Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems
This study proposes improved tunicate swarm algorithm (ITSA) for solving and optimizing the dynamic economic emission dispatch (DEED) problem. The DEED optimization target is to reduce the fuel cost and pollutant emission of the power system. In addition, DEED is a complex optimization problem and c...
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| Published in | Applied soft computing Vol. 108; p. 107504 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This study proposes improved tunicate swarm algorithm (ITSA) for solving and optimizing the dynamic economic emission dispatch (DEED) problem. The DEED optimization target is to reduce the fuel cost and pollutant emission of the power system. In addition, DEED is a complex optimization problem and contains multiple optimization goals. To strengthen the ability of the ITSA algorithm for solving DEED, the tent mapping is employed to generate initial population for improving the directionality in the optimization process. Meanwhile, the gray wolf optimizer is used to generate the global search vector for improving global exploration ability, and the Levy flight is introduced to expand the search range. Three test systems containing 5, 10 and 15 generator units are employed to verify the solving performance of ITSA. The test results show that the ITSA algorithm can provide a competitive scheduling plan for test systems containing different units. ITSA proposed algorithm gives the optimal economic and environmental dynamic dispatch scheme for achieving more precise dispatch strategy.
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•This study proposes ITSA to solve the dynamic economic emission dispatch problem in power system.•The Tent mapping is used to generate initial population for improving the ITSA directionality in the optimization process.•The gray wolf optimizer is used to generate the global search vector for improving global ITSA optimization ability.•The Levy flight is introduced to expand the ITSA search range.•The results show that the ITSA has better optimization ability and stability. |
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| AbstractList | This study proposes improved tunicate swarm algorithm (ITSA) for solving and optimizing the dynamic economic emission dispatch (DEED) problem. The DEED optimization target is to reduce the fuel cost and pollutant emission of the power system. In addition, DEED is a complex optimization problem and contains multiple optimization goals. To strengthen the ability of the ITSA algorithm for solving DEED, the tent mapping is employed to generate initial population for improving the directionality in the optimization process. Meanwhile, the gray wolf optimizer is used to generate the global search vector for improving global exploration ability, and the Levy flight is introduced to expand the search range. Three test systems containing 5, 10 and 15 generator units are employed to verify the solving performance of ITSA. The test results show that the ITSA algorithm can provide a competitive scheduling plan for test systems containing different units. ITSA proposed algorithm gives the optimal economic and environmental dynamic dispatch scheme for achieving more precise dispatch strategy.
[Display omitted]
