Abstract

Transmission congestion is the major challenge in the operation of competitive power market. Sufficient transmission corridor is necessary for realization of power transaction. This paper proposes an efficient approach for transmission congestion management using the Black Hole Algorithm (BHA). Congestion is relieved by rescheduling of real power from the market clearing schedule. BHA is a recently introduced nature inspired algorithm with less number of parameters. The algorithm is easy for implementation, takes less number of iterations and tuning for a particular application. The strength of the algorithm is validated by comparing its performance with that of Particle Swarm Optimization (PSO) and Big Bang Big Crunch (BBBC) algorithms available in the literature. Modified IEEE-30 and Modified IEEE-57 bus systems are taken for the simulation purpose.

References

• Vries LJ. Capacity allocation in a restructured electricity market: technical and economic evaluation of congestion management methods on interconnectors. In: Proceedings of the 2001 IEEE Porto power tech conference.
• Lommerdal M, Soder L. Simulation of congestion management methods. In: Proceedings of the 2003 Bologna power tech.
• K.L. Lo, Y.S. Yuen, L.A. Snider, Congestion management in deregulated electricity markets, in: IEEE International Conference on Electric Utility Deregulation and Restructuring and Power Technologies 2000, 2000, pp. 47–52.
• Shirmohammadi D, Wollenbarg B, Vojdani A, Sandrin P, Pereira M, Rahimi F, et al. Transmission dispatch and congestion management in the emerging energy market structures. IEEE Trans Power Syst 1998; 13(4):1466–1474.
• Ashwani Kumar, S.C. Srivastava, S.N. Singh, A zonal congestion management approach using real and reactive power rescheduling, IEEE Transactions on Power Systems 19 (1) (2004) 554–562.
• W. W. Hogan, “Contract networks for electric power transmission,” J.Regul. Econ., vol. 4, pp. 211–242, Sept. 1992.
• F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn, Spot Pricing of Electricity. Norwell, MA: Kluwer, 1988.
• H. Chao and S. Peck, “A market mechanism for electric power transmission,” J. Regu. Econ., vol. 10, pp. 25–29, July 1996.
• Yamina HY, Shahidehpour SM. Congestion management coordination in the deregulated power market. Electr Power Syst Res 2003;65(2):119–127.
• R. S. Fang and A. K. David, “Transmission congestion management in an electricity market,” IEEE Trans. Power Syst., vol. 4, pp. 877–883, Aug. 1999.
• H. Glavisch and F. Alvarado, “Management of multiple congested conditions in unbundled operation of power systems,” IEEE Trans. Power Syst., vol. 13, pp. 1013–1019, Aug. 1998.
• H. Singh, S. Hao, and A. Papalexopoulos, “Transmission congestion management in competitive electricity markets,” IEEE Trans. Power Syst., vol. 13, pp. 672–680, May 1998.
• Huang G, Hsieh SC. Fast textured algorithms for optimal delivery problems in deregulated environments. IEEE Trans Power Syst 1998;13 (2):493–500.
• Momoh JA, Zhu JZ. A new approach to optimal power flow with phase shifter. In: IEEE international conference on systems, vol. 5; 1998. p. 4794–4799.
• 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.
• G. Yesuratnam and D. Thukaram, “Congestion management in open access based on relative electrical distances using voltage stability criteria,” Elect. Power Syst. Res., vol. 77, pp. 1608–1618, 2007.
• Talukdar, B. K., Sinha, A. K., Mukhopadhyay, S., and Bose, A., “A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management,” Int. J. Elect. Power Energy Syst., Vol. 27, No. 5, pp. 379–388, June 2005.
• Gnanadas R, Padhy NP, Palanivelu TG. A new method for the transaction congestion management in the restructured power market. J Electr Eng, Electrika 2007;9(1):52–58.
• Panida Boonyaritdachochai, Chanwit Boonchuay, Weerakorn Ongsakul, Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients Computers and Mathematics with Applications 60 (2010) 1068-1077.
• Tulika Bhattacharjee & Ajoy Kumar Chakraborty (2013) NSGAII-based Congestion Management in a Pool-based Electricity Market Incorporating Voltage and Transient Stability, Electric Power Components and Systems, 41:10, 990-1001, DOI: 10.1080/15325008.2013.801055.
• Hazra, J., and Sinha, A. K., “Congestion management using multi objective particle swarm optimization,” IEEE Trans.Power Syst., Vol. 22, No. 4, pp. 1726–1734, November 2007.
• S. Dutta, S.P. Singh, Optimal rescheduling of generators for congestion management based on particle swarm optimization, IEEE Transactions on Power Systems 23 (4) (2008) 1560–1569.
• B.K. Panigrahi , V. Ravikumar Pandi, Congestion management using adaptive bacterial foraging algorithm Energy Conversion and Management 50 (2009) 1202–1209.
• Venkaiah, C., and Vinod Kumar, D. M., “Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators,” Appl. Soft Comput. J., Vol. 11, No. 8, pp. 4921–4930, 2011.
• Abdolreza Hatamlou Black hole: A new heuristic optimization approach for data clustering Information Sciences Information Sciences 222 (2013) 175–184.
• Mostafa Nemati, Reza Salimi, Navid Bazrkar Black Holes Algorithm: A Swarm Algorithm inspired of Black Holes for Optimization Problems IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 2, No. 3, September 2013, pp. 143~150.
• Sujatha balaraman, N. kamaraj, transmission congestion management using particle swarm optimization journal of electrical systems 7-1 (2011): 54-70