Assessing Solar and Wind Complementarity in Texas: A 2018 Greene Prize Excerpt

Posted by on Aug 18, 2018

Below is an excerpt from Rice undergraduate student Joanna Slusarewicz’s winning essay for the 2018 Greene Prize for Environmental Writing.

Endowed by the family of alumnus David Greene (’93), the Greene Prize was established in 1998 to encourage the creation, dissemination, and recognition of original environmental writing by students at Rice. In 2016, the competition was expanded to include Rice graduate students for the first time, with the aim of offering prizes at both the undergraduate and graduate level to reward excellence in environmental writing. All Greene Prize submissions this year were nominated by Rice faculty who taught courses with substantial environmental content. Nominations included outstanding coursework, creative writing, thesis projects, research reports and writing oriented toward a public audience. 

 

Assessing Solar and Wind Complementarity in Texas

By Joanna H. Slusarewicz and Daniel S. Cohan

Abstract: As wind and solar power installations proliferate, power grids will face new challenges in ensuring consistent coverage from variable renewable resources. One option to reduce variability is to integrate the output from wind and solar facilities with dissimilar temporal profiles of output. This study measured the complementarity of wind and solar resources sited in various regions of Texas. This study modeled solar and wind power output using the System Advisory Model with solar data from the National Solar Radiation Database and wind data from the Wind Integration National Dataset Toolkit. Half-hourly power production was assessed based on resource location, plant size, hourly load, inter-annual variability, and solar array design for all sites. We found that solar and wind resources exhibit complementary peaks in production on an annual and daily level, and that West and South Texas wind resources also exhibit complementarity. Pairings of West Texas wind with solar power or South Texas wind sites yield the highest firm capacity. Solar farms are better suited for providing power during summertime hours of peak demand, whereas wind farms are better for winter. Taken together, our results suggest that Texas renewable power production can be made more reliable by combining resources of different types and locations.

Introduction: Wind and solar power now provide the least-cost options for electricity generation in windy and sunny regions of the United States, even before accounting for subsidies and environmental impacts (Lazard, 2017). Wind and solar also yield substantial benefits for climate, air quality, and health when replacing fossil fuels (Jacobson, 2008). However, the ability of wind and solar power to displace fossil fuels is limited by their variable nature. Aggregating multiple intermittent generators whose output differs temporally can reduce the uncertainty and variability of their output (Hart et al., 2012). Reduced intermittency can be achieved by aggregating multiple wind farms (Kahn, 1979) or by combining the output of wind and solar farms (Zhou et al., 2010).

In 2017, wind provided 17% of power generation on the Electric Reliability Council of Texas (ERCOT) grid, which covers most of Texas, while solar provided just 1% (ERCOT, 2017). Many analysts expect both of these sources could provide an increasing share of electricity as their costs have fallen (Lazard, 2017) and as aging coal-fired power plants close. In 2018 alone, four coal plants are closing in ERCOT. ERCOT provides a distinct testbed for analysis because it is relatively isolated from the interconnected power grids that supply electricity to most of North America.

The rapid evolution of generating resources in ERCOT raises the question of the extent to which variable output from solar and wind can replace the retiring coal. Since solar and wind power production vary with the weather, other sources or storage are needed to ensure power demand is fulfilled continuously.

One way to reduce the need for costly storage and for polluting fossil generation is to deploy wind and solar capacity in a way that minimizes the times when their power is unavailable. Previous studies have shown that in many areas solar and wind resources demonstrate anticorrelated peaks and valleys in intensity throughout the day (Monforti et al., 2014). Alongside this natural resource complementarity at a given location, an important component of wind/solar complementarity depends on diversifying the locations of these resources (Prasad et al., 2015; Yi et al., 2013; Shaner et al., 2018). In addition, spreading out wind farms in certain region results in an increase in the reliability of both and solar alone (LBNL, 2009) and wind alone (Katzenstein et al., 2010). In particular, wind production in northwest Texas has been shown to increase as the area of distribution increases (Katzenstein et al., 2010).

Taking advantage of complementarily of wind and solar resources as well as the natural complementarity of systems with large spread can lead to a greater ability to meet consumer demand. Including both wind and solar in ERCOT’s energy portfolio evens out production and reduces the number or hours where either resource cannot produce (Prasad et al., 2015). In addition, increasing the area over which wind and solar plants are located means that production times are less correlated, enhancing the performance of forecasts (Hart, 2011) that are crucial to power pricing and dispatch.

This study aims to identify locations and configurations of wind and solar facilities in ERCOT that would optimize the magnitude and complementarity of their power production. To do so, we analyze temporal patterns and variability in expected power production from potential wind and solar farms in Texas, using metrics developed in past studies for other regions together with metrics specifically targeted to ERCOT.

Conclusions: WT wind produced the most total power annually, followed by ST wind production and then solar. Over the year, solar production is complementary with both WT and ST wind. WT wind paired with solar provided the highest levels of firm capacity at a 87.5% threshold. Accordingly, combining solar resources with WT wind might increase reliable power production on an annual basis. On a daily basis, however, WT wind and ST wind and solar all have different peak production times with ST wind peaking in the later afternoon, when demand for power is highest. This suggests that combining solar with ST wind might increase reliable power production over the course of a summer day during hours of high demand.