•This study proposes ITSA to solve the dynamic economic emission dispatch problem in power system.•The Tent mapping is used to generate initial population for improving the ITSA directionality in the optimization process.•The gray wolf optimizer is used to generate the global search vector for improving global ITSA optimization ability.•The Levy flight is introduced to expand the ITSA search range.•The results show that the ITSA has better optimization ability and stability. |
| ArticleNumber | 107504 |
| Author | Lim, Ming K. Liu, Zhi-Feng Zheng, Sheng-Jie Tseng, Ming-Lang Li, Ling-Ling |
| Author_xml | – sequence: 1 givenname: Ling-Ling surname: Li fullname: Li, Ling-Ling email: lilinglinglaoshi@126.com organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China – sequence: 2 givenname: Zhi-Feng surname: Liu fullname: Liu, Zhi-Feng email: tjliuzhifeng@126.com organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China – sequence: 3 givenname: Ming-Lang surname: Tseng fullname: Tseng, Ming-Lang email: tsengminglang@asia.edu.tw, tsengminglang@gmail.com organization: Institute of Innovation and Circular Economy, Asia University, Taiwan – sequence: 4 givenname: Sheng-Jie surname: Zheng fullname: Zheng, Sheng-Jie email: hebutzsj@gmail.com organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China – sequence: 5 givenname: Ming K. surname: Lim fullname: Lim, Ming K. email: ac2912@coventry.ac.uk organization: Faculty Research Centre for Business in Society, Coventry University, United Kingdom |
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| Cites_doi | 10.3233/IFS-141126 10.1049/iet-gtd.2018.5364 10.1016/j.asoc.2017.06.041 10.1049/iet-gtd.2018.0369 10.1002/2050-7038.12026 10.1142/S0219876218500895 10.1016/j.epsr.2008.01.012 10.4249/scholarpedia.1486 10.1108/IMDS-04-2017-0145 10.1007/s00521-016-2481-7 10.1002/etep.2530 10.1016/j.asoc.2019.106044 10.1002/ese3.827 10.1287/mnsc.45.5.748 10.1016/j.ijepes.2007.06.009 10.1007/s00521-018-3399-z 10.1016/j.epsr.2006.11.012 10.1016/j.asoc.2020.107061 10.1109/JAS.2017.7510454 10.1007/s40998-018-0158-1 10.1016/j.neucom.2017.03.086 10.1016/j.ijepes.2018.03.019 10.1016/j.ijepes.2018.02.021 10.1049/iet-gtd.2017.0257 10.5370/JEET.2008.3.4.476 10.1016/j.energy.2019.07.131 10.1016/0167-8191(90)90086-O 10.1371/journal.pone.0185454 10.3390/en14051222 10.1080/15325000490195871 10.1016/j.engappai.2019.05.005 10.1504/IJHST.2021.117554 10.1016/j.engappai.2020.103541 10.9790/1676-110502141148 10.1109/ACCESS.2020.2995213 10.1080/15325000500241225 10.1002/etep.2683 10.5755/j01.eie.23.5.19267 10.1007/s00500-020-04861-4 10.1016/j.eswa.2021.114607 10.1016/j.ijepes.2015.11.121 10.1007/s00521-019-04151-7 10.1016/j.asej.2016.03.001 10.1109/59.336133 10.1080/15325000600596759 |
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| Keywords | Power system Improved tunicate swarm algorithm Soft-computing Fuel cost Dynamic economic emission dispatch |
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| References | Li, Sang, Pan, Duan, Gao (b4) 2017; 6 Shen, Zou, Duan, Zhang (b10) 2019; 186 Edwin Selva Rex, Beno, Annrose (b2) 2019; 28 Dey, Roy, Bhattacharyya (b21) 2018; 22 Basu (b40) 2007; 8 Wang, Singh (b13) 2007; 77 Jevtic, Jovanovic, Radosavljevic, Klimenta (b20) 2017; 23 Li, Chang, Tseng, Liu, Lim (b44) 2020; 270 Sen, Mathur (b36) 2016; 78 Wang, Singh (b17) 2008; 78 Liang, Liu, Li, Shen (b5) 2018; 101 Li, Zou, Kong (b34) 2019; 84 Bai, Wu, Xia (b51) 2021; 9 Basu (b29) 2004; 32 Mason, Duggan, Howley (b46) 2018; 100 Mahdi, Vasant, Abdullah-Al-Wadud, Kallimani, Watada (b6) 2018; 31 Zhang, Lin, Mao, Liu, Dou, Liu (b42) 2021; 101 Karthik, Parvathy, Arul (b12) 2019; 29 Mason, Duggan, Howley (b49) 2017; 270 Muthusamy, Ravindran, Yaacob, Polat (b41) 2021; 172 Dorigo, Oca, Engelbrecht (b26) 2008; 3 Basu (b30) 2006; 34 