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Summary of Wind Variability and Uncertainty: Considerations in System Planning and Operation
In this chapter, several important aspects of wind power are presented. When considering wind power, there are several contributing factors to its overall value in the power system. The collective impacts of these factors typically lead to a net positive economic value, acknowledging that there may be instances when this is not the case, depending on the specific utility or electrical system and its market. One of the cost components of wind energy is the “integration” cost, due to the effects of wind powers variability and uncertainty on system operation. NREL (Wan 2004, 2005, 2008) has published some insightful reports describing the variability of wind power, based on the output of actual wind power plants in the United States. The data presented in this chapter is intended to provide a sense for the order of magnitude and frequency of power output changes with which a system operator or planner must deal. The data suggest that there will be a relatively small impact at the regulation (minute-to-minute) time scale, but it becomes considerable at the hourly time scale and beyond (e.g., load following, unit commitment, reserve requirements). In addition to being variable, wind power is also uncertain, and although accurately predictable much of the time, it can suffer from large forecast errors that may occur at inopportune times during system operation. Wind power, while primarily an energy resource, has a capacity value that should be considered in system planning.
Another positive aspect of wind energy is its environmental attribute of being a carbon-free, water-free energy generation technology. What makes wind power different to a system operator and planner as compared to other power resources is its variability and uncertainty, and learning how to understand and work with these characteristics. The overall impact of the wind power variations, forecast errors, and their associated integration cost—combined with the cost of wind energy, its marginal value, and the positive benefits it brings to the electrical system, depend upon a host of factors including the system load, the generation fleet, operational and market flexibility, etc.—can only be accurately estimated via a thorough cost production simulation of the power system. The methods employed in carrying-out such a simulation are presented in Volume II, Chapter 1 of the Task 24 report. Results of such studies pertaining to wind and hydropower integration will be discussed in Chapter 4.
New references cited in this chapter
Acker, T.L, Knitter, K., Conway, K., and Buechler, J. (2006) “Wind and Hydropower Integration in the Grant County Public Utility District, Washington,” Proceedings of the 2006 Hydrovision Conference, Portland, Oregon, USA.
Acker, T.L. (2007) “Final Report: Arizona Public Service Wind Integration Cost Impact Study,” Northern Arizona University, available at http://wind.nau.edu/APSWindIntegrationsStudy.shtml.
Barthelmie, R.J, Murray, F., and Pryor, S.C. (2008) “The economic benefit of short-term forecasting for wind energy in the UK electricity market,” Energy Policy 36, pp. 1687–1696
Bolinger, M., Wiser R., and Golove, W. (2002) “Quantifying the Value that Wind Power Provides as a Hedge against Volatile Natural Gas Prices,” Proceedings of AWEA WindPower 2002 Conference, June 2-5, 2002, Portland, Oregon.
Costa, A., Crespo, A., Navarro, J., Palomares, A., and Madsen, H. (2006) “Modelling the Integration of Mathematical and Physical Models forShort-term Wind Power Forecasting,” Proceedings of the European Wind Energy Conference, February-March 2006, Athens, Greece.
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., and Feitoso, E. (2008) “A review on the young history of the wind power short-term prediction,” Renewable & Sustainable Energy Reviews 12 (2008) 1725-1744.
Denny, E., Bryans, G., Gerald, J.F., and O’Malley, M. (2006), “A Quantitative Analysis of the Net Benefits of Grid Integrated Wind,” ISBN: 1-4244-0493-2, Proceedings of the IEEE 2006 Power Engineering Society General Meeting, Montreal, Québec.
Ernst, B., Oakleaf, B., Ahlstrom, M.L., Lange, M., Moehrlen, C., Lange, B., Focken, U., and Rohrig, K. (2007) “Predicting the Wind” IEEE Power & Energy Magazine, 1540-7977, November/December 2007, pgs. 78-89.
GE Energy (2005). “The Effects of Integrating Wind Power on Transmission System Planning, Reliability, and Operations: Report on Phase 2, prepared for The New York State Energy Research and Development Authority,” Albany, NY, available at http://www.uwig.org/opimpactsdocs.html.
Kunz, T.H., Arnett, E.B., Cooper, B.M., Erickson, W.P., Larkin, R.P., Mabee, T., Morrison, M.L.,
Strickland, M.D., and Szewczak, J.M., (2007), “Assessing Impacts of Wind-Energy Development
on Nocturnally Active Birds and Bats: A Guidance Document,” Journal of Wildlife Management 71(8): 2449–2486; 2007
Piwko, D. (2005) “The Value of Wind Forecasting,” Proceedings of the UWIG Fall Technical Workshop Wind Integration: Focus on the Value of Wind Forecasting, November 7-9, 2005 - Sacramento, California. Available at http://www.uwig.org/Sacramento/Session1-Piwko.pdf
NWCC (2004). “Wind Turbine Interactions with Birds and Bats: A Summary of Research Results and Remaining Questions,” fact sheet posted at the National Wind Coordinating Collaborative website http://www.nationalwind.org/publications/wildlife.htm, accessed September 2009.
Wan, Y. (2004) “Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data,” U.S. National Renewable Energy Laboratory, NREL/TP-500-36551.
Wan, Y. (2005) “A Primer on Wind Power for Utility Applications,” U.S. National Renewable Energy Laboratory, NREL/TP-500-36230.
