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The value of energy storage in optimal non-firm wind capacity connection to power systems


  • Fallahi, Farhad
  • Nick, Mostafa
  • Riahy, Gholam H.
  • Hosseinian, Seyed Hossein
  • Doroudi, Aref


Wind is a variable and uncontrollable source of power with a low capacity factor. Using energy storage facilities with a non-firm connection strategy is the key to maximum integration of distant wind farms into a transmission-constrained power system. In this paper, we explore the application of energy storage in optimal allocation of wind capacity to a power system from distant wind sites. Energy storage decreases transmission connection requirements, smoothes the wind farm output and decreases the wind energy curtailments in a non-firm wind capacity allocation strategy. Specifically, we examine the use of compressed air energy storage (CAES) technology to supplement wind farms and downsize the transmission connection requirements. Benders decomposition approach is applied to decompose this computationally challenging and large-scale mixed-integer linear programming (MILP) into smaller problems. The simulation results show that using energy storage systems can decrease the variation of wind farms output as well as the total cost, including investment and operation costs, and increase the wind energy penetration into the power system.

Suggested Citation

  • Fallahi, Farhad & Nick, Mostafa & Riahy, Gholam H. & Hosseinian, Seyed Hossein & Doroudi, Aref, 2014. "The value of energy storage in optimal non-firm wind capacity connection to power systems," Renewable Energy, Elsevier, vol. 64(C), pages 34-42.
  • Handle: RePEc:eee:renene:v:64:y:2014:i:c:p:34-42
    DOI: 10.1016/j.renene.2013.10.025

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    References listed on IDEAS

    1. Denholm, Paul & Sioshansi, Ramteen, 2009. "The value of compressed air energy storage with wind in transmission-constrained electric power systems," Energy Policy, Elsevier, vol. 37(8), pages 3149-3158, August.
    2. Tuohy, A. & O'Malley, M., 2011. "Pumped storage in systems with very high wind penetration," Energy Policy, Elsevier, vol. 39(4), pages 1965-1974, April.
    3. Zhao, M. & Chen, Z. & Blaabjerg, F., 2006. "Probabilistic capacity of a grid connected wind farm based on optimization method," Renewable Energy, Elsevier, vol. 31(13), pages 2171-2187.
    4. DeCarolis, Joseph F. & Keith, David W., 2006. "The economics of large-scale wind power in a carbon constrained world," Energy Policy, Elsevier, vol. 34(4), pages 395-410, March.
    5. Denholm, Paul, 2006. "Improving the technical, environmental and social performance of wind energy systems using biomass-based energy storage," Renewable Energy, Elsevier, vol. 31(9), pages 1355-1370.
    6. Niknam, Taher & Khodaei, Amin & Fallahi, Farhad, 2009. "A new decomposition approach for the thermal unit commitment problem," Applied Energy, Elsevier, vol. 86(9), pages 1667-1674, September.
    7. Pattanariyankool, Sompop & Lave, Lester B., 2010. "Optimizing transmission from distant wind farms," Energy Policy, Elsevier, vol. 38(6), pages 2806-2815, June.
    8. Greenblatt, Jeffery B. & Succar, Samir & Denkenberger, David C. & Williams, Robert H. & Socolow, Robert H., 2007. "Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation," Energy Policy, Elsevier, vol. 35(3), pages 1474-1492, March.
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    Cited by:

    1. Rinne, S. & Syri, S., 2015. "The possibilities of combined heat and power production balancing large amounts of wind power in Finland," Energy, Elsevier, vol. 82(C), pages 1034-1046.


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