Hardiansyah, Junaidi, Yandri (b16) 2016; 11 Gupta, Swarnkar, Wadhwani, Wadhwani (b15) 2012; 49 Mousavi, Akhondali, Naseri, Eslamian, Saadati (b25) 2021 Gherbi, Lakdja, Bouzeboudja, Gherbi (b38) 2019; 31 Chinnadurrai, Victoire (b47) 2020; 8 Muralidharan, Srikrishna, Subramanian (b24) 2006; 34 Maity, Banerjee, Chanda (b7) 2018; 43 Baziar, Rostami, Akbari-Zadeh (b14) 2014; 27 Alrefaei, Andradóttir (b28) 1999; 45 Liu, Zhu, Jiang (b50) 2018; 12 Qian, Wu, Xu (b35) 2020 Silva Chavez, Zamora-Mendez, Arrieta Paternina, Yrena Heredia, Cardenas-Javier (b39) 2019; 177 Kheshti, Kang, Li, Regulski, Terzija (b8) 2017; 12 Li, Yang, Tseng, Wang, Wu, Lim (b3) 2018; 118 Basu (b48) 2008; 30 Momoh, Guo, Ogbuobiri, Adapa (b23) 1994; 9 Daryani, Zare (b32) 2016; 9 Kenan Dosoglu, Guvenc, Duman, Sonmez, Tolga Kahraman (b19) 2016; 29 Elsheakh, Zou, Ma, Zhang (b22) 2018; 12 Rajagopalan, Kasinathan, Nagarajan, Ramachandaramurthy, Sengoden, Alavandar (b9) 2019; 29 Tudose, Picioroaga, Sidea, Bulac (b43) 2021; 14 Alomoush, Oweis (b1) 2018; 28 Kaur, Awasthi, Sangal, Dhiman (b18) 2020; 90 Naderi, Azizivahed, Narimani, Fathi, Narimani (b37) 2017; 61 Jayabarathi, Ramesh, Kothari, Pavan, Thumbi (b11) 2008; 3 Zhang, Lei, Wang, Yue, Xie (b45) 2017; 12 Vijay (b33) 2018; 15 Whitley, Starkweather, Bogart (b27) 1990; 14 Hagh, Kalajahi, Ghorbani (b31) 2020; 88 Momoh (10.1016/j.asoc.2021.107504_b23) 1994; 9 Sen (10.1016/j.asoc.2021.107504_b36) 2016; 78 Dorigo (10.1016/j.asoc.2021.107504_b26) 2008; 3 Muthusamy (10.1016/j.asoc.2021.107504_b41) 2021; 172 Gupta (10.1016/j.asoc.2021.107504_b15) 2012; 49 Chinnadurrai (10.1016/j.asoc.2021.107504_b47) 2020; 8 Basu (10.1016/j.asoc.2021.107504_b48) 2008; 30 Li (10.1016/j.asoc.2021.107504_b44) 2020; 270 Qian (10.1016/j.asoc.2021.107504_b35) 2020 Liu (10.1016/j.asoc.2021.107504_b50) 2018; 12 Maity (10.1016/j.asoc.2021.107504_b7) 2018; 43 Mahdi (10.1016/j.asoc.2021.107504_b6) 2018; 31 Elsheakh (10.1016/j.asoc.2021.107504_b22) 2018; 12 Shen (10.1016/j.asoc.2021.107504_b10) 2019; 186 Dey (10.1016/j.asoc.2021.107504_b21) 2018; 22 Edwin Selva Rex (10.1016/j.asoc.2021.107504_b2) 2019; 28 Liang (10.1016/j.asoc.2021.107504_b5) 2018; 101 Li (10.1016/j.asoc.2021.107504_b4) 2017; 6 Kenan Dosoglu (10.1016/j.asoc.2021.107504_b19) 2016; 29 Alrefaei (10.1016/j.asoc.2021.107504_b28) 1999; 45 Kheshti (10.1016/j.asoc.2021.107504_b8) 2017; 12 Jayabarathi (10.1016/j.asoc.2021.107504_b11) 2008; 3 Daryani (10.1016/j.asoc.2021.107504_b32) 2016; 9 Rajagopalan (10.1016/j.asoc.2021.107504_b9) 2019; 29 Karthik (10.1016/j.asoc.2021.107504_b12) 2019; 29 Basu (10.1016/j.asoc.2021.107504_b29) 2004; 32 Kaur (10.1016/j.asoc.2021.107504_b18) 2020; 90 Zhang (10.1016/j.asoc.2021.107504_b45) 2017; 12 Wang (10.1016/j.asoc.2021.107504_b13) 2007; 77 Baziar (10.1016/j.asoc.2021.107504_b14) 2014; 27 Tudose (10.1016/j.asoc.2021.107504_b43) 2021; 14 Wang (10.1016/j.asoc.2021.107504_b17) 2008; 78 Bai (10.1016/j.asoc.2021.107504_b51) 2021; 9 Jevtic (10.1016/j.asoc.2021.107504_b20) 2017; 23 Mousavi (10.1016/j.asoc.2021.107504_b25) 2021 Hardiansyah (10.1016/j.asoc.2021.107504_b16) 2016; 11 Li (10.1016/j.asoc.2021.107504_b3) 2018; 118 Basu (10.1016/j.asoc.2021.107504_b40) 2007; 8 Muralidharan (10.1016/j.asoc.2021.107504_b24) 2006; 34 Zhang (10.1016/j.asoc.2021.107504_b42) 2021; 101 Alomoush (10.1016/j.asoc.2021.107504_b1) 2018; 28 Whitley (10.1016/j.asoc.2021.107504_b27) 1990; 14 Li (10.1016/j.asoc.2021.107504_b34) 2019; 84 Vijay (10.1016/j.asoc.2021.107504_b33) 2018; 15 Basu (10.1016/j.asoc.2021.107504_b30) 2006; 34 Silva Chavez (10.1016/j.asoc.2021.107504_b39) 2019; 177 Mason (10.1016/j.asoc.2021.107504_b49) 2017; 270 Naderi (10.1016/j.asoc.2021.107504_b37) 2017; 61 Mason (10.1016/j.asoc.2021.107504_b46) 2018; 100 Gherbi (10.1016/j.asoc.2021.107504_b38) 2019; 31 Hagh (10.1016/j.asoc.2021.107504_b31) 2020; 88 |
