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Probabilistic Method of Wind Generation Placement for Congestion Management

Authors: S. Z. Moussavi, A. Badri, F. Rastegar Kashkooli


Wind farms (WFs) with high level of penetration are being established in power systems worldwide more rapidly than other renewable resources. The Independent System Operator (ISO), as a policy maker, should propose appropriate places for WF installation in order to maximize the benefits for the investors. There is also a possibility of congestion relief using the new installation of WFs which should be taken into account by the ISO when proposing the locations for WF installation. In this context, efficient wind farm (WF) placement method is proposed in order to reduce burdens on congested lines. Since the wind speed is a random variable and load forecasts also contain uncertainties, probabilistic approaches are used for this type of study. AC probabilistic optimal power flow (P-OPF) is formulated and solved using Monte Carlo Simulations (MCS). In order to reduce computation time, point estimate methods (PEM) are introduced as efficient alternative for time-demanding MCS. Subsequently, WF optimal placement is determined using generation shift distribution factors (GSDF) considering a new parameter entitled, wind availability factor (WAF). In order to obtain more realistic results, N-1 contingency analysis is employed to find the optimal size of WF, by means of line outage distribution factors (LODF). The IEEE 30-bus test system is used to show and compare the accuracy of proposed methodology.

Keywords: wind power, Congestion management, Probabilistic optimal power flow, Pointestimate methods

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[1]S. R. Dahman, K. J. Patten, A. M. Visnesky S. Grijalva, "Large-Scale Integration of Wind Generation Including Network Temporal Security Analysis," IEEE Trans Energy Conversion, vol. 22, no. 1, pp. 181-188, March 2007.
[2]N. Amjady, H. A. Shayanfar M. Esmaili, "Stochastic congestion management in power markets using efficient scenario approaches," Energy Conv. Manag., vol. 51, pp. 2285-2293, 2010.
[3]M. Fotuhi-Firuzabad A. Salehi-Dobakhshari, "Integration of large-scale wind farm projects including system reliability analysis," IET Renew. Power Gener., vol. 5, no. 1, pp. 89-98, 2011.
[4]C. L. T. Borges, and D. M. Falcão A. P. Leite, "Probabilistic Wind Farms Generation Model for Reliability Studies Applied to Brazilian Sites," IEEE Trans Power Systems, vol. 21, no. 4, pp. 1493-1501, Nov 2006.
[5]S. Li, D. C. Wunsch, E. A. O'Hair, and M. G. Giesselmann, "Using neural networks to estimate wind turbine power generation," IEEE Trans Energy Conversion, vol. 16, no. 3, pp. 276-282, Sep. 2001.
[6]J. A. Carta, P. Ramírez, and S. Velázquez, "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable Sustainable Energy Rev., vol. 13, no. 5, pp. 933- 955, 2009.
[7]M. R. Patel, Wind and Solar Power Systems.: Boca Raton, FL: CRC Press, 1999.
[8]X. Liu, W. Xu, "Economic load dispatch constrained by wind power availability: A here-and-now approach," IEEE Trans Sustainable Energy, vol. 23, no. 2, pp. 2-9, April 2010.
[9]J. Hetzer, D. C. Yu, and K. Bhattarai, "An economic dispatch model incorporating wind power," IEEE Trans Energy Convers., vol. 23, no. 2, pp. 603-611, June 2008.
[10] M. Afkousi-Paqaleh, A. Abbaspour-Tehrani Fard, M. Rashidinejad, "Distributed generation placement for congestion management considering economic and financial issues," Journal of Electr. Eng., vol. 92, pp. 193-201, September 2010.
[11] D. Gautam, M. Nadarajah, "Influence of distributed generation on congestion and LMP in competitive electricity market," Int. Jour. Electr. Power Eng., vol. 3, no. 4, pp. 228-235, 2010.
[12] G. J. Hahn and S. S. Shapiro, Statistical Models in Engineering. New York: Wiley, 1967.
[13] M. Madrigal, K. Ponnambalam, and V. H. Quintana, "Probabilistic optimal power flow," in IEEE, Waterloo, ON, Canada, 1998, p. IEEE Can. Conf. Electrical Computer Engineering.
[14] A. Schellenberg, J. Aguado, and W. Rosehart, "Introduction to cumulant-based probabilistic optimal power flow (P-OPF)," IEEE Trans Power Syst., vol. 20, no. 2, pp. 1184-1186, May 2005.
[15] H. P. Hong, "An efficient point estimate method for probabilistic analysis," Reliab. Eng. Syst. Saf., vol. 59, pp. 261-267, 1998.
[16] E. Rosenblueth, "Point estimation for probability moments," Proc. Nat. Acad. Sci. Unites States Amer., vol. 72, no. 10, pp. 3812-3814, Oct 1975.
[17] G. Verbic, C. A. Canizares, "Probabilistic optimal power flow in electricity markets based on two-point estimate method," IEEE Trans Power Syst., vol. 21, no. 4, pp. 1883-1893, November 2006.
[18] C.-L Su, "Probabilistic load-flow computation using point estimate method," IEEE Trans Power Syst., vol. 20, no. 4, pp. 1843-1851, November 2005.
[19] Y. Fu, Z. Li, and M. Shahidehpour J. Guo, "Direct Calculation of Line Outage Distribution Factors," IEEE Trans Power Systems, vol. 24, no. 3, pp. 1633-1634, Aug 2009.
[20] P. Ramirez, J. A. Carta, "Influence of the data sampling interval in the estimation of the parameter of the Weibull wind speed probability distribution: a case study," Energy Conv. Manag. Elsevier, vol. 46, pp. 2419- 2438, 2005.
[21] T. Ackerman, Wind Power in Power Systems.: Wiley, 2005.
[22] K. S. Li, "Point-estimate method for calculating statistical moments," Journal of Engineering Mechanics, ASCE, vol. 118, no. 7, pp. 1506-1511, 1992.
[23] A. J. Wood, and B. F. Wollenberg, Power Generation, Operation, and Control, Wiley, 2nd Edition, 1996.
[24] B. Stott O. Alsac, "Optimal Load Flow with Steady State Security," IEEE Transactions on Power Apparatus and Systems, vol. 93, no. 3, pp. 745- 751, 1974.