Demonstrating the Importance of Applying a New Probabilistic Power Flow Strategy to Evaluate Power Systems with High Penetration of Wind Farms
Publication: Journal of Energy Engineering
Volume 142, Issue 4
Abstract
Because today’s power systems encounter so many uncertainties, probabilistic methods, such as probabilistic power flow (PPF), are useful to analyze the systems. One of these methods is Monte-Carlo Simulation (MCS), which has the ability to consider all uncertainties, including renewable energy power production, load, and random outages of components. In this paper, the proposed method is based on MCS and data clustering to improve drawback of MCS, which include high burden of computations. The proposed method cannot only reduce the runtime, but also considers correlation between load and wind power generation (WPG). This correlation would be important to power systems having large-scale wind farms (WFs). The proposed method first was validated by applying it on an IEEE 24-bus reliability test system (RTS). Then the modified version of the method, which can model outages of components, was implemented by using analysis software on a power system that is an actual bulk power system. To demonstrate importance of applying the proposed method, the method was implemented on the system two times. For the first time, the power system was analyzed in the presence of a WF; in the second one, the power system was analyzed such that conventional power plants were replaced with the WF. The results show how much is necessary to apply the probabilistic power flow method on power systems, including WFs.
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References
ABB. (2013). “PROMOD IV.” 〈http://new.abb.com/enterprise-software/energy-portfolio-management/market-analysis/promod-iv〉.
Aien, M., Rashidinejad, M., and Fotuhi-Firuzabad, M. (2014). “On possibilistic and probabilistic uncertainty assessment of power flow problem: A review and a new approach.” Renewable Sustainable Energy Rev., 37, 883–895.
Allan, R. N., and Al-shakarchi, M. R. G. (1977). “Probabilistic techniques in A.C. load flow analysis.” Proc. IEEE, 124(2), 154–160.
Allan, R. N., Leite da silva, A. M., and Burchett, R. C. (1981). “Evaluations methods and accuracy in probabilistic load flow solutions.” IEEE Trans. Power App. Syst., PAS-100(5), 2539–2546.
Barin, A., Pozzatti, L. F., Canha, L. N., Machado, R. Q., Abaide, A. R., and Arend, G. (2010). “Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning.” Int. J. Electr. Power Energy Syst., 32(10), 1157–1164.
Baringo, L., and Conejo, A. J. (2013). “Correlated wind-power production and electric load scenarios for investment decisions.” Appl. Energy J., 101, 475–482.
Billinton, R., and Jonnavithula, S. (1996). “A test system for teaching overall power system reliability assessment.” IEEE Trans. Power Syst., 11(4), 1670–1676.
Bishop, C. M. (2006). Pattern recognition and machine learning, Springer, New York.
Borkowska, B. (1974). “Probabilistic load flow.” IEEE Trans. Power App. Syst., PAS-93(3), 752–759.
Cai, D., Shi, D., and Chen, J. (2014). “Probabilistic load flow with correlated input random variables using uniform design sampling.” Int. J. Electr. Power Energy Syst., 63, 105–112.
Chen, C., Wu, W., Zhang, B., and Sun, H. (2015). “Correlated probabilistic load flow using a point estimate method with Nataf transformation.” Int. J. Electr. Power Energy Syst., 65, 325–333.
CIGRE Working Group. (2010). “Review of the current status of tools and techniques for risk-based and probabilistic planning in power systems.”.
Delgado, C., and Dominguez, J. A. (2014). “Point estimate method for probabilistic load flow of an unbalanced power distribution system with correlated wind and solar sources.” Int. J. Electr. Power Energy Syst., 61, 267–278.
DIgSILENT. (2015). “DIgSILENT PowerFactory.” 〈http://www.digsilent.de/index.php/products-powerfactory.html〉.
Hagspiel, S., Papaemannouil, A., Schamid, M., and Andersson, G. (2012). “Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid.” Appl. Energy J., 96, 33–44.
Julier, S. J., and Uhlmann, J. K. (2004). “Unscented filtering and nonlinear estimation.” Proc IEEE, 92(3), 401–422.
Kolenc, M., Papic, I., and Blizic, B. (2015). “Assessment of maximum distributed generation penetration levels in low voltage networks using a probabilistic approach.” Int. J. Electr. Power Energy Syst., 64, 505–515.