| References_xml | – volume: 15 year: 2018 ident: b33 article-title: Quorum sensing driven Bacterial Swarm Optimization to Solve Practical Dynamic Power Ecological Emission Economic dispatch publication-title: Int. J. Comput. Methods – volume: 84 start-page: 18 year: 2019 end-page: 40 ident: b34 article-title: A harmony search variant and a useful constraint handling method for the dynamic economic emission dispatch problems considering transmission loss publication-title: Eng. Appl. Artif. Intell. – volume: 101 start-page: 103 year: 2018 end-page: 115 ident: b5 article-title: A multiobjective hybrid bat algorithm for combined economic/emission dispatch publication-title: Int. J. Electr. Power Energy Syst. – volume: 22 start-page: 55 year: 2018 end-page: 66 ident: b21 article-title: Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms publication-title: Eng. Sci. Technol. Int. J. – year: 2021 ident: b25 article-title: Evaluation of whale and Particle Swarm Optimization Algorithms in Optimal Allocation of water resources of irrigation network to maximize net benefit case study: Salman Farsi publication-title: Int. J. Hydrol. Sci. Technol. – volume: 27 start-page: 1601 year: 2014 end-page: 1607 ident: b14 article-title: An intelligent approach based on bat algorithm for solving economic dispatch with practical constraints publication-title: J. Intell. Fuzzy Systems – volume: 177 year: 2019 ident: b39 article-title: A hybrid optimization framework for the non-convex economic dispatch problem via meta-heuristic algorithms publication-title: Electr. Power Syst. Res. – volume: 31 start-page: 8547 year: 2019 end-page: 8559 ident: b38 article-title: Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem publication-title: Neural Comput. Appl. – volume: 12 start-page: 25 year: 2017 ident: b45 article-title: Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty publication-title: Plos One – volume: 6 start-page: 1240 year: 2017 end-page: 1250 ident: b4 article-title: Solving multi-area environmental economic dispatch by Pareto-based chemical-reaction optimization algorithm publication-title: IEEE/CAA J. Automat. Sinica – volume: 49 start-page: 1 year: 2012 end-page: 6 ident: b15 article-title: Combined economic emission dispatch problem of thermal generating units using particle swarm optimization publication-title: Int. J. Comput. Appl. – volume: 11 start-page: 141 year: 2016 end-page: 148 ident: b16 article-title: Combined economic Emission Dispatch Solution using Simulated Annealing Algorithm publication-title: IOSR J. Electr. Electron. Eng. – volume: 8 start-page: 94678 year: 2020 end-page: 94696 ident: b47 article-title: Dynamic economic emission dispatch considering wind uncertainty using non-Dominated Sorting Crisscross Optimization publication-title: IEEE Access – volume: 270 start-page: 188 year: 2017 end-page: 197 ident: b49 article-title: Multi-objective dynamic economic emission dispatch using particle swarm optimization variants publication-title: Neurocomputing – volume: 43 start-page: 77 year: 2018 end-page: 90 ident: b7 article-title: Bare bones teaching learning-based Optimization Technique for Economic Emission Load Dispatch Problem Considering Transmission losses publication-title: Iran. J. Sci. Technol. Trans. Electr. Eng. – volume: 34 start-page: 343 year: 