Improved Hybrid Biogeography-Based Algorithm for Combined Heat and Power Economic Dispatch with Feasible Operating Region and Energy Saving Potential

References

• M. Habka and S. Ajib, “Determination and evaluation of the operation characteristics for two configurations of combined heat and power systems depending on the heating plant parameters in low-temperature geothermal applications,” Energy Convers. Manage., vol. 76, pp. 996–1008, 2013. DOI: 10.1016/j.enconman.2013.08.046.
   Web of Science ®Google Scholar
• S. D. Beigvand, H. Abdi, and M. La Scala, “Combined heat and power economic dispatch problem using gravitational search algorithm,” Electric Power Syst. Res., vol. 133, pp. 160–172, 2016. DOI: 10.1016/j.epsr.2015.10.007.
   Web of Science ®Google Scholar
• M. Basu, “Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 135–141, 2013. DOI: 10.1016/j.ijepes.2013.04.014.
   Web of Science ®Google Scholar
• B. Mohammadi-Ivatloo, M. Moradi-Dalvand, and A. Rabiee, “Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients,” Electric Power Syst. Res., vol. 95, pp. 9–18, 2013. DOI: 10.1016/j.epsr.2012.08.005.
   Web of Science ®Google Scholar
• A. Bhattacharya and P. K. Chattopadhyay, “Solving complex economic load dispatch problems using biogeography-based optimization,” Expert Syst. Appl., vol. 37, no. 5, pp. 3605–3615, 2010. DOI: 10.1016/j.eswa.2009.10.031.
   Web of Science ®Google Scholar
• K. P. Wong and C. Algie, “Evolutionary programming approach for combined heat and power dispatch,” Electr. Power Syst. Res., vol. 61, no. 3, pp. 227–232, 2002. DOI: 10.1016/S0378-7796(02)00028-7.
   Web of Science ®Google Scholar
• S. Ramesh, S. Kannan, and S. Baskar, “Application of modified NSGA-II algorithm to multi-objective reactive power planning,” Appl. Soft Comput., vol. 12, no. 2, pp. 741–753, 2012. DOI: 10.1016/j.asoc.2011.09.015.
   Web of Science ®Google Scholar
• C. Jena, M. Basu, and C. K. Panigrahi, “Improved differential evolution for combined heat and power economic dispatch,” Int. J. Emerg. Electr. Power Syst., vol. 17, no. 2, pp. 151–163, 2016. DOI: 10.1515/ijeeps-2015-0065.
   Web of Science ®Google Scholar
• L. G. Wang, Y. P. Yang, C. Q. Dong, T. Morosuk, and G. Tsatsaronis, “Parametric optimization of supercritical coal-fired power plants by MINLP and differential evolution,” Energy Convers. Manag., vol. 85, pp. 828–838. 2014. DOI: 10.1016/j.enconman.2014.01.006.
   Web of Science ®Google Scholar
• S. D. Beigvand, H. Abdi, and M. La Scala, “Hybrid gravitational search algorithm-particle swarm optimization with time varying acceleration coefficients for large scale CHPED problem,” Energy, vol. 126, pp. 841–853, 2017. DOI: 10.1016/j.energy.2017.03.054.
   Web of Science ®Google Scholar
• S. M. Zadeh, D. M. W. Powers, K. Sammut, and A. M. Yazdani, “Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management,” J. Mar. Sci. Appl., vol. 15, no. 4, pp. 463–477, 2016. DOI: 10.1007/s11804-016-1382-6.
   Web of Science ®Google Scholar
• D. Du, D. Simon, M. Ergezer, and IEEE. “Biogeography-Based Optimization Combined with Evolutionary Strategy and Immigration Refusal,” 2009 IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 997–1002.
   Google Scholar
• H. Ma and D. Simon, “Blended biogeography-based optimization for constrained optimization,” Eng. Appl. Artif. Intell., vol. 24, no. 3, pp. 517–525, 2011. DOI: 10.1016/j.engappai.2010.08.005.
   Web of Science ®Google Scholar
• P. K. Roy and M. Ghosh, “Combined heat and power dispatch using hybrid genetic algorithm and biogeography-based optimization,” Int. J. Energy Optim. Eng., vol. 6, no. 1, pp. 49–65, 2017. DOI: 10.4018/IJEOE.2017010103.
   Web of Science ®Google Scholar
• V. Garg and K. Deep, “Constrained Laplacian biogeography-based optimization algorithm,” Int. J. Syst. Assur. Eng. Manag., vol. 8, no. S2, pp. 867–885, 2017. DOI: 10.1007/s13198-016-0539-7.
   Google Scholar
• H. W. Mo and Z. Z. Li, IEEE. Bio-geography based differential evolution for robot path planning, 2012.
   Google Scholar
• M. Ergezer, D.Simon, D. Du, and IEEE. Oppositional biogeography-based optimization. 2009 IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 1009–1014.
   Google Scholar
• S. K. Goudos, “A novel generalized oppositional biogeography-based optimization algorithm: Application to peak to average power ratio reduction in OFDM systems,” Open Math., vol. 14, no. 1, pp. 705–722, 2016. DOI: 10.1515/math-2016-0066.
   Google Scholar
• R. K. Yadav and H. Banka, “IBBOMSA: An improved biogeography-based approach for multiple sequence alignment,” Evol. Bioinform., vol. 12, pp. 237–246, 2016.
   PubMed Web of Science ®Google Scholar
• D. Bach Hoang, N. Thang Trung, Q. Nguyen Vu, and D. Le Van, “A novel method for economic dispatch of combined heat and power generation,” Energies, vol. 11, no. 11, 2018. DOI: 10.3390/en11113113.
   PubMed Web of Science ®Google Scholar
• I. Boussaid, A. Chatterjee, P. Siarry, and M. Ahmed-Nacer, “Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO),” Comput. Oper. Res., vol. 38, no. 8, pp. 1188–1198, 2011. DOI: 10.1016/j.cor.2010.11.004.
   Web of Science ®Google Scholar
• P. Rao, “Combined heat and power economic dispatch: A direct solution,” Electr. Power Comp. Syst., vol. 34, no. 9, pp. 1043–1056, 2006. DOI: 10.1080/15325000600596775.
   Web of Science ®Google Scholar
• V. N. Dieu and W. Ongsakul, “Augmented Lagrange—Hopfield network for economic load dispatch with combined heat and power,” Electr Power Comp Syst., vol. 37, no. 12, pp. 1289–1304, 2009. DOI: 10.1080/15325000903054969.
   Web of Science ®Google Scholar
• A. Sashirekha, J. Pasupuleti, N. H. Moin et al., “Combined heat and power (CHP) economic dispatch solved using Lagrangian relaxation with surrogate subgradient multiplier updates,” Int. J. Electr. Power Energy Syst., vol. 44, no. 1, pp. 421–430, 2013. DOI: 10.1016/j.ijepes.2012.07.038.
   Web of Science ®Google Scholar