75
Views
4
CrossRef citations to date
0
Altmetric
Computers and Computing

A Novel Quasi-Oppositional Learning-Based Chaos-Assisted Sine Cosine Algorithm for Hybrid Energy Integrated Dynamic Economic Emission Dispatch

ORCID Icon, ORCID Icon & ORCID Icon

References

  • “hec.com.hk”, 2021.
  • “clp.com.hk”, 2021.
  • O. A. Al-Shahri, F. B. Ismail, M. A. Hannan, M. S. Hossain Lipu, A. Q. Al-Shetwi, R. A. Begum, N. F. O. Al-Muhsen, and E. Soujeri, “Solar photovoltaic energy optimization methods, challenges and issues: a comprehensive review,” J. Clean. Prod., Vol. 284, p. 125465, 2021.
  • G.-C. Liao, “Integrated isolation niche and immune genetic algorithm for solving bid-based dynamic economic dispatch,” Int. J. Electrical Power Energy Syst., Vol. 42, no. 1, pp. 264–75, 2012.
  • J. Kishore Pattanaik, M. Basu, and D. Prasad Dash, “Improved real coded genetic algorithm for dynamic economic dispatch,” J. Electrical Syst. Inf. Technol., Vol. 5, no. 3, pp. 349–62, 2018.
  • S. Hemamalini, and S. P. Simon, “Dynamic economic dispatch using Maclaurin series based Lagrangian method,” Energy Convers. Manag., Vol. 51, no. 11, pp. 2212–19, 2010.
  • M. Basu, “Particle swarm optimization based goal-attain-ment method for dynamic economic emission dispatch,” Electric Power Compon. Syst., Vol. 34, no. 9, pp. 1015–25, 2006.
  • R. Balamurugan, and S. Subramanian, “Differential evolution-based dynamic economic dispatch of generating units with valve-point effects,” Electric Power Compon. Syst., Vol. 36, no. 8, pp. 828–43, 2008.
  • S. Hemamalini, and S. P. Simon, “Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect,” Eur. Trans. Electrical Power, Vol. 21, no. 1, pp. 70–81, 2010.
  • Y. N. Vijay Kumar, S. Sivanagaraju, and C. V. Suresh, “Analyzing the effect of dynamic loads on economic dispatch in the presence of interline power flow controller using modified bat algorithm,” J. Electrical Syst. Inf. Technol., Vol. 3, no. 1, pp. 45–67, 2016.
  • W.-C. Yeh, M.-F. He, C.-L. Huang, S.-Y. Tan, X. Zhang, Y. Huang, and L. Li, “New genetic algorithm for economic dispatch of stand-alone three-modular microgrid in Dongao Island,” Appl. Energy., Vol. 263, p. 114508, 2020.
  • B. K. Panigrahi, V. Ravikumar Pandi, and S. Das, “Adaptive particle swarm optimization approach for static and dynamic economic load dispatch,” Energy Conversion Manag., Vol. 49, no. 6, pp. 1407–15, 2008.
  • B. Mohammadi-ivatloo, A. Rabiee, and M. Ehsan, “Time-varying acceleration coefficients IPSO for solving dynamic economic dispatch with non-smooth cost function,” Energy Conversion Manag., Vol. 56, pp. 175–83, 2012.
  • K. Mason, J. Duggan, and E. Howley, “Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants,” Neurocomputing, Vol. 270, pp. 188–97, 2017.
  • Z. Zhu, J. Wang, and M. H. Baloch, “Dynamic economic emission dispatch using modified NSGA-II,” Int. Trans. Electrical Energy Syst., Vol. 26, no. 12, pp. 2684–98, 2016.
  • C. X. Guo, J. P. Zhan, and Q. H. Wu, “Dynamic economic emission dispatch based on group search optimizer with multiple producers,” Electric Power Syst. Res., Vol. 86, pp. 8–16, 2012.