2006 end-page: 353 ident: b24 article-title: Emission Constrained Economic Dispatch—A new recursive approach publication-title: Electr. Power Compon. Syst. – volume: 32 start-page: 163 year: 2004 end-page: 173 ident: b29 article-title: An interactive fuzzy satisfying-based simulated annealing technique for economic emission load dispatch with non-smooth fuel cost and emission level functions publication-title: Electr. Power Compon. Syst. – volume: 34 start-page: 1015 year: 2006 end-page: 1025 ident: b30 article-title: Particle Swarm Optimization based Goal-attainment method for Dynamic Economic Emission Dispatch publication-title: Electr. Power Compon. Syst. – volume: 29 year: 2019 ident: b12 article-title: Multi-objective economic emission dispatch using interior search algorithm publication-title: Int. Trans. Electr. Energy Syst. – volume: 100 start-page: 201 year: 2018 end-page: 221 ident: b46 article-title: A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch publication-title: Int. J. Electr. Power Energy Syst. – volume: 172 year: 2021 ident: b41 article-title: An improved elephant herding optimization using sine-cosine mechanism and opposition based learning for global optimization problems publication-title: Expert Syst. Appl. – volume: 9 start-page: 316 year: 2021 end-page: 329 ident: b51 article-title: An enhanced multi-objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power publication-title: Energy Sci. Eng. – volume: 28 year: 2018 ident: b1 article-title: Environmental-economic dispatch using stochastic fractal search algorithm publication-title: Int. Trans. Electr. Energy Syst. – volume: 14 start-page: 1222 year: 2021 ident: b43 article-title: Solving single- and multi-objective optimal reactive power dispatch problems using an Improved Salp Swarm Algorithm publication-title: Energies – volume: 8 year: 2007 ident: b40 article-title: Dynamic economic Emission Dispatch using Evolutionary Programming and Fuzzy Satisfying Method publication-title: Int. J. Emerging Electr. Power Syst. – volume: 29 start-page: 721 year: 2016 end-page: 737 ident: b19 article-title: Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems publication-title: Neural Comput. Appl. – volume: 61 start-page: 1186 year: 2017 end-page: 1206 ident: b37 article-title: A comprehensive study of practical economic dispatch problems by a new hybrid evolutionary algorithm publication-title: Appl. Soft Comput. – volume: 78 start-page: 1466 year: 2008 end-page: 1476 ident: b17 article-title: Stochastic economic emission load dispatch through a modified particle swarm optimization algorithm publication-title: Electr. Power Syst. Res. – year: 2020 ident: b35 article-title: An improved particle swarm optimization with clone selection principle for dynamic economic emission dispatch publication-title: Soft Comput. – volume: 3 start-page: 1486 year: 2008 ident: b26 article-title: Particle swarm optimization publication-title: Scholarpedia – volume: 12 start-page: 104 year: 2017 end-page: 116 ident: b8 article-title: Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints publication-title: IET Gener. Transm. Distrib. – volume: 3 start-page: 476 year: 2008 end-page: 483 ident: b11 article-title: Hybrid differential evolution technique for economic dispatch problems publication-title: J. Electr. Eng. Technol. – volume: 12 start-page: 3844 year: 2018 end-page: 3851 ident: b22 article-title: Decentralised gradient projection method for economic dispatch problem with valve point effect publication-title: IET Gener. Transm. Distrib. – volume