An Improved Particle Swarm Optimization for the Combined Heat and Power Dynamic Economic Dispatch Problem

REFERENCES

• Travers, D.L., and Kaye, R., “Dynamic dispatch by constructive dynamic programming,” IEEE Trans. Power Syst., Vol. 13, No. 1, pp. 72–78, 1998.
   Web of Science ®Google Scholar
• Attaviriyanupap, P., Kita, H., Tanaka, E., and Hasegawa, J., “A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function,” IEEE Trans. Power Syst., Vol. 172, pp. 411–416, 2002.
   Web of Science ®Google Scholar
• Victoire, T., and Jeyakumar, A.E., “A modified hybrid EP–SQP approach for dynamic dispatch with valve-point effect,” Int. J. Elect. Power Ener. Syst., Vol. 278, pp. 594–601, 2005.
   Web of Science ®Google Scholar
• Panigrahi, C.K., Chattopadhyay, P.K., Chakrabarti, R.N., and Basu, M., “Simulated annealing technique for dynamic economic dispatch,” Elect. Power Compon. Syst., Vol. 345, pp. 577–586, 2006.
   Web of Science ®Google Scholar
• Yuan, X., Wang, L., Yuan, Y., Zhang, Y., Cao, B., and Yang, B., “A modified differential evolution approach for dynamic economic dispatch with valve-point effects,” Energy Convers. Manag, Vol. 4912, pp. 3447–3453, 2008.
   Web of Science ®Google Scholar
• Yuan, X., Su, A., Yuan, Y., Nie, H., and Wang, L., “An improved PSO for dynamic load dispatch of generators with valve-point effects,” Energy, Vol. 341, pp. 67–74, 2009.
   Web of Science ®Google Scholar
• Hemamalini, S., and Simon, S.P., “Dynamic economic dispatch using artificial immune system for units with valve-point effect,” Int. J. Elec. Power Ener. Syst., Vol. 334, pp. 868–874, 2011.
   Web of Science ®Google Scholar
• Gil, E., Bustos, J., and Rudnick, H., “Short-term hydrothermal generation scheduling model using a genetic algorithm,” IEEE Trans. Power Syst., Vol. 18, No. 4, pp. 1256–1264, 2003.
   Web of Science ®Google Scholar
• Narang, N., Dhillon, J.S., and Kothari, D.P., “Multi-objective short-term hydrothermal generation scheduling using predator–prey optimization,” Elect. Power Compon. Syst., Vol. 4015, pp. 1708–1730, October 2012.
   Web of Science ®Google Scholar
• Madlener, R., and Schmid, C., “Combined heat and power generation in liberalised markets and a carbon-constrained world,” GAIA Ecolog. Perspect. Sci. Soc., Vol. 122, pp. 114–120, 2003.
   Google Scholar
• Basu, M., “Combined heat and power economic dispatch by using differential evolution,” Elect. Power Compon. Syst., Vol. 388, pp. 996–1004, 2010.
   Web of Science ®Google Scholar
• Liu, P., Fu, Y., and Kargarian Marvasti, A., “Multi-stage stochastic optimal operation of energy-efficient building with combined heat and power system,” Elect. Power Compon. Syst., Vol. 423–4, pp. 327–338, March 2014.
   Web of Science ®Google Scholar
• Karami, H., Sanjari, M.J., Tavakoli, A., and Gharehpetian, G.B., “Optimal scheduling of residential energy system including combined heat and power system and storage device,” Elect. Power Compon. Syst., Vol. 418, pp. 765–781, May 2013.
   Web of Science ®Google Scholar
• Rooijers, F.J., and van Amerongen, R.A. M., “Static economic dispatch for co-generation systems,” IEEE Trans. Power Syst., Vol. 93, pp. 1392–1398, 1994.
   Web of Science ®Google Scholar
• Guo, T., Henwood, M.I., and van Ooijen, M., “An algorithm for heat and power dispatch,” IEEE Trans. Power Syst., Vol. 114, pp. 1778–1784, 1996.
   Web of Science ®Google Scholar
• Jubril, A.M., Adediji, A.O., and Olaniyan, O.A., “Solving the combined heat and power dispatch problem: A semi-definite programming approach,” Electr.Power Compon. Syst., Vol. 4012, pp. 1362–1376, August 2012.
   Web of Science ®Google Scholar
• Song, Y.H., and Xuan, Q.Y., “Combined heat and power economic dispatch using genetic algorithm based penalty function method,” Elect. Mach. Power Syst., Vol. 264, pp. 363–372, 1998.
   Web of Science ®Google Scholar
• Song, Y.H., Chou, C.S., and Stonham, T.J., “Combined heat and power dispatch by improved ant colony search algorithm,” Elect. Power Syst. Res., Vol. 522, pp. 115–121, 1999.
   Web of Science ®Google Scholar
• Wong, K.P., and Algie, C., “Evolutionary programming approach for combined heat and power dispatch,” Elect. Power Syst. Res., Vol. 613, pp. 227–232, 2002.
   Web of Science ®Google Scholar
• Vasebi, A., Fesanghary, M., and Bathaee, S.M. T., “Combined heat and power economic dispatch by harmony search algorithm,” Int. J. Elect. Power Ener. Syst., Vol. 29, No. 10, pp. 713–719, 2007.
