Abstract
New research results in several areas that can help to facilitate the large-scale integration of variable renewable power sources into the electric power system are reviewed.
Increasing the market share of variable renewable electric power generation in the United States from the present 4% is eminently feasible, and can be facilitated by recent research. The amplitude of variability of wind and solar power is much less at high frequencies than at low frequencies, so that slow-ramping generators such as combined-cycle natural gas and coal can compensate for most of the variability. The interannual variability of wind power is beginning to be understood, as are the biases in its day-ahead forecasts. Geographic aggregation of wind and solar power has been proposed as a method to smooth their variability; for wind power, it has been shown that there is little smoothing at timescales where the magnitude of variability is strongest. It has also been shown that the point of diminishing returns is reached after a relatively few wind plants have been interconnected. While good prospects for lower cost electric storage for grid applications exist, the profitability of storage for integration of renewable power is likely to remain a difficult issue. New extremely efficient, low pollution, and fast-ramping natural gas plants have come on the market. It is now possible to predict the amount of additional capacity of this sort that must be procured by system operators to cover the uncertainty in wind forecasts.
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References
Lave L.B. and Seskin E.P.: Air pollution and human health. Science 169(3947), 723–733 (1970).
Laden F., Schwartz J., Speizer F.E., and Dockery D.W.: Reduction in fine particulate air pollution and mortality. Am. J. Respir. Crit. Care Med. 173, 667–672 (2006).
IEA: CO2 Emissions from Fuel Combustion (International Energy Agency, Paris, 2014).
EIA: Electric Power Monthly with Data for December 2013 (U.S. Energy Information Agency, 2014). www.eia.gov/electricity/monthly/current_year/february2014.pdf.
Burger B.: Electricity Production from Solar and Wind in Germany in 2014 (Fraunhofer Institute for Solar Energy Systems ISE, 2014). http://www.ise.fraunhofer.de/en/downloads-englisch/pdf-files-englisch/data-nivc-/electricity-production-from-solar-and-wind-in-germany-2014.pdf.
Global Wind Energy Council: Annual Market Update (2014). http://www.gwec.net/wp-content/uploads/2014/04/GWEC-Global-Wind-Report_9-April-2014.pdf.
Marvel K., Kravitz B., and Caldeira K.: Geophysical limits to global wind power. Nat. Clim. Change 3, 118–121 (2013).
CPUC: Renewable Energy Portfolio Standard, Quarterly Report, 1st and 2nd Quarter 2012 (California Public Utilities Commission, 2012). www.cpuc.ca.gov/NR/rdonlyres/2060A18B-CB42-4B4B-A426-E3BDC01BDCA2/0/2012_Q1Q2_RPSReport.pdf.
CPUC: Decision 11–12–020 (California Public Utilities Commission, 2011). http://docs.cpuc.ca.gov/WORD_PDF/FINAL_DECISION/154695.PDF.
Hirth L., Ueckerdt F., and Edenhofer O.: Integration costs revisited—An economic framework for wind and solar variability. Renewable Energy 74, 925–939 (2015).
Wan Y. and Bucaneg D.: Short-term power fluctuations of large wind power plants. Sol. Energy Eng. 124(4), 427–431 (2002).
Wan Y.: Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data. National Renewables Energy Laboratory Technical Report NREL/TP-500-36551, 2004. http://www.nrel.gov/docs/fy04osti/36551.pdf.
Moura P.S. and de Almeida A.T.: Large scale integration of wind power generation. In Handbook of Power Systems I, Energy Systems, Rebennack S. ed.; Springer-Verlag, Berlin, Heidelberg, 2010; pp. 95–119.
Apt J.: The spectrum of power from wind turbines. J. Power Sources 169(2), 369–374 (2007).
Katzenstein W. and Apt J.: The cost of wind power variability. Energy Policy 5, 233–243 (2012).
Lueken C., Cohen G., and Apt J.: The costs of solar and wind power variability for reducing CO2 emissions. Environ. Sci. Technol. 46(17), 9761–9767 (2012).
Kolmogorov A.N.: Dissipation of energy in the locally isotropic turbulence. Dokl. Akad. Nauk. SSSR 30, 301–305 (1941). Reprinted Proc. R. Soc. Lond. A 434(1890), 9–13 (1991).
Katzenstein W., Fertig E., and Apt J.: The variability of interconnected wind plants. Energy Policy 38(8), 4400–4410 (2010).
