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Energy Systems
Erschienen in: Energy Systems 2/2013

01.06.2013 | Original Paper

The impacts of stochastic programming and demand response on wind integration

verfasst von: Seyed Hossein Madaeni, Ramteen Sioshansi

Erschienen in: Energy Systems | Ausgabe 2/2013

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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.
Nächster Artikel Multiple channel PMU placement considering communication constraints

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Fußnoten
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.
 
Literatur
1.
Abbey, C., Joos, G.: Supercapacitor energy storage for wind energy applications. IEEE Trans. Ind. Appl. 43, 769–776 (2007)CrossRef
2.
Birge, J.R.: The value of the stochastic solution in stochastic linear programs with fixed recourse. Math. Program. 24, 314–325 (1982)MathSciNetMATHCrossRef
3.
Black, M., Strbac, G.: Value of bulk energy storage for managing wind power fluctuations. IEEE Trans. Energy Convers. 22, 197–205 (2007)CrossRef
4.
Borenstein, S.: The long-run efficiency of real-time electricity pricing. Energy J. 26, 93–116 (2005)
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)
7.
Bouffard, F., Galiana, F.D.: Stochastic security for operations planning with significant wind power generation. IEEE Trans. Power Syst. 23, 306–316 (2008)CrossRef
8.
Cavallo, A.J.: High-capacity factor wind energy systems. J. Solar Energy Eng. 117, 137–143 (1995)CrossRef
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)CrossRef
11.
DeMeo, E.A., Grant, W., Milligan, M.R., Schuerger, M.J.: Wind plant integration. IEEE Power Energy Mag. 3, 38–46 (2005)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
15.
Dupačová, J., Gröwe-Kuska, N., Römisch, W.: Scenario reduction in stochastic programming. Math. Program. 95, 493–511 (2003)MathSciNetMATHCrossRef
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)MathSciNetMATH
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)MathSciNetMATHCrossRef
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)CrossRef
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)CrossRef
29.
Sioshansi, R., Hurlbut, D.: Market protocols in ERCOT and their effect on wind generation. Energy Policy 38, 3192–3197 (2010)CrossRef
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)CrossRef
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)CrossRef
32.
Söder, L.: Reserve margin planning in a wind-hydro-thermal power system. IEEE Trans. Power Syst. 8, 564–571 (1993)CrossRef
33.
Sørensen, B.: A combined wind and hydro power system. Energy Policy 9, 51–55 (1981)CrossRef
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)CrossRef
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)CrossRef
37.
Western Wind and Solar Integration Study. Tech. Rep. NREL/SR-550-47434, National Renewable Energy Laboratory (2010)
Metadaten
Titel
The impacts of stochastic programming and demand response on wind integration
verfasst von
Seyed Hossein Madaeni
Ramteen Sioshansi
Publikationsdatum
01.06.2013
Verlag
Springer-Verlag
Erschienen in
Energy Systems / Ausgabe 2/2013
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-012-0068-7

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