  • X. Yuan, L. Wang, Y. Zhang, and Y. Yuan, “A hybrid differential evolution method for dynamic economic dispatch with valve-point effects,” Expert. Syst. Appl., Vol. 36, no. 2, pp. 4042–8, 2009.
  • Y. Lu, J. Zhou, H. Qin, Y. Li, and Y. Zhang, “An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects,” Expert. Syst. Appl., Vol. 37, no. 7, pp. 4842–9, 2010.
  • X. Jiang, J. Zhou, H. Wang, and Y. Zhang, “Dynamic environmental economic dispatch using multiobjective differential evolution algorithm with expanded double selection and adaptive random restart,” Int. J. Electrical Power Energy Syst., Vol. 49, pp. 399–407, 2013.
  • F. Zaman, S. M. Elsayed, T. Ray, and R. A. Sarker, “Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems,” Eng. Appl. Artif. Intell., Vol. 53, pp. 105–125, 2016.
  • V. Ravikumar Pandi, and B. Ketan Panigrahi, “Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm,” Expert. Syst. Appl., Vol. 38, no. 7, pp. 8509–14, 2011.
  • Q. Niu, H. Zhang, K. Li, and G. W. Irwin, “An efficient harmony search with new pitch adjustment for dynamic economic dispatch,” Energy, Vol. 65, pp. 25–43, 2014.
  • Z. Li, D. Zou, and Z. Kong, “A harmony search variant and a useful constraint handling method for the dynamic economic emission dispatch problems considering transmission loss,” Eng. Appl. Artif. Intell., Vol. 84, pp. 18–40, 2019.
  • N. Pandit, A. Tripathi, S. Tapaswi, and M. Pandit, “An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch,” Appl. Soft. Comput., Vol. 12, no. 11, pp. 3500–13, 2012.
  • K. Vaisakh, P. Praveena, S. Rama Mohana Rao, and K. Meah, “Solving dynamic economic dispatch problem with security constraints using bacterial foraging pso-de algorithm,” Int. J. Electrical Power Energy Syst., Vol. 39, no. 1, pp. 56–67, 2012.
  • R. Azizipanah-Abarghooee, “A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch,” Int. J. Electrical Power Energy Syst., Vol. 49, pp. 414–29, 2013.
  • R. Arul, S. Velusami, and G. Ravi, “A new algorithm for combined dynamic economic emission dispatch with security constraints,” Energy, Vol. 79, pp. 496–511, 2015.
  • A. Azizivahed, A. Arefi, E. Naderi, H. Narimani, M. Fathi, and M. Rasoul Narimani, “An efficient hybrid approach to solve bi-objective multi-area dynamic economic emission dispatch problem,” Electr. Power Compon. Syst., Vol. 48, no. 4–5, pp. 485–500, 2020.
  • M. Basu, “Quasi-oppositional group search optimization for multi-area dynamic economic dispatch,” Int. J. Electr. Power Energy Syst., Vol. 78, pp. 356–67, 2016.
  • P. Kumar Roy, and S. Bhui, “A multi-objective hybrid evolutionary algorithm for dynamic economic emission load dispatch,” Int. Trans. Electr. Energy Syst., Vol. 26, no. 1, pp. 49–78, 2015.
  • Y. Wang, J. Zhou, Y. Lu, H. Qin, and Y. Wang, “Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects,” Expert. Syst. Appl., Vol. 38, no. 11, pp. 14231–7, 2011.
  • D. He, G. Dong, F. Wang, and Z. Mao, “Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms,” Energy Conversion Manag., Vol. 52, no. 2, pp. 1026–32, 2011.
  • Y. Lu, J. Zhou, H. Qin, Y. Wang, and Y. Zhang, “Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects,” Eng. Appl. Artif. Intell., Vol. 24, no. 2, pp. 378–87, 2011.