Mauch B., Apt J., Carvalho P.M.S., and Small M.J.: An effective method for modeling wind power forecast uncertainty. Energy Systems 4(4), 393–417 (2013).
Hodge B., Florita A., Orwig K., Lew D., and Milligan M.: A comparison of wind power and load forecasting distributions. 2012 World Renewable Energy Forum, NREL/CP-5500-54384 (2012). www.nrel.gov/docs/fy12osti/54384.pdf.
GE Energy: The Effects of Integrating Wind Power on Transmission System Planning, Reliability and Operations, Report on Phase 2: System Performance Evaluation (New York State Energy Research and Development Authority, Schenectady, NY, 2005).
EnerNex: Final Report—2006 Minnesota Wind Integration Study: Volume One, 2006.
EnerNex, Ventyx: Nebraska Power Association. Nebraska Statewide Wind Integration Study, U.S. Department of Energy, Golden, Colorado, National Renewable Energy Laboratory Report No: NREL/SR-550-47519 (2010).
NYISO: Growing Wind Final Report of the NYISO Wind Generation Study (2010).
GE Energy: Western Wind and Solar Integration Study, U.S. Department of Energy, Golden, CO, National Renewable Energy Laboratory Report No: NREL/SR-550-47434 (2010).
Hodge B. and Milligan M.: Wind power forecasting error distributions over multiple timescales. Presented at the Power and Energy Society General Meeting, 2011. http://dx.doi.org/10.1109/PES.2011.6039388.
Holttinen H., Milligan M., Kirby B., Acker T., Neimane V., and Molinski T.: Using standard deviation as a measure of increased operational reserve requirement for wind power. Wind Eng. 32(4), 355–378 (2008).
Charles River Associates: SPP WITF Wind Integration Study, CRA Project No. D14422, Boston, MA (2010). http://www.uwig.org/CRA_SPP_WITF_Wind_Integration_Study_Final_Report.pdf.
KEMA: Research Evaluation of Wind Generation, Solar Generation, and Storage Impact on the California Grid, Public Interest Energy Research Program, California Energy Commission, CEC-500-2010-010 (2010).
Katzenstein W. and Apt J.: Air emissions due to wind and solar power. Environ. Sci. Technol. 43(2), 253–258 (2009).
Fertig E., Apt J., Jaramillo P., and Katzenstein W.: The effect of long-distance interconnection on wind power variability. Environ. Res. Lett. 7(3), 034017 (2012).
Whitacre J.F., Wiley T., Shanbhag S., Wenzhou Y., Mohamed A., Chun S.E., Weber E., Blackwood D., Lynch-Bell E., Gulakowski J., Smith C., and Humphreys D.: An aqueous electrolyte, sodium ion functional, large format energy storage device for stationary applications. J. Power Sources 213, 255–264 (2012).
Huskinson B., Marshak M.P., Shuh C., Süleyman E., Gerhardt M.R., Galvin C.J., Chen X., Aspuru-Guzik A., Gordon R.G., and Aziz M.J.: A metal-free organic-inorganic aqueous flow battery. Nature 505(7482), 195–198 (2014).
Hittinger E., Whitacre J.F., and Apt J.: Compensating for wind variability using co-located natural gas generation and energy storage. Energy Syst. 1(4), 417–439 (2010).
Pattanariyankool S. and Lave L.B.: Optimizing transmission from distant wind farms. Energy Policy 38, 2806–2815 (2010).
Lueken C.: Integrating variable renewables into the electric Grid: An evaluation of challenges and potential solutions, Ph.D. thesis, Carnegie Mellon University, 2012. http://wpweb2.tepper.cmu.edu/electricity/theses/Colleen_Lueken_PhD_Thesis_2012.pdf.
Fertig E., Heggedal A.M., Doorman G., and Apt J.: Optimal investment timing and capacity choice for pumped hydropower storage. Energy Syst. 5(2), 285–306 (2014).
Gyuk I.P.: Epri-doe Handbook Supplement of Energy Storage for Grid Connected Wind Generation Applications, EPRI report, 1008703 (2004).
Fertig E. and Apt J.: Economics of compressed air energy storage to integrate wind power: A case study in ERCOT. Energy Policy 39(5), 2330–2342 (2011).
