Skip to main content

Advertisement

SpringerLink
Log in

The impacts of stochastic programming and demand response on wind integration

  • Original Paper
  • Published:
Energy Systems Aims and scope Submit manuscript

Abstract

Wind imposes costs on power systems due to uncertainty and variability of real-time resource availability. Stochastic programming and demand response are offered as two possible solutions to mitigate these so-called wind-uncertainty costs. We examine the benefits of these two solutions, and show that although both will reduce wind-uncertainty costs, demand response is significantly more effective. We also examine the impacts of using demand response and stochastic optimization together. We show that most of the value of demand response in reducing wind-uncertainty costs remain if a stochastic optimization is used and that there are subadditive benefits from using the two together.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. These data are publicly available at http://wind.nrel.gov/Web_nrel/.

  2. We caveat the word ‘actual’ with the word ‘modeled’ to stress that we use modeled data in our analysis. Thus these values may differ from actual weather conditions in 2005.

  3. Details of the wind forecast error model are given in appendix C of the California ISO’s report.

  4. Section 2.4 of Appendix B of the California ISO’s report discusses empirical findings regarding the autocorrelation of wind forecast errors.

References

  1. Abbey, C., Joos, G.: Supercapacitor energy storage for wind energy applications. IEEE Trans. Ind. Appl. 43, 769–776 (2007)

    Article  Google Scholar 

  2. Birge, J.R.: The value of the stochastic solution in stochastic linear programs with fixed recourse. Math. Program. 24, 314–325 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  3. Black, M., Strbac, G.: Value of bulk energy storage for managing wind power fluctuations. IEEE Trans. Energy Convers. 22, 197–205 (2007)

    Article  Google Scholar 

  4. Borenstein, S.: The long-run efficiency of real-time electricity pricing. Energy J. 26, 93–116 (2005)

    Google Scholar 

  5. Borenstein, S., Bushnell, J.B., Knittel, C.R.: A Cournot-Nash equilibrium analysis of the New Jersey electricity market. Appendix A of Review of General Public Utilities Restructuring Petition, Final Report, New Jersey Board of Public Utilities. Docket Number EA97060396 (1997)

  6. Borenstein, S., Holland, S.P.: On the efficiency of competitive electricity markets with time-invariant retail prices. RAND J. Econ. 36, 469–493 (2005)

    Google Scholar 

  7. Bouffard, F., Galiana, F.D.: Stochastic security for operations planning with significant wind power generation. IEEE Trans. Power Syst. 23, 306–316 (2008)

    Article  Google Scholar 

  8. Cavallo, A.J.: High-capacity factor wind energy systems. J. Solar Energy Eng. 117, 137–143 (1995)

    Article  Google Scholar 

  9. Contaxis, G.C., Vlachos, A.G.: Optimal power flow considering operation of wind parks and pump storage hydro units under large scale integration of renewable energy sources. In: Power Engineering Society Winter Meeting, pp. 1745–1750. Institute of Electrical and Electronics Engineers (2000)

  10. DeCarolis, J.F., Keith, D.W.: The economics of large-scale wind power in a carbon constrained world. Energy Policy 34, 395–410 (2006)

    Article  Google Scholar 

  11. DeMeo, E.A., Grant, W., Milligan, M.R., Schuerger, M.J.: Wind plant integration. IEEE Power Energy Mag. 3, 38–46 (2005)

    Article  Google Scholar 

  12. DeMeo, E.A., Jordan, G.A., Kalich, C., King, J., Milligan, M.R., Murley, C., Oakleaf, B., Schuerger, M.J.: Accommodating wind’s natural behavior. IEEE Power Energy Mag. 5, 59–67 (2007)

    Article  Google Scholar 

  13. Denholm, P., Sioshansi, R.: The value of compressed air energy storage with wind in transmission-constrained electric power systems. Energy Policy 37, 3149–3158 (2009)

    Article  Google Scholar 

  14. Doherty, R., O’Malley, M.: A new approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Trans. Power Syst. 20, 587–595 (2005)

    Article  Google Scholar 

  15. Dupačová, J., Gröwe-Kuska, N., Römisch, W.: Scenario reduction in stochastic programming. Math. Program. 95, 493–511 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  16. Escudero, L.F., Kamesam, P.V., King, A.J., Wets, R.J.B.: Production planning via scenario modelling. Ann. Oper. Res. 43, 311–335 (1993)

    MathSciNet  MATH  Google Scholar 

  17. Fabbri, A., Román, T.G.S., Abbad, J.R., Quezada, V.H.M.: Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market. IEEE Trans. Power Syst. 20, 1440–1446 (2005)

    Article  Google Scholar 

  18. García-González, J., de la Muela, R.M.R., Santos, L.M., González, A.M.: Stochastic joint optimization of wind generation and pumped-storage units in an electricity market. IEEE Trans. Power Syst. 23, 460–468 (2008)

