Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-28T17:33:41.922Z Has data issue: false hasContentIssue false
  • English
  • Français

Recent results on the integration of variable renewable electric power into the US grid

Published online by Cambridge University Press:  03 June 2015

Jay Apt
Jay Apt*
Affiliation:
Tepper School of Business and Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
*
*Address all correspondence to Jay Apt at apt@cmu.edu
Get access

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.

Keywords

energyrenewableselectricity
Type
Review
Information
MRS Energy & Sustainability , Volume 2 , 2015 , E6
Copyright
Copyright © Materials Research Society 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Lave, L.B. and Seskin, E.P.: Air pollution and human health. Science 169(3947), 723–733 (1970).CrossRefGoogle ScholarPubMed
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).Google Scholar
IEA: CO2 Emissions from Fuel Combustion (International Energy Agency, Paris, 2014).Google Scholar
EIA: Electric Power Monthly with Data for December 2013 (U.S. Energy Information Agency, 2014). www.eia.gov/electricity/monthly/current_year/february2014.pdf.Google Scholar
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.Google Scholar
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.Google Scholar
Marvel, K., Kravitz, B., and Caldeira, K.: Geophysical limits to global wind power. Nat. Clim. Change 3, 118–121 (2013).CrossRefGoogle Scholar
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.Google Scholar
CPUC: Decision 11–12–020 (California Public Utilities Commission, 2011). http://docs.cpuc.ca.gov/WORD_PDF/FINAL_DECISION/154695.PDF.Google Scholar
Hirth, L., Ueckerdt, F., and Edenhofer, O.: Integration costs revisited—An economic framework for wind and solar variability. Renewable Energy 74, 925–939 (2015).CrossRefGoogle Scholar
Wan, Y. and Bucaneg, D.: Short-term power fluctuations of large wind power plants. Sol. Energy Eng. 124(4), 427–431 (2002).Google Scholar
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.Google Scholar
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.Google Scholar
Apt, J.: The spectrum of power from wind turbines. J. Power Sources 169(2), 369–374 (2007).CrossRefGoogle Scholar
Katzenstein, W. and Apt, J.: The cost of wind power variability. Energy Policy 5, 233–243 (2012).Google Scholar
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).Google Scholar
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).Google Scholar
Katzenstein, W., Fertig, E., and Apt, J.: The variability of interconnected wind plants. Energy Policy 38(8), 4400–4410 (2010).Google Scholar
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).CrossRefGoogle Scholar
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.Google Scholar
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).Google Scholar
EnerNex: Final Report—2006 Minnesota Wind Integration Study: Volume One, 2006.Google Scholar
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).Google Scholar
NYISO: Growing Wind Final Report of the NYISO Wind Generation Study (2010).Google Scholar
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).Google Scholar
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.Google Scholar
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).Google Scholar
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.Google Scholar
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).Google Scholar
Katzenstein, W. and Apt, J.: Air emissions due to wind and solar power. Environ. Sci. Technol. 43(2), 253–258 (2009).CrossRefGoogle ScholarPubMed
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).Google Scholar
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).Google Scholar
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).Google Scholar
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).Google Scholar
Pattanariyankool, S. and Lave, L.B.: Optimizing transmission from distant wind farms. Energy Policy 38, 2806–2815 (2010).CrossRefGoogle Scholar
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.Google Scholar
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).Google Scholar
Gyuk, I.P.: Epri-doe Handbook Supplement of Energy Storage for Grid Connected Wind Generation Applications, EPRI report, 1008703 (2004).Google Scholar
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).Google Scholar
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).Google Scholar
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).Google Scholar
Letendre, S.E. and Kempton, W.: The V2G concept: A new model for power? Public Utilities Fortnightly 140(4), 16–26 (2002).Google Scholar
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.Google Scholar
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).Google Scholar
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).CrossRefGoogle ScholarPubMed
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.Google Scholar
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).CrossRefGoogle Scholar
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).Google Scholar
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).CrossRefGoogle Scholar
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).CrossRefGoogle Scholar
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).Google Scholar
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.Google Scholar
Rose, S. and Apt, J.: The cost of curtailing wind turbines for secondary frequency regulation capacity. Energy Syst. 5(3), 407–422 (2014).Google Scholar
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).Google Scholar
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).CrossRefGoogle ScholarPubMed
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).Google Scholar