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http://dx.doi.org/10.5370/JEET.2015.10.3.820

A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase  

Wang, Xu (Dept. of Electrical Engineering, Shanghai Jiao Tong University)
Jiang, Chuan-Wen (Dept. of Electrical Engineering, Shanghai Jiao Tong University)
Publication Information
Journal of Electrical Engineering and Technology / v.10, no.3, 2015 , pp. 820-831 More about this Journal
Abstract
In a deregulated electricity market, congestion of the transmission lines is a major problem the independent system operator (ISO) would face. Rescheduling of generators is one of the most practiced techniques to alleviate the congestion. However, not all generators in the system operate deterministically and independently, especially wind power generators (WTGs). Therefore, a novel optimal rescheduling model for congestion management that accounts for the uncertain and correlated power sources and loads is proposed. A probabilistic power flow (PPF) model based on 2m+1 point estimate method (PEM) is used to simulate the performance of uncertain and correlated input random variables. In addition, the impact of direct electricity purchase contracts on the congestion management has also been studied. This paper uses artificial bee colony (ABC) algorithm to solve the complex optimization problem. The proposed algorithm is tested on modified IEEE 30-bus system and IEEE 57-bus system to demonstrate the impacts of the uncertainties and correlations of the input random variables and the direct electricity purchase contracts on the congestion management. Both pool and nodal pricing model are also discussed.
Keywords
PPF; Congestion Management; ABC; 2m+1 PEM; Direct electricity purchase;
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1 K. L. Lo, Y. S. Yuen, and L. A. Snider, “Congestion management in deregulated electricity markets,” in Proc. Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies, London, U.K., 2000, pp. 47-52.
2 Stoft S., “Power system economics: designing market for electricity”. IEEE Press & Wiley-Interscience, New York, USA, 2002.
3 Ashwani Kumar, S. C. Srivastava and S. N. Singh, “A zonal congestion management approach using real and reactive power rescheduling,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 554-562, Feb. 2004.
4 Sudipta Dutta and S. P. Singh, “Optimal rescheduling of generators for congestion management based on particle swarm optimization,” IEEE Trans. Power Syst., vol. 23, no. 4, pp. 1560-1569, Nov. 2008.   DOI   ScienceOn
5 Singh H., Hao S. and Papalexoplulos A., “Transmission congestion management in competitive electricity markets,” IEEE Trans. Power Syst., vol. 13, no. 2, pp. 672-680, May 1998.   DOI   ScienceOn
6 F. Jian and J. W. Lamont, “A combined framework for service identification and congestion management,” IEEE Trans. Power Syst., vol.16, no. 1, pp. 56-61, Feb. 2001.   DOI   ScienceOn
7 P. Zhang, S. T. Lee, “Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 676-682, Feb. 2004.
8 Gregor Verbiˇc and Claudio A. Cañizares, “Probabilistic optimal power flow in electricity markets based on a two-point estimate method,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1883-1893, Nov. 2006.   DOI   ScienceOn
9 Juan. M. Morales and Juan. Pérez-Ruiz, “Point estimate schemes to solve the probabilistic power flow,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1594-1601, Nov. 2007.   DOI   ScienceOn
10 Deb, S., and Goswami, A.K, "Congestion management by generator rescheduling using artificial bee colony optimization technique," 2012 Annual IEEE India Conference, INDICON 2012, pp. 909-914.
11 J.M. Morales, L. Baringo, A.J. Conejo and R. Mı´nguez, “Probabilistic power flow with correlated wind sources,” IET Gener. Transm. Distrib., vol. 4, iss. 5, pp. 641-651, 2010.   DOI   ScienceOn
12 D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
13 Karaboga D, and Basturk B, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 8, iss. 1, pp. 687-697, Jan. 2008.   DOI   ScienceOn
14 Kiliç, U., and Ayan, K, “Optimizing power flow of AC-DC power systems using artificial bee colony algorithm,” International Journal of Electrical Power and Energy Systems, vol. 53, iss. 1, pp. 592-602, 2013.   DOI   ScienceOn
15 H. S. Jung, D. Hur, and J. K. Park, “Congestion cost allocation method in a pool model,” Generation, Transmission and Distribution, IEE Proceedings, vol. 150, pp. 604-610, 2003.   DOI   ScienceOn
16 M.S. Kumar and C.P. Gupta, “Congestion management in a pool model with bilateral contract by generation rescheduling based on PSO,” in Advances in Power Conversion and Energy Technologies (APCET), 2012 International Conference on, 2012, pp. 1-6.