Directly comparing the sites’ hourly production with times of greatest demand throughout the year yielded further insights. Solar production was the highest during summer hours when load on the ERCOT grid was highest, and WT and ST wind productions were the highest during winter peak hours. WT wind showed greater production during both the summer and winter peak hours than the ERCOT estimate, suggesting ERCOT’s approach is conservative in this case. Our results also suggest a need for ERCOT to re-evaluate its estimates of ST wind availability during seasonal peak hours. We estimate that these coastal sites provide more output during winter peak load than summer, contrary to ERCOT’s assumptions in its resource assessments.

Comparisons of different solar configurations show that, though a west-facing fixed-tilt system yields less than half the output of a dual-axis tracking system, it can produce almost as much power during the peak load hours for summer. This suggests that a relatively low-cost system could play a valuable role in meeting summer peak demand.

Areas for further investigation include expanding the scope of measurements from seven sample sites to locations throughout the state in order to pinpoint specific locations that maximize complementarity (thus reliability) and best meet demand over the course of each day. Further research could also explore alternatives to the ERCOT resource adequacy factors that might more fully characterize the reliable production potential of Texas renewables. These results might suggest ways to organize future renewables projects to maximize reliability with minimal investment in expensive storage technologies.  Such analyses will become increasingly important as the mix of Texas variable renewable electricity supply shifts from predominately West Texas wind to include more solar power and a broader mix of wind locations.

 

References

Archer, C. L., & Jacobson, M. Z. (2007). Supplying Baseload Power and Reducing Transmission Requirements by Interconnecting Wind Farms. Journal of Applied Meteorology and Climatology, 46, 1701–1717., doi:10.1175/2007jamc1538.1.

Bolinger, M., Seel, J., & LaCommare, K. (2017). Utility-Scale Solar 2016: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States. Electricity Markets & Policy Group, LBNL-2001055.

Draxl, C., Clifton, A., Hodge, B., & McCaa, J. (2015). The Wind Integration National Dataset (WIND) Toolkit. Applied Energy, 151, 355-366.

ERCOT (2017). Inside the Promise. 2016 State of the Market Report, Energy Reliability Council of Texas (ERCOT), Austin, http://www.ercot.com/content/wcm/lists/114739/ERCOT_Quick_Facts_10917.pdf

ERCOT (2018). Seasonal Assessment of Resource Adequacy for the ERCOT Region [Microsoft Excel spreadsheet]. Summer 2018 Preliminary Seasonal Assessment, Energy Reliability Council of Texas (ERCOT). Retrieved from: http://www.ercot.com/gridinfo/resource

ERCOT (2017). Seasonal Assessment of Resource Adequacy for the ERCOT Region [Microsoft Excel spreadsheet]. Winter 2017-18 Final Seasonal Assessment, Energy Reliability Council of Texas (ERCOT). Retrieved from: http://www.ercot.com/gridinfo/resource

Hart, E. K., Stoutenburg E. D., & Jacobson M. Z. (2012). The Potential of Intermittent Renewables to Meet Electric Power Demand: Current Methods and Emerging Analytical Techniques. Proceedings of the IEEE, 100, 322–334., doi:10.1109/jproc.2011.2144951.

Jacobson, M.Z. (2008). Review of solutions to global warming, air pollution, and energy security. Energy & Environmental Science, 2, 148-173, doi: 10.1039/b809990c.

Kahn, E. (1979). The reliability of distributed wind generators. Electric Power Systems Research, 2(1), 1-14.

Katzenstein, W., Fertig E., & Apt, J. (2010). The Variability of Interconnected Wind Plants. Energy Policy, 38, 4400–4410., doi:10.1016/j.enpol.2010.03.069.

Lazard (2017). Levelized Cost of Energy 2017. Lazard’s Levelized Cost of Energy Analysis, www.lazard.com/perspective/levelized-cost-of-energy-2017/.

Xiao, L., Wang, H., Dai, S., & Qi, Z. (2013). Analysis on the Hourly Spatiotemporal Complementarities between China’s Solar and Wind Energy Resources Spreading in a Wide Area. Science China Technological Sciences, 56, 683–692., doi:10.1007/s11431-012-5105-1.

Mills, A., Ahlstrom, M., Brower, M., Ellis, A., George, R., Hoff, T., Kroposki, B., Lenox, C., Miller, N., Stein, J., & Wan, Y. (2009). Understanding Variability and Uncertainty of Photovoltaics for Integration with the Electric Power System. The Electricity Journal, doi:LBNL-2855E.

Monfortia, F., Hulda, T., Bódisa, K., Vitalic, L., D’Isidoroc, M., & Lacal-Arántegui, R. (2014). Assessing Complementarity of Wind and Solar Resources for Energy Production in Italy. A Monte Carlo Approach. Renewable Energy, 63, 576–586., doi:10.1016/j.renene.2013.10.028.

Prasad, A. A., Taylor, R. A., Kay, M. (2007). Assessment of Solar and Wind Resource Synergy in Australia. Applied Energy, 190, 354–367., doi:10.1016/j.apenergy.2016.12.135.

Sengupta, M., Weekley, A., Habte, A., Lopez, A., Molling, C., & Heidinger, A. (2015). Validation of the National Solar Radiation Database (NSRDB) (2005‒2012): Preprint. National Renewable Energy Lab, 6, NREL/CP-5D00-64981.

Shaner, M. R., Davis, S. J., Lewis, N. S., & Caldeira. K. (2018). Geophysical Constraints on the Reliability of Solar and Wind Power in the United States. Energy & Environmental Science, doi:10.1039/c7ee03029k.

Zhou, W., Lou, C., Li, Z., Lu, L., and Yang, H. (2010). Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems. Applied Energy, 87, 380-389.