   Web of Science ®Google Scholar
• Ramesh, V., Jayabarathi, T., Shrivastava, N., and Baska, A., “A novel selective particle swarm optimization approach for combined heat and power economic dispatch,” Elect. Power Compon. Syst., Vol. 3711, pp. 1231–1240, 2009.
   Web of Science ®Google Scholar
• Mohammadi-Ivatloo, B., Moradi-Dalvand, M., and Rabiee, A., “Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients,” Elect. Power Syst. Res., Vol. 95, pp. 9–18, 2013.
   Web of Science ®Google Scholar
• Basu, M., “Artificial immune system for combined heat and power economic dispatch,” Int. J. Elect. Power Ener. Syst., Vol. 43, No. 1, pp. 1–5, 2012.
   Web of Science ®Google Scholar
• Basu, M., “Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II,” Int. J. Elect. Power Ener. Syst., Vol. 53, pp. 135–141, 2013.
   Web of Science ®Google Scholar
• Xia, X., and Elaiw, A.M., “Optimal dynamic economic dispatch of generation: A review,” Elect. Power Syst. Res., Vol. 808, pp. 975–986, 2010.
   Web of Science ®Google Scholar
• Niknam, T., Azizipanah-Abarghooee, R., Roosta, A., and Amiri, B., “A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch,” Energy, Vol. 421, pp. 530–545, 2012.
   Web of Science ®Google Scholar
• Bahmani-Firouzi, B., Farjah, E., and Seifi, A., “A new algorithm for combined heat and power dynamic economic dispatch considering valve-point effects,” Energy, Vol. 521, pp. 320–332, 2013.
   Web of Science ®Google Scholar
• Kennedy, J., and Eberhart, R., “Particle swarm optimization,” Proceedings of IEEE International Conference on Neural Networks (ICNN 95), Vol. 4, No. 2, pp. 1942–1948, Perth, Western Australia, 27 November–1 December 1995.
   Google Scholar
• Shi, Y., and Eberhart, R., “Empirical study of particle swarm optimization,” IEEE Int. Cong. Evolut. Computat., Vol. 3, pp. 101–106, Washington, DC, 6–9 July 1999.
   Google Scholar
• Wood, A.J., and Wollenberg, B.F., Power Generation, Operation and Control, 2nd ed., New York: John Wiley & Sons, pp. 116–118, 1996.
   Google Scholar
• Rong, A., Hakonen, H., and Lahdelma, R., “A dynamic regrouping based sequential dynamic programming algorithm for unit commitment of combined heat and power systems,” Energy Convers. Manag., Vol. 504, pp. 1108–1115, 2009.
   Web of Science ®Google Scholar
• Ng, W.Y., “Generalized generation distribution factors for power system security evaluations,” IEEE Trans. Power Appar. Syst., Vol. PAS-1003, pp. 1001–1005, March 1981.
   Web of Science ®Google Scholar
• Feng, Y., Yao, Y.M., and Wang, A.X., “Comparing with chaotic inertia weights in particle swarm optimization,” Int. Conf. Machine Learn. Cybernet., Vol. 1, pp. 329–333, Hong Kong, 19–22 August 2007.
   Google Scholar
• Caponetto, R., Fortuna, L., Fazzino, S., and Xibilia, M.G., “Chaotic sequences to improve the performance of evolutionary algorithms,” IEEE Trans. Evol. Comp., Vol. 73, pp. 289–304, June 2003.
   Web of Science ®Google Scholar
• dos Santos Coelho, L., and Mariani, V.C., “Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect,” IEEE Trans. Power Syst., Vol. 21, No. 2, pp. 989–996, May 2006.
   Web of Science ®Google Scholar
• Park, J.B., Jeong, Y.W., Shin, J.R., and Lee, K.Y., “An improved particle swarm optimization for non convex economic dispatch problems,” IEEE Trans. Power Syst., Vol. 251, pp. 156–166, 2010.
   Web of Science ®Google Scholar
• Ratnaweera, A., Halgamuge, S.K., and Watson, H.C., “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. Evol. Comp., Vol. 83, pp. 240–255, 2004.
   Web of Science ®Google Scholar
• Deb, K., “An efficient constraint handling method for genetic algorithms,” Comput. Meth. Appl. Mech. Eng., Vol. 1862, pp. 311–338, June 2000.
   Web of Science ®Google Scholar
• Zielinski, K., and Laur, R., “Stopping criteria for a constrained single-objective particle swarm optimization algorithm,” Informatica, Vol. 31, pp. 51–59, 2007.
   Google Scholar
• Vlachogiannis, J.G., and Lee, K.Y., “Determining generator contributions to transmission system using parallel vector evaluated particle swarm optimization,” IEEE Trans. Power Syst., Vol. 204, pp. 1765–1774, November 2005.
   Web of Science ®Google Scholar
• Vaisakh, K., Praveena, P., Rama Mohana Rao, S., and Meah, K., “Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm,” Int. J. Elect. Power Ener. Syst., Vol. 391, pp. 56–67, 2012.
   Web of Science ®Google Scholar