17 Yan Chen, Jinyu Wen, Shijie Cheng, “Probabilistic load flow method based on Nataf transformation and Latin Hypercube Sampling,” IEEE Trans. Sustainable Energy, vol. 4, no. 2, pp. 294-301, Apr. 2013.   DOI   ScienceOn
18 Kaigui Xie, Roy Billinton, “Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique,” Electr. Power Syst. Res., vol. 79, iss. 4, pp. 687-693, Apr. 2009.   DOI   ScienceOn
19 H. Yu, C.Y. Chung, K.P. Wong, H.W. Lee, and J.H. Zhang, “Probabilistic load flow evaluation with hybrid Latin hypercube sampling and Cholesky decomposition,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 661-667, May 2009.   DOI   ScienceOn
20 Y.Yuan, J. Zhou, P. Ju, J. Feuchtwang, “Probabilistic load flow computation of a power system containing wind farms using the method of combined cumulants and Gram-Charlier expansion,” IET Renewable Power Generation, vol. 5, iss. 6, pp. 448-454, Nov. 2011.   DOI   ScienceOn
21 Peiling Liu and Armen Der Kiureghian, “Multivariate distribution models with prescribed marginal and covariances,” Probab. Eng. Mech., vol. 1, iss. 2, pp. 105-112, Jun. 1986.   DOI   ScienceOn
22 K. Chandrasekaran and S.P. Simon, “Multi-objective unit commitment problem with reliability function using fuzzified binary real coded artificial bee colony algorithm,” IET Gener. Transm. Distrib., vol. 6, iss. 10, pp. 1060-1073, 2012.   DOI   ScienceOn
23 B. Basturk and D. Karaboga, “An artificial bee colony (ABC) algorithm for numeric function optimization,” in Proc. IEEE Swarm Intell. Symp., Indianapolis, IN, May 12-14, 2006.
24 D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” J. Global Optimiz., vol. 39, pp. 459-471, 2007.   DOI
25 Quanke Pan, Ling Wang and Kun Mao etc., “An Effective Artificial Bee Colony Algorithm for a Real-World Hybrid Flowshop Problem in Steelmaking Process,” IEEE Trans. Automation Science and Engineering, vol. 10, no. 2, pp. 307-322, Apr. 2013.   DOI   ScienceOn
26 R.S. Rao, S.V.L. Narasimham, and M. Ramalingaraju, “Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm,” Int. J. Elect. Power Energy Syst. Eng., vol. 1, no. 2, pp. 116-122, 2008.
27 D. Karaboga, B. B. Akay, and C. Ozturk, “Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks,” Lect. Notes Comput. Sci.: Modeling Decisions for Artif. Intell., vol. 4617, pp. 318-319, 2007.
28 F.S. AbuMouti, M.E. ElHawary, “Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm,” IEEE Trans. Power Delivery, vol. 26, no. 4, pp. 2090-2101, Oct. 2011.   DOI   ScienceOn
29 R. W. Ferrero, S. M. Shahidehpour and V. C. Ramesh, “Transaction analysis in deregulated power systems using game theory”, IEEE Trans. Power Syst., vol. 12, no. 3, pp. 1340-1347, Aug. 1997.   DOI   ScienceOn
30 O. Alsac and B. Stott, “Optimal load flow with steady state security”, IEEE Trans. Power Syst., vol. 93, no. 3, pp. 745-751, 1974.
31 L. L. Freris and A. M. Sasson, “Investigation of the load flow problem,” Proc. Inst. Elect. Eng., vol. 115, no. 10, pp. 1459-1466, 1968.   DOI
32 D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Pub. Co., 1989.