Leite da silva, A. M., Arienti, V. L., and Allan, R. N. (1984). “Probabilistic load flow considering dependence between input nodal powers.” IEEE Trans. Power App. Syst., PAS-103(6), 1524–1530.
Li, W. (2011). “System analysis techniques.” Probabilistic transmission system planning, Wiley, Hoboken, NJ.
Madrigal, M., Ponnambalam, K., and Quintana, V. H. (1998). “Probabilistic optimal power flow.” IEEE Canadian Conf. on Electrical and Computer Engineering, Vol. 1, 385–388.
Miranda, V., Matos, M. A., and Saraiva, J. T. (1990). “Fuzzy load flow new algorithms incorporating uncertain generation and load representation.” Proc., 10th Power System Computation Conf., Graz, Austria, 621–627.
Moeini-Aghtaie, M., Abbaspour, A., and Fotuhi-Firuzabad, M. (2012). “Incorporating large-scale distant wind farms in probabilistic transmission expansion planning—Part I: Theory and algorithm.” IEEE Trans. Power Syst., 27(3), 1585–1593.
Mohammadi, M., Shayegani, A., and Adaminejad, H. (2013). “A new approach of point estimate method for probabilistic load flow.” Int. J. Electr. Power Energy Syst., 51, 54–60.
Morales, J. M., Baringo, L., Conejo, A. J., and Minguez, R. (2010). “Probabilistic power flow with correlated wind sources.” IET Gener. Transm. Distrib., 4(5), 641–651.
Nayeripour, M., Mahboubi-Moghaddam, E., Aghaei, J., and Azizi-Vahed, A. (2013). “Multi-objective placement and sizing of DGs in distribution networks ensuring transient stability using hybrid evolutionary algorithm.” Renewable Sustainable Energy Rev. J., 25, 759–767.
Owuor, J. O., Munda, J. L., and Jimoh, A. A. (2011). “The IEEE 34 node radial test feeder as a simulation test bench for distributed generation.” Proc., IEEE AFRICON, Livingstone, 1–6.
Ramezani, M., Falaghi, H., and Singh, C. (2013). “A deterministic approach for probabilistic TTC evaluation of power systems including wind farm based on data clustering.” IEEE Trans. Sustainable Energy, 4(3), 643–651.
Ramezani, M., Singh, C., and Haghifam, M. R. (2009). “Role of clustering in the probabilistic evaluation of TTC in power systems including wind power generation.” IEEE Trans. Power Syst., 24(2), 849–858.
Rubinstein, R. Y. (1981). Simulation and the Monte Carlo method, Wiley, New York.
Sanabria, L. A., and Dillon, T. S. (1986). “Stochastic power flow using cumulants and von Mises functions.” Int. J. Electr. Power Energy Syst., 8(1), 47–60.
Singh, C., and Kim, Y. (1988). “An efficient technique for reliability analysis of power systems including time dependent sources.” IEEE Trans. Power Syst., 3(3), 1090–1096.
Wu, C., Wen, F., Lou, Y., and Xin, F. (2015). “Probabilistic load flow analysis of photovoltaic generation system with plug-in electric vehicles.” Int. J. Electr. Power Energy Syst., 64, 1221–1228.
Xu, R., and Wunsch, D. (2005). “Survey of clustering algorithms.” IEEE Trans. Neural Networks, 16(3), 645–678.
Zhang, P., and Lee, S. T. (2004). “Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion.” IEEE Trans. Power Syst., 19(1), 676–682.
Zimmerman, R. D., and Murillo-Sánchez, C. E. (2015). “MATPOWER.” 〈http://www.pserc.cornell.edu//matpower/〉.
Zio, E., Delfanti, M., Giorgi, L., Olivieri, V., and Sansavini, G. (2015). “Monte Carlo simulation-based probabilistic assessment of DG penetration in medium voltage distribution networks.” Int. J. Electr. Power Energy Syst., 64, 852–860.
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Published In
Journal of Energy Engineering
Volume 142 • Issue 4 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Mar 31, 2015
Accepted: Sep 22, 2015
Published online: Jan 14, 2016
Discussion open until: Jun 14, 2016
Published in print: Dec 1, 2016
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