HSABC Algorithm for Economic Operation Emission Based
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Abstract
The total cost of maintaining the energy infrastructure is one of the most important problems. Technically, this issue considers the fuels and emissions of generating units working within specific parameters in an Economic Operation Emission Based (EOEB). This study evaluates the performance of the Harvest Season Artificial Bee Colony (HSABC) Algorithm in search of the best EOEB solution. To compute the EOEB issue on the IEEE-62 bus system, simulation programming techniques are applied based on HSABC Algorithm. The simulation findings indicate that the investigated approaches have a range of characteristics, speed, starting, and statistical value values.
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References
I. Ahmed, M. Rehan, A. Basit, S. H. Malik, U.-E.-H. Alvi, and K.-S. Hong, (2022). “Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations,” Energy, vol. 261, p. 125178, doi: 10.1016/j.energy.2022.125178. DOI: https://doi.org/10.1016/j.energy.2022.125178
M. Basu, (2023). “Multi-county combined heat and power dynamic economic emission dispatch incorporating electric vehicle parking lot,” Energy, vol. 275, p. 127523, doi: 10.1016/j.energy.2023.127523. DOI: https://doi.org/10.1016/j.energy.2023.127523
S. Bu et al., (2024). “Energy, exergy, environmental, and economic analyses and multiobjective optimization of a DSORC system for waste heat utilization in low-concentration gas power generation,” Energy, vol. 286, p. 129647, doi: 10.1016/j.energy.2023.129647. DOI: https://doi.org/10.1016/j.energy.2023.129647
J. N. R. Araujo and L. S. Batista, (2023). “A diversity-driven migration strategy for distributed evolutionary algorithms,” Swarm Evol. Comput., vol. 82, p. 101361, doi: 10.1016/j.swevo.2023.101361. DOI: https://doi.org/10.1016/j.swevo.2023.101361
E. Y. Avila-Melgar, M. A. Cruz-Chávez, B. Martínez-Bahena, M. L. Eraña-Díaz, and M. H. Cruz-Rosales, (2023). “Parallel evolutionary algorithm for Water Distribution Network Design, using the Masters–Students model in distributed environment,” Appl. Soft Comput., vol. 135, p. 109986, doi: 10.1016/j.asoc.2023.109986. DOI: https://doi.org/10.1016/j.asoc.2023.109986
Y. Abdi, M. Asadpour, and Y. Seyfari, (2023). “μMOSM: A hybrid multi-objective micro evolutionary algorithm,” Eng. Appl. Artif. Intell., vol. 126, p. 107000, doi: 10.1016/j.engappai.2023.107000. DOI: https://doi.org/10.1016/j.engappai.2023.107000
A. N. Afandi, (2014). “Optimal scheduling power generations using HSABC algorithm considered a new penalty factor approach,” in The 2nd IEEE Conference on Power Engineering and Renewable Energy (ICPERE) 2014, Bali, Indonesia: IEEE, pp. 13–18. doi: 10.1109/ICPERE.2014.7067227. DOI: https://doi.org/10.1109/ICPERE.2014.7067227
Y. Chen and E. A. Raad, (2022). “Application of novel economic emission dispatch by considering the benchmark of multi-stage steam turbines, spinning reserve, and emission loss function,” Energy Rep., vol. 8, pp. 8652–8660, doi: 10.1016/j.egyr.2022.06.064. DOI: https://doi.org/10.1016/j.egyr.2022.06.064
S. Divya, M. K. Paramathma, A. Sheela, and S. D. Kumar, (2024). “Hybrid renewable energy source optimization using black widow optimization techniques with uncertainty constraints,” Meas. Sens., vol. 31, p. 100968, doi: 10.1016/j.measen.2023.100968. DOI: https://doi.org/10.1016/j.measen.2023.100968
C. Collados-Rodriguez, M. Cheah-Mane, E. Prieto-Araujo, and O. Gomis-Bellmunt, (2022). “Stability and operation limits of power systems with high penetration of power electronics,” Int. J. Electr. Power Energy Syst., vol. 138, p. 107728, doi: 10.1016/j.ijepes.2021.107728. DOI: https://doi.org/10.1016/j.ijepes.2021.107728
W.-K. Hao, J.-S. Wang, X.-D. Li, H.-M. Song, and Y.-Y. Bao, (2022). “Probability distribution arithmetic optimization algorithm based on variable order penalty functions to solve combined economic emission dispatch problem,” Appl. Energy, vol. 316, p. 119061, doi: 10.1016/j.apenergy.2022.119061. DOI: https://doi.org/10.1016/j.apenergy.2022.119061
A. N. Afandi, I. Fadlika, and Y. Sulistyorini, (2016). “Solution of dynamic economic dispatch considered dynamic penalty factor,” in 2016 3rd Conference on Power Engineering and Renewable Energy (ICPERE), Yogyakarta, Indonesia: IEEE, pp. 241–246. doi: 10.1109/ICPERE.2016.7904870. DOI: https://doi.org/10.1109/ICPERE.2016.7904870
K. Máslo, A. Kasembe, and M. Kolcun, (2016). “Simplification and unification of IEEE standard models for excitation systems,” Electr. Power Syst. Res., vol. 140, pp. 132–138, doi: 10.1016/j.epsr.2016.06.030. DOI: https://doi.org/10.1016/j.epsr.2016.06.030
M. Ahmadipour, M. Murtadha Othman, R. Bo, M. Sadegh Javadi, H. Mohammed Ridha, and M. Alrifaey, (2024). “Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer,” Expert Syst. Appl., vol. 235, p. 121212, doi: 10.1016/j.eswa.2023.121212. DOI: https://doi.org/10.1016/j.eswa.2023.121212
G. H. S. Diniz, V. dos S. Miranda, and B. S. Carmo, (2023). “Dynamic modelling, simulation, and control of hybrid power systems for escort tugs and shuttle tankers,” J. Energy Storage, vol. 72, p. 108091, doi: 10.1016/j.est.2023.108091. DOI: https://doi.org/10.1016/j.est.2023.108091
M. Ahmed, S. H. Kamel, N. H. Abbasy, and Y. Abouelseoud, (2023). “A Gaussian random walk salp swarm algorithm for optimal dynamic charging of electric vehicles,” Appl. Soft Comput., vol. 147, p. 110838, doi: 10.1016/j.asoc.2023.110838. DOI: https://doi.org/10.1016/j.asoc.2023.110838
L. Fan, T. Peng, H. Yu, L. Ding, Y. Zhang, and J. Ma, (2023). “Optimization method of flexible response capability of power system with limited cost constraint,” Energy Rep., vol. 9, pp. 1069–1076, doi: 10.1016/j.egyr.2023.06.044. DOI: https://doi.org/10.1016/j.egyr.2023.06.044