Two-Point Estimate Method for Probabilistic Optimal Power Flow Computation Including Wind Farms with Correlated Parameters - Intelligent Computing for Sustainable Energy and Environment - page 423

7Conluon
2PEM combined with path following interior point method is then employed to
solve the POPF including wind farms with correlated parameters. The simu-
lation results show that: (i) The effectiveness and feasibility of the 2PEM are
confirmed to solve the POPF including wind farms with correlated parameters.
(ii) the superiority of the proposed method is that it has higher calculation pre-
cision and requires less execution time to solve the POPF than the MCS method
dramatically. (iii) After considering the correlated parameters, some impacts on
the power system occur, especially for the nodal injection real power. Thus, the
POPF including wind farms with correlated parameters can be used as an e-
cacious analytical tool for the researchers to address long term studies such as
transmission congestion and reliability analysis in the power system.
Acknowledgment. This work was supported in part by the National Science
Foundation of China under Grant No. 51007052 and in part by the innovation
fund project for graduate students of Shanghai University No. SHUCX120091.
References
1. Zhang, H., Li, P.: Probabilistic Analysis for Optimal Power Flow Under Uncer-
tainty. IET Generation, Transmission & Distribution 4(5), 553-561 (2010)
2. Morales, J.M., Perez-Ruiz, J.: Point Estimate Schemes to Solve the Probabilistic
Power Flow. IEEE Transactions on Power Systems 22(4), 1594-1601 (2007)
3. Li, X., Li, Y.Z., Zhang, S.H.: Analysis of Probabilistic Optimal Power Flow Tak-
ing Account of the Variation of Load Power. IEEE Transactions on Power Sys-
tems 23(3), 992-999 (2008)
4. Chemozhukov, V.: Rearranging Edgeworth-Cornish-Fisher Expansions. Institute
for Fiscal Studies and Massachusetts Institute of Technology 42(2), 419-435 (2010)
5. Li, X., Li, Y.Z., Li, H.Y.: Comparison and Analysis of Several Probabilistic Power
Flow Algorithms. Proceedings of the CSU-EPSA 21(3), 12-17 (2009)
6. Morales, J.M., Baringo, L., Conejo, A.J., Minguze, R.: Probabilistic Power Flow
with Correlated Wind Sources. IET Generation, Transmission, & Distribution 4(5),
641-651 (2010)
7. Usaola, J.: Probabilistic Load Flow with Correlated Wind Power Injections. Elec-
tric Power Systems Research 80(5), 528-536 (2010)
8. Papaefthymiou, G., Pinson, P.: Modeling of Spatial Dependence in Wind Power
Forecast Uncertainty. In: Probabilistic Methods Applied to Power Systems
(PMAPS) International Conference, Mayaguez, pp. 1-9 (2008)
9. Li, X., Du, D.J., Pei, J.X., Menhas, M.I.: Probabilistic Load Flow Calculation
with Latin Hypercube Sampling Applied to Grid-Connected Induction Wind Power
System. Transactions of the Institute of Measurement and Control, 1-10 (2011),
doi:10.1177/0142331211410101
10. Morales, J.M., Conejo, A.J., Perez-Ruiz, J.: Simulating the impact of Wind Produc-
tion on Locational Marginal Prices. IEEE Transactions on Power Systems 26(2),
820-828 (2011)
11. Li, X., Cao, J., Du, D.J.: Comparison of Levenberg-Marquardt Method and Path
Following Interior Point Method for the Solution of Optimal Power Flow Problem.
International Journal of Emerging Electric Power Systems 13(3), 1-21 (2012)