Abstract
This paper presents a Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II) solution to Multi-objective Generation Scheduling (MOGS) problem. The MOGS problem involves the decisions with regards to the unit start-up, shut down times and the assignment of the load demands to the committed generating units, considering conflicting objectives such as minimization of system operational cost and minimization of emission release. Through an intelligent encoding scheme, hard constraints such as minimum up/down time constraints are automatically satisfied. For maintaining good diversity in the performance of NSGA-II, the concepts of Dynamic Crowding Distance (DCD) is implemented in NSGA-II algorithm and given the name as MNSGA-II. In order to prove the capability of the proposed approach 10 units, 24-hour test system is considered. The performance of the MNSGA-II are compared with NSGA-II and validated with reference Pareto front generated by conventional weighted sum method using Real Coded Genetic Algorithm (RGA). Numerical results demonstrate the ability of the proposed approach, to generate well distributed pareto front solutions for MOGS problem.
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References
Wood, A.J., Wollenberg, B.F.: Power Generation, Operation and Control, 2nd edn. Wiley, New York (1996)
Yamin, H.Y.: Review on methods of generation scheduling in electric power systems. J. Electr. Power Syst. Res. 69, 227–248 (2004)
Padhy, N.P.: Unit commitment – a bibliographical survey. IEEE Trans. Power Syst. 19, 1196–1205 (2004)
Lee, F.N.: Short-term unit commitment – a new method. IEEE Trans. Power Syst. 3, 691–698 (1988)
Pang, C.K., Sheble, G.B., Albuyeh, F.: Evaluation of dynamic programming based methods and multiple area representation for thermal unit commitment. IEEE Trans. Power Apparatus syst. 100, 1212–1218 (1981)
Virmani, S., Imhof, K., Mukherjee, S.: Implementation of lagrangian based unit commitment problem. IEEE Trans. Power Syst. 10, 772–777 (1995)
Kazarlis, S.A., Bakirtzis, A.G., Petridis, J.: A genetic algorithm solution to the unit commitment problem. IEEE Trans. Power Syst. 11, 83–92 (1996)
Orero, S.O., Irving, M.R.: A genetic algorithm for generation scheduling in power systems. Int. J. Electr. power Energy syst. 18, 19–26 (1996)
Swarup, K.S., Yamashiro, S.: Unit commitment solution methodology using genetic algorithm. IEEE Trans. Power Syst. 17, 87–91 (2002)
Dasgupta, D., Mcgregor, D.R.: Thermal unit commitment using genetic algorithms. IEE Proc. Gen Trans. Dist. 141, 459–465 (1994)
Juste, K.A., Kita, H., Tanaka, E., et al.: An evolutionary programming solution to the unit commitment problem. IEEE Trans. Power Syst. 14, 1452–1459 (1999)
Mantawy, A.H., Abdel-Magid, Y.L., Selim, S.Z.: A simulated annealing algorithm for unit commitment. IEEE Trans. Power Syst. 13, 197–204 (1998)
Mantawy, A.H., Abdel-Magid, Y.L., Selim, S.Z.: Unit commitment by tabu search. Proc. Inst. Elect. Eng. Gen. Trans. Dist. 145, 56–64 (1998)
Saneifard, S., Prasad, N.R., Smolleck, H.: A fuzzy logic approach to unit commitment. IEEE Trans. Power Syst. 12, 988–995 (1997)
Ouyang, Z., Shahidehpour, S.M.: Heuristic muti-area unit commitment with economic dispatch. IEE Proc. 138, 242–252 (1991)
Tong, S.K., Shahidehpour, S.M., Ouyang, Z.: A heuristic short-term unit commitment. IEEE Trans. Power Syst. 6, 1210–1216 (1991)
Wang, C., Shahidehpour, S.M.: Effects of ramp-rate limits on unit commitment and economic dispatch. IEEE Trans. Power Syst. 8, 1341–1350 (1993)
Ongsakul, W., Petcharaks, N.: Unit commitment by enhanced adaptive lagrangian relaxation. IEEE Tran. Power Syst. 19, 620–628 (2004)
Cheng, C.P., Liu, C.W., Liu, C.C.: Unit commitment by lagrangian relaxation and genetic algorithms. IEEE Trans. power Syst. 15, 707–714 (2002)
Mahadevan, K., Kannan, P.S.: Lagrangian relaxation based particle swarm optimization for unit commitment problem. J. Power Energy Syst. 27(4), 320–329 (2007)
Bharathi, R., Kumar, M.J., Sunitha, D., Premalatha, S.: Optimization of combined economic and emission dispatch problem – a comparative study. In: Proceedings of Eighth International Power Engineering Conference, pp. 134–139 (2007)
Dhillon, J.S., Parti, S.C., Kothari, D.P.: Stochastic economic emission dispatch. Electr. Power Syst. Res. 26, 197 (1993)
Deb, K.: Optimization using evolutionary algorithms, 2nd edn, pp. 171–280. Wiley, New York (2001)
Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Generation dispatch based on probability security criteria. IEEE Trans. Power Syst. 3, 317–324 (1988)
Abido, M.A.: Evolutionary algorithms for electric power dispatch problem. IEEE Trans. Evol. Comput. 10(3), 315–329 (2006). doi:10.1109/tevc.2005.857073
Zio, E., Baraldi, P., Pedroni, N.: Optimal power system generation scheduling by multi-objective genetic algorithms with preferences. Reliab. Eng. Syst. Saf. 94, 432–444 (2009)
Li, Y.F., Pedroni, N., Zio, E.: A memetic evolutionary multi-objective optimization method for environmental power unit commitment. IEEE Trans. Power Syst. 28(3), 2660–2669 (2013)
Luo, B., Zheng, J., Xie, J., Wu, J.: Dynamic crowding distance - A new diversity maintenance strategy for MOEAs. In: 4th International Conference on Natural Computation (ICNC 2008), vol. 1, pp. 580–585 (2008)
Dhanalakshmi, S., Kannan, S., Mahadevan, K., Baskar, S.: Application of modified NSGA-II algorithm to combined economic and emission dispatch problem. Int. J. Electr. Power Energy Syst. 33(4), 992–1002 (2011)
Baskar, S., Subbaraj, P., Chidambaram, P.: Application of genetic algorithms to unit commitment problem. IE(I) J. 81, 195–201 (2001)
Dhanalakshmi, S., Kannan, S., Baskar, S., Mahadevan, K.: Intelligent genetic algorithm for generation scheduling under deregulated environment. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 282–289. Springer, Heidelberg (2011)
Manoharan, P.S., Kannan, P.S., Baskar, S., Iruthayarajan, M.W.: Penalty parameter-less constraint handling scheme based evolutionary algorithm solutions to economic dispatch. IET Gener. Transm. Distrib. 2, 478–490 (2008)
Danaraj, R.M.S., Gajendran, F.: Quadratic programming solution to emission and economic dispatch problems. IE (I) J. 86, 129–132 (2005)
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Dhanalakshmi, S., Kannan, S., Baskar, S., Mahadevan, K. (2015). Multi-objective Generation Scheduling Using Modified Non-dominated Sorting Genetic Algorithm- II. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_40
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