    Article  Google Scholar 

  19. Greenblatt, J.B., Succar, S., Denkenberger, D.C., Williams, R.H., Socolow, R.H.: Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation. Energy Policy 35, 1474–1492 (2007)

    Article  Google Scholar 

  20. King, C.S., Chatterjee, S.: Predicting California demand response. Public Utilities Fortnightly, pp. 27–32 (2003)

  21. Lee, S.T., Yamayee, Z.A.: Load-following and spinning-reserve penalties for intermittent generation. IEEE Trans. Power Apparatus Syst. PAS-100, 1203–1211 (1981)

    Article  Google Scholar 

  22. Loutan, C., Hawkins, D.: Integration of renewable resources. Tech. Rep, California Independent System Operator (2007)

  23. Lower Colorado River Authority, Austin, Texas: Study of Electric Transmission in Conjunction with Energy Storage Technology. Prepared for Texas State Energy Conservation Office (2003)

  24. Paatero, J.V., Lund, P.D.: Effect of energy storage on variations in wind power. Wind Energy 8, 421–441 (2005)

    Article  Google Scholar 

  25. Papavasiliou, A., Oren, S.S.: Coupling wind generators with deferrable loads. In: Energy 2030 Conference. Institute of Electrical and Electronics Engineers, Atlanta, GA, USA (2008)

  26. Rockafellar, R.T., Wets, R.J.B.: Scenario and policy aggregation in optimization under uncertainty. Math. Oper. Res. 16, 119–147 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  27. Ruiz, P.A., Philbrick, C.R., Zak, E., Cheung, K.W., Sauer, P.W.: Uncertainty management in the unit commitment problem. IEEE Trans. Power Syst. 24, 642–651 (2009)

    Article  Google Scholar 

  28. Sioshansi, R.: Evaluating the impacts of real-time pricing on the cost and value of wind generation. IEEE Trans. Power Syst. 25, 741–748 (2010)

    Article  Google Scholar 

  29. Sioshansi, R., Hurlbut, D.: Market protocols in ERCOT and their effect on wind generation. Energy Policy 38, 3192–3197 (2010)

    Article  Google Scholar 

  30. Sioshansi, R., Short, W.: Evaluating the impacts of real-time pricing on the usage of wind generation. IEEE Trans. Power Syst. 24, 516–524 (2009)

    Article  Google Scholar 

  31. Smith, J.C., Milligan, M.R., DeMeo, E.A., Parsons, B.: Utility wind integration and operating impact state of the art. IEEE Trans. Power Syst. 22, 900–908 (2007)

    Article  Google Scholar 

  32. Söder, L.: Reserve margin planning in a wind-hydro-thermal power system. IEEE Trans. Power Syst. 8, 564–571 (1993)

    Article  Google Scholar 

  33. Sørensen, B.: A combined wind and hydro power system. Energy Policy 9, 51–55 (1981)

    Article  Google Scholar 

  34. Streiffert, D., Philbrick, R., Ott, A.: A mixed integer programming solution for market clearing and reliability analysis. In: Power Engineering Society General Meeting, 2005. IEEE. San Francisco, CA (2005)

  35. Swider, D.J.: Compressed air energy storage in an electricity system with significant wind power generation. IEEE Trans. Energy Convers. 22, 95–102 (2007)

    Article  Google Scholar 

  36. Tuohy, A., Meibom, P., Denny, E., O’Malley, M.: Unit commitment for systems with significant wind penetration. IEEE Trans. Power Syst. 24, 592–601 (2009)

    Article  Google Scholar 

  37. Western Wind and Solar Integration Study. Tech. Rep. NREL/SR-550-47434, National Renewable Energy Laboratory (2010)

Download references

Acknowledgments

The authors would like to thank S. Sen, A. Sorooshian, the editor, and two anonymous reviewers for helpful suggestions and discussions. T. Grasso, P. Denholm, M. Milligan, D. Lew, and D. Hurlbut provided invaluable assistance in gathering ERCOT market and wind generation data.

Author information

Authors and Affiliations

  1. Department of Integrated Systems Engineering, The Ohio State University, Baker Systems Engineering Building, 1971 Neil Avenue, Columbus, OH, 43210, USA

    Seyed Hossein Madaeni & Ramteen Sioshansi

Authors
  1. Seyed Hossein Madaeni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramteen Sioshansi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramteen Sioshansi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Madaeni, S.H., Sioshansi, R. The impacts of stochastic programming and demand response on wind integration. Energy Syst 4, 109–124 (2013). https://doi.org/10.1007/s12667-012-0068-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12667-012-0068-7

Keywords

Search

Navigation