Probabilistic Method of Wind Generation Placement for Congestion Management

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.

Authors:

• S. Z. Moussavi
• A. Badri
• F. Rastegar Kashkooli

Keywords:

• Probabilistic optimal power flow
• Wind power
• Pointestimate methods
• Congestion management

References:

[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.