Application of Portfolio Theory Based on CVaR in Determining Optimal Spinning Reserve with Consideration of Load and Wind Power Uncertainties

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Abstract:

With the increased installed capacity of wind power in power system, determining optimal spinning reserve capacity is one of the most important problems in operation of electricity power system. CVaR (conditional value at risk) is introduced to calculate the risk of the cost associated with load shed and abandoning wind power with the consideration of load and wind power prediction uncertainties. Portfolio theory based on CVaR is used to build the Cost-CVaR model. Efficient frontier, which can support the system operators (SO) with the decision of optimal spinning reserve, can be obtained by solving the Cost-CVaR model. The analysis of RTS example can demonstrate the usefulness and efficiency of the model.

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Advanced Materials Research (Volumes 724-725)

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649-654

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] H. Yu, C.Y. Chung, K.P. Wong and J.H. Zhang 'A Chance Constrained Transmission Network Expansion Planning Method With Consideration of Load and Wind Farm Uncertainties', IEEE Transactions on Power Systems, Vol.24, No.3, August 2009.

DOI: 10.1109/tpwrs.2009.2021202

Google Scholar

[2] based on chance-constrained programming for wind power penetration limit calculation

Google Scholar

[3] based on stochastic programming and forecast of dynamic economic dispatch in wind power systems

Google Scholar

[4] J. M. Morales, A. Conejo, and J. Perez-Ruiz, "Economic valuation of reserves in power systems with high penetration of wind power," IEEE Transactions on Power Systems, vol. 24, no. 2, p.900–910,2009.

DOI: 10.1109/tpwrs.2009.2016598

Google Scholar

[5] D. Chattopadhyay and R. Baldick, "Unit commitment with probabilistic reserve," in Proc. IEEE Power Eng. Soc. Winter Meeting, 2002, p.280–285.

DOI: 10.1109/pesw.2002.984999

Google Scholar

[6] H. Holttinen, "Impact of hourly wind power variations on the system operation in the Nordic countries," Wind Energy, vol. 8, no. 2, p.197–218, Apr./Jun. 2005.

DOI: 10.1002/we.143

Google Scholar

[7] Ortega-Vazquez, M.A. and D.S. Kirschen, "Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration," IEEE Transactions on Power Systems, vol. 24, no. 1, pp.114-124, Feb 2009.

DOI: 10.1109/tpwrs.2008.2004745

Google Scholar

[8] M. Black and G. Strbac, "Value of bulk energy storage for managing wind power fluctuations," IEEE Trans. Energy Convers., vol. 22, no. 1, p.197–205, Mar. 2007.

DOI: 10.1109/tec.2006.889619

Google Scholar

[9] J. X. Wang, X. F. Wang, and Y. Wu, "Operating reserve model in the power market," IEEE Trans. Power Syst., vol. 20, no. 1, p.223–229, Feb. 2005.

DOI: 10.1109/tpwrs.2004.841232

Google Scholar

[10] M. A. Ortega-Vazquez, "Optimizing the spinning reserve requirements," in Sch. Elect. Electron. Eng.. Manchester, U.K.: Univ. Manchester, 2006, p.1–219

Google Scholar

[11] Al-Awami, A.T., El-Sharkawi, M., (July 2011), "A Coordinated Trading of Wind and Thermal Energy", IEEE Transactions on Sustainable Energy, Vol 2, Issue 3, pp.277-287.

DOI: 10.1109/tste.2011.2111467

Google Scholar

[12] J. M. Morales, A. J. Conejo, and J. Perez-Ruiz, "Short-term trading for a wind power producer," IEEE Trans. Power Syst., vol. 25, no. 1, p.554–564, Feb. 2010.

DOI: 10.1109/tpwrs.2009.2036810

Google Scholar

[13] Rockafellar, R.T.and S.Uryasev,"Optimization of conditional value-at-risk," Journal of Risk, no.2 pp.21-41.

Google Scholar

[14] A. Street, L.A. Barroso, B.C. Flach, M.V. Pereira, and S.Granville."Risk Constrained Portfolio Selection of Renewable Sources in Hydrothermal Electricity Markets," IEEE Trans. Power Syst, vol.24, no.3, pp.1136-1144, 2009.

DOI: 10.1109/tpwrs.2009.2022981

Google Scholar

[15] B.Blaesig and H.-J.Haubrich,"Methods of risk management in the generation and trading planning, " in IEEE St.Petersburg Power Tech Proceedings, 2005.

DOI: 10.1109/ptc.2005.4524617

Google Scholar

[16] S.Tewari, C.J. Geyer, and N.Mohan. "A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets" IEEE Trans. Power Syst, vol. 26, no.4, pp.2031-3039, Nov. 2011.

DOI: 10.1109/tpwrs.2011.2141159

Google Scholar

[17] C. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, R. Billinton,Q. Chen, C. Fong, S. Haddad, S. Kuruganty, W. Li, R. Mukerji, D.Patton, N. Rau, D. Reppen, A. Schneider, M. Shahidehpour, and C.Singh, "The IEEE reliability test system—1996," IEEE Trans. Power Syst., vol. 14, no. 3, p.1010–1018, Aug. 1999.

DOI: 10.1109/59.780914

Google Scholar