = 4.14, b = Changes in direct solar insolation due to clouds, as captured by the direct clearness index, are similarly less correlated than changes in the direct insolation 1-min Delta Direct Clearness Index 5-min Delta Direct Clearness Index 15-min Delta Direct Clearness Index 30-min Delta Direct Clearness Index 60-min Delta Direct Clearness Index 180-min Delta Direct Clearness Index Distance Between Sites (km) 17

18 Diversity Decreases Probability of Large Swings in 1-min 1 Global Clearness Index for Multiple Sites Comparison of Changes in Global Clearness Index at a Single Site to Multiple Sites Probability One Site (C1) Avg. of 23 Sites Standard Deviation of Changes in 1-min Global Clearness Index: C1 = 8.19 E-02 All 23 Sites = 1.76 E-02 Statistical theory predicts standard deviation of average of 23 uncorrelated sites similar to site C1 would be: 8.19E-02 / sqrt(23) = 1.71 E min Delta Clearness Index 18

19 Diversity Decreases Probability of Large Swings in 5-min 5 Global Clearness Index for Multiple Sites Comparison of Changes in Global Clearness Index at a Single Site to Multiple Sites Probability One Site (C1) Avg. of 23 Sites Standard Deviation of Changes in 5-min Global Clearness Index: C1 = 10.9 E-02 All 23 Sites = 2.34 E-02 Statistical theory predicts standard deviation of average of 23 uncorrelated sites similar to site C1 would be: 10.9E-02 / sqrt(23) = 2.27 E min Delta Clearness Index 19

20 Discussion of Results 1- to 5-min changes in global insolation, direct insolation, and clearness index are approximately uncorrelated for all pairs of stations in the SGP site dataset (20 km to 440 km apart) Results from the SGP site and statistical theory therefore suggests the average standard deviation of 1- to 5-min changes in insolation at n sites at least 20 km apart will be approximately 1/sqrt(n) times smaller than the standard deviation of changes measured at one similar site. Based on results from the SGP Site, there is no additional benefit on the 1- to 5- min time scale from separating sites by more than 20 km. Additional separation in the SGP site does not lead to less correlation over these short time scales. Extreme events (~800 W/m 2 /min) occurred at one site in 2004, but average output from all 23 sites in the SGP site never exceeded 200 W/m 2 /min 20

21 Discussion of Results: Cont. Changes in insolation due to clouds will be much less correlated than total changes in insolation that include the effect of the movement of the sun. Over longer averaging periods and larger distances (i.e. 1- hr and 200 km apart), changes in insolation due to the movement of the sun may dominate changes in insolation due to cloud cover Geographic diversity, therefore, may not do much to reduce changes in solar plant output over hourly timescales. The changes that cannot be reduced by geographic diversity, however, are essentially deterministic. 21

22 Next Steps: Data Needs and Sources Analysis Step Ideal Data Set Potential Sources Temporal and spatial scales of geographic diversity for solar insolation 1-min time-synchronized insolation data across wide spatial scales and multiple geographic regions SGP Site in ARM (1-min insol.) DONE SunPower PV sites (15 min historic, potential for higher resolution going forward) SGIP (California sites, 15 min historic) Enphase residential PV sites? (Bay area, high-time resolution) Reserve impacts of geographically dispersed PV sites vs. geographically concentrated PV sites Costs incurred due to additional demand for operating reserves Time-synchronized load data Time dependent prices for operating reserves CAISO: RPS integration Analysis Data; hourly demand for reserves Other balancing areas CAISO historic hour ahead operating reserve prices Hourly reserve prices from system simulation (i.e. PROMOD etc) 22