  • R. Arul, G. Ravi, and S. Velusami, “Chaotic self-adaptive differential harmony search algorithm based dynamic economic dispatch,” Int. J. Electr. Power Energy Syst., Vol. 50, pp. 85–96, 2013.
  • K. Dasgupta, P. Kumar Roy, and V. Mukherjee, “A novel oppositional learning-based chaotic sine cosine algorithm for the dynamic thermal–wind economic dispatch problem,” Eng. Optim., Vol. 54, pp. 2104–22, 2021.
  • H. Ma, Z. Yang, P. You, and M. Fei, “Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging,” Energy, Vol. 135, pp. 101–111, 2017.
  • J.-C. Lee, W.-M. Lin, G.-C. Liao, and T.-P. Tsao, “Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system,” Int. J. Electr. Power Energy Syst., Vol. 33, no. 2, pp. 189–97, 2011.
  • C. Peng, H. Sun, J. Guo, and G. Liu, “Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm,” Int. J. Electr. Power Energy Syst., Vol. 42, no. 1, pp. 119–26, 2012.
  • J. Aghaei, T. Niknam, R. Azizipanah-Abarghooee, and J. M. Arroyo, “Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties,” Int. J. Electr. Power Energy Syst., Vol. 47, pp. 351–67, 2013.
  • B. Bahmani-Firouzi, E. Farjah, and R. Azizipanah-Abarghooee, “An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties,” Energy, Vol. 50,pp. 232–44, 2013.
  • M. Basu, “Multi-region dynamic economic dispatch of solar-wind-hydro-thermal power system incorporating pumped hydro energy storage,” Eng. Appl. Artif. Intell., Vol. 86, pp. 182–96, 2019.
  • S. Mirjalili, “Sca: a sine cosine algorithm for solving optimization problems,” Knowl. Based Syst., Vol. 96, pp. 120–33, 2016.
  • M. A. Tawhid, and V. Savsani, “Multi-objective sine-cosine algorithm (mo-sca) for multi-objective engineering design problems,” Neural Comput. Appl., Vol. 31, no. S2, pp. 915–29, 2017.
  • S. Li, H. Fang, and X. Liu, “Parameter optimization of support vector regression based on sine cosine algorithm,” Expert. Syst. Appl., Vol. 91, pp. 63–77, 2018.
  • J. Wang, W. Yang, P. Du, and T. Niu, “A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm,” Energy Conversion Manag., Vol. 163, pp. 134–50, 2018.
  • K. Dasgupta, P. Kumar Roy, and V. Mukherjee, “Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm,” Electric Power Syst. Res., Vol. 178, p. 106018, 2020.
  • S. Saremi, S. Mirjalili, and A. Lewis, “Biogeography-based optimisation with chaos,” Neural Comput. Appl., Vol. 25, no. 5, pp. 1077–97, 2014.
  • C. Paul, P. Kumar Roy, and V. Mukherjee, “Chaotic whale optimization algorithm for optimal solution of combined heat and power economic dispatch problem incorporating wind,” Renew. Energy Focus, Vol. 35, pp. 56–71, 2020.
  • H. R. Tizhoosh, “Opposition-based learning: a new scheme for machine intelligence,” in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), IEEE, 2005.
  • S. Hazra, and P. Kumar Roy., “Quasi-oppositional chemical reaction optimization for combined economic emission dispatch in power system considering wind power uncertainties,” Renew. Energy Focus, Vol. 31, pp. 45–62, 2019.
  • M. Basu, “Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II,” Int. J. Electr. Power Energy Syst., Vol. 30, no. 2, pp. 140–9, 2008.
  • N. Ahmed Khan, A. Bilal Awan, A. Mahmood, S. Razzaq, A. Zafar, and G. Ahmed Sardar Sidhu, “Combined emission economic dispatch of power system including solar photo voltaic generation,” Energy Convers. Manag., Vol. 92, pp. 82–91, 2015.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.