Optimal Rescheduling of Generators for Congestion Management by using Godlike Algorithm
Citation
K. Uma Maheswari , R.M.Sasiraja . "Optimal Rescheduling of Generators for Congestion Management by using Godlike Algorithm", International Journal of Engineering Trends and Technology (IJETT), V10(9),435-440 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Congestion charges can be analyzed in both the cases. In a pool market mode, the sellers (competitive generators) may propose their rise and reduce bid prices in a real-time balancing market. Correspondingly, in the crate of the joint market mode, each operation agreement may contain a reward charge that the purchaser-supplier pair is ready to agree that must its operation be shortened. This is capable of next be designed as a position of the connections dependent on the latter’s sensitivities to the violated constriction in case congestion arises. By applying technique like GODLIKE, we can capable to compute this part of trouble. GODLIKE means for Global Optimum Determination by Linking and Interchanging Kindred Evaluators, and it is right what it does. It applies all four abovementioned techniques concurrently (connecting), and behind junction of either of them, or beyond exact predefined confines, it takes casual members from each inhabitants and apply then into casual other populations (modification) before abiding the optimization. The GODLIKE algorithm was written as an attempt to improve the robustness of the meta-heuristic algorithms, and to do away with the need to ¯ne-tune the algorithm of your choice for each optimization problem. The exchange operator is tremendously valuable for multi-aim troubles; when one inhabitants is totally non-dominated, modifying individuals among inhabitants will frequently result in a conquered inhabitant, which follows the seek for the Pareto front, instead of exposure convergence. GODLIKE does not objective to create either of the techniques more capable in case of function computations, (rather, it tends to involve more function computations).
References
[1] K.Vijayakumar,” Multiobjective optimization methods for congestion management in deregulated power system” Hindawi Publishing Corporation, Journal of electrical and Computer Engineering, volume 2012, Article ID 962402,8 pages.
[2] L. S. Hyman, “Transmission, congestion, pricing, and incentives,”IEEE Power Engineering Review, vol. 19, no. 8, pp. 4–10,1999.
[3] F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn,Spot Pricing of Electricity, Kluwer Academic Publishers, Boston, Mass, USA, 1988.
[4] S. Dutta and S. P. Singh, “Optimal rescheduling of generators for congestion management based on particle swarm optimization,”IEEE Transactions on Power Systems,vol.23,no.4, pp. 1560–1569, 2008.
[5] K. P. Wong and Z. Dong, “Differential evolution, an alternative approach to evolutionary algorithm,” inProceedings of the 13th International Conference on Intelligent Systems Application to Power Systems (ISAP ’05), pp. 73–83, November 2005.
[6] S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Quasi-oppositional differential evolution,” inProceedings of the IEEE Congress on Evolutionary Computation, CEC 2007,pp. 2229–2236, September 2007.
[7] L. dos Santos Coelho and V. C. Mariani, “Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect,”IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 989–996, 2006.
[8] P. Somasundaram and K. Kuppusamy, “Application of evolutionary programming to security constrained economic dispatch,”International Journal of Electrical Power and Energy Systems, vol. 27, no. 5-6, pp. 343–351, 2005.
[9] R. C. Burchett, H. H. Happ, and D. R.Vierath, “Quadratically convergent optimal power flow,” IEEE Transactions on Power Apparatus and Systems, vol. 103, no. 11, pp. 3267-3276, November 1984.
[10] L. S. Vargas, V. H. Quintana, and A.Vannelli, “A tutorial description of an interior point method and its application to security-constrained economic dispatch,” IEEE Transactions on Power Systems, vol. 8, no. 3, pp. 1315-1324, August 1993.
[11] J. A. Momoh, S. X. Guo, E. C. Ogbuobiri, and R. Adapa, “Quadratic interior point method for solving power system optimization problems,”IEEE Transactions on Power Systems, vol. 9, no. 3, pp. 1327-1336, August 1994.
[12] R. S. Fang and A. K. David, “Transmission congestion management in an electricity market,” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 877-883, August 1999.
[13] H. Glatvitsch and F. Alvarado, “Management of multiple congested conditions in unbundled operation of a power system,” IEEE Transactions on Power Systems, vol. 13, no. 3, pp. 1013-1019, August 1998.
[14] D. Shirmohammadi et al., “Transmission dispatch and congestion management in the emerging energy market structures,” IEEE Transactions on Power Systems, vol. 13, no. 4, pp. 1466-1474, November 1998.
[15] K. Bhattacharya, M. H. J.Bollen, and J. E. Daalder, Operation of Restructured Power Systems. Boston: Kluwer Academic Publishers, 2000.
[16] GAMS Release 2.25, A User’s Guide, Washington: GAMS Development Corporation, 2000. [17] N. G. Hingorani, “Flexible AC transmission,” IEEE Spectrum, April 1993, pp. 40-45.
[18] E. J. de Oliveri, J. W. M. Lima, and J. L. R. Pereira, “Flexible AC transmission system devices: Allocation and transmission pricing,” International Journal of Electric Power and Energy Systems., vol. 21, no. 2, pp. 111-118, February 1999.
Keywords
Flexible AC Transmission system(FACTS), unified power flow controller(UPFC),30 bus sytem,godlike algorithm.