Wan, Y. (2008) “Summary Report of Wind Farm Data,” U.S. National Renewable Energy Laboratory, NREL/TP-500-44348.
Zack, J.W. (2009) “Wind Power Production Forecasting: The Basics,” Proceedings of the UWIG/NREL Workshop on Wind Forecasting Applications to Utility Planning and Operations, Phoenix, Arizona, USA.
References cited in the Ch 1 of Vol 1 (to be listed in the references section of Vol 1):
Acker, T., Buechler, J., Knitter, K., and Conway, K. (2007)“Impacts of Integrating Wind Power into the Grant County PUD Balancing Area,” proceedings of the 2007 AWEA Windpower Conference, Los Angeles, CA.
DENA, 2005. “Energy Management Planning for the Integration of Wind Energy into the Grid in Germany, Onshore and Offshore by 2020,” Deutsche Energie-Agentur GmbH (dena), February 2005. English version posted at www.uwig.org/Dena-2005_English.pdf.
Holttinen, H., Meibom, P. Orths, A., van Hulle, F., Lange, B., O’Malley, M., Pierik, J., Ummels, B., Tande, J.O., Estanqueiro, A., Matos, M., Gomez, E., Söder, L., Strbac, G., Shakoor, A., Ricardo, J. Smith, J.C., Milligan, M., and Ela, E. (2008), “Design and operation of power systems with large amounts of wind power; Final report Phase 1: 2006-08,” IEA WIND Task 25, VTT Tiedotteita Research Notes 2493.
Holttinen, H., Milligan, M., Kirby, B., Acker, T., Neimane, V., and T.Molinski, (2008a) “Using standard deviation as a measure of increased operational reserve requirement for wind power,” Wind Engineering, Vol. 4, No. 30, 2008, pgs. 355-378, special issue on wind integration.
IEA (2005). Variability of Wind Power and Other Renewables: Management options and strategies, OECD/IEA, Paris.
IEA (2008). Renewables Information 2008, IEA Publications, France, ISBN 978-92-64-02775-6, OECD/IEA, Paris.
IEA (2008). Empowering Variable Renewables: Options for Flexible Electricity Systems, OECD/IEA, Paris.
IEEE (2005). IEEE Power and Energy Magazine, Special Issue: Working with Wind; Integrating Wind into the Power System, November–December, (3:6).
Ireland (2008). All Island Grid Study, sponsored by the Republic of Ireland Energy Department and the Department of Enterprise, Trade and Investment, available at http://www.uwig.org/opimpactsdocs.html.
Milligan, M., and Parsons, B. (1997) A Comparison and Case Study of Capacity Credit Algorithms for Intermittent Generators, Proceedings of Solar ’97 Conference, NREL/CP-440-22591, Washington, DC.
Milligan, M., and Porter, K. (2008). Determining the Capacity Value of Wind: An Updated Survey of Methods and Implementation, Proceedings of the AWEA Windpower 2008 Conference, NREL/CP-500-43433, Houston, TX.
NERC (2009). Special Report:Accommodating High Levels of Variable Generation, published by the National Electric Reliability Council, www.nerc.com/files/IVGTF_Report_041609.pdf.
Smith, J.C., Parsons, B., Acker, T., Milligan, M., Zavadil, R., Schuerger, M, and E. DeMeo (2007). Best Practices in Grid Integration of Variable Wind Power: Summary of Recent US Case Study Results and Mitigation Measures, proceedings of the 2007 European Wind Energy Conference, Milan, Italy.
Söder, L. and Holttinen, H. (2008). “On methodology for modeling wind power impact on power systems,” Int. J. Global Energy Issues, Vol. 29, Nos. 1/2, pp.181-198.
US DOE. 2008. “20% Wind Energy by 2030; Increasing Wind Energy’s Contribution to U.S. Electricity Supply,” U.S. Department of Energy report DOE/GO-102008-2567.
Westrick, K., Storck, P. and Froese, G. (2003). “Reliance on Renewables – The Synergistic Relationship between Wind and Hydropower,” Proceedings of the AWEA Windpower 2003 Conference, Austin, TX.
Wiser, R. and Bolinger, M., (2009) “2008 Wind Technologies Market Report,”
US Department of Energy, Energy Efficiency and Renewable Energy, http://www.windpoweringamerica.gov/pdfs/2008_annual_wind_market_report.pdf.
New Glossary Terms
MAE Mean Absolute Error
NREL National Renewable Energy Laboratory, US Department of Energy
RMSE Root Mean Square Error
US United States
AEMO Australian Energy Market Operator (http://www.aemo.com.au/).
AGC Automatic Generation Control
BA Balancing area
ELCC Effective Load Carrying Capacity
EMS Energy management system
IA Implementing Agreement
IEA International Energy Agency (http://www.iea.org/).
LOLP Loss of Load Probability
MISO Midwest System Independent Operator (http://www.midwestiso.org/home).
NordPool The Nordic Power Exchange: the single power market for Norway, Denmark, Sweden and Finland (http://www.nordpool.com/en).
PUD Public Utility District
RTO Regional Transmission Organization
SCADA Supervisory Control and Data Acquisition
TSO Transmission System Operator.
1 Spacing between these power plants ranged from 40 to 1500 km.