Siler-Evans K., Azevedo I., Morgan M.G., and Apt J.: Regional variations in the health, environmental, and climate benefits of wind and solar generation. Proc. Natl. Acad. Sci. U. S. A. 110(29), 11768–11773 (2013).
Peterson S.B., Whitacre J.F., and Apt J.: The economics of using PHEV battery packs for grid storage. J. Power Sources 195(8), 2377–2384 (2010).
Letendre S.E. and Kempton W.: The V2G concept: A new model for power? Public Utilities Fortnightly 140(4), 16–26 (2002).
Kempton W., Udo V., Huber K., Komara K., Letendre S., Baker S., Brunner D., and Pearre N.: A Test of vehicle-to-grid (V2G) for energy storage and frequency regulation in the PJM system (2008). http://www.udel.edu/V2G/resources/test-v2g-in-pjm-jan09.pdf.
Weis A., Jaramillo P., and Michalek J.: Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration. Appl. Energy 115, 190–204 (2014).
Valentino L., Valenzuela V., Botterud A., Zhou Z., and Conzelmann G.: System-wide emissions implications of increased wind power penetration. Environ. Sci. Technol. 46(7), 4200–4206 (2012).
Oates D.L. and Jaramillo P.: Production cost and air emissions impacts of coal cycling in power systems with large-scale wind penetration. Environ. Res. Lett. 8, (2013). doi: 10.1088/1748-9326/8/2/024022.
Yang M., Patino-Echeverri D., and Yang F.: Wind power generation in China: Understanding the mismatch between capacity and generation. Renewable Energy 41, 145–151 (2012).
De Jonghe C., Hobbs B.F., and Belmans R.: Optimal generation mix with short-term demand response and wind penetration. IEEE Trans. Power Syst. 27(2), 830–839 (2012).
Moura P.S. and de Almeida A.T.: The role of demand-side management in the grid integration of wind power. Appl. Energy 87, 2581–2588 (2014).
Dietrich K., Latorre J.M., Olmos L., and Ramos A.: Demand response in an isolated system with high wind integration. IEEE Trans. Power Syst. 27(1), 20–29 (2012).
Focken U., Lange M., Mönnich K., Waldl H.P., Beyer H.G., and Luig A.: Short-term prediction of the aggregated power output of wind farms—A statistical analysis of the reduction of the prediction error by spatial smoothing effects. J. Wind. Eng. Ind. Aerod. 90, 231–246 (2002).
Mauch B., Apt J., Carvalho P.M.S., and Jaramillo P.: What day-ahead reserves are needed in electric grids with high levels of wind power?Environmental Research Letters 8(3), (2013). doi: 10.1088/1748-9326/8/3/034013.
Rose S. and Apt J.: The cost of curtailing wind turbines for secondary frequency regulation capacity. Energy Syst. 5(3), 407–422 (2014).
Lindenberg S., Smith B., O’Dell K., DeMeo E., and Ram B.: US DOE, 20% Wind Energy by 2030, DOE/GO-102008-102567, U.S. Department of Energy, Washington, DC (2008).
Rose S., Jaramillo P., Small M.J., Grossmann I., and Apt J.: Quantifying the hurricane risk to offshore wind turbines. Proc. Natl. Acad. Sci. U. S. A. 109(9), 3247–3252 (2012).
Rose S., Jaramillo P., Small M.J., and Apt J.: Quantifying the hurricane catastrophe risk to offshore wind power. Risk Anal. 33(12), 2126–2141 (2013).
Acknowledgments
This summary of recent research is based on the work of the RenewElec project and its team: Jonathan R. Dowds, Michael Dworkin, Emily Fertig, Mark Handschy, Paul Hines, Eric Hittinger, Paulina Jaramillo, Warren Katzenstein, Elizabeth Kirby, Colleen Lueken, Roger Lueken, Brandon Mauch, Jared Moore, M. Granger Morgan, Robert R. Nordhaus, David Luke Oates, Scott Peterson, Steven Rose, Deborah D. Stine, Allison Weis, and David Yaffe. Primary support for this work was provided by grants from the Doris Duke Charitable Foundation and the Richard King Mellon Foundation. Additional support was provided by The Heinz Endowments and by the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation and Carnegie Mellon University (SES-0949710).
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Apt, J. Recent results on the integration of variable renewable electric power into the US grid. MRS Energy & Sustainability 2, 6 (2015). https://doi.org/10.1557/mre.2015.7
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DOI: https://doi.org/10.1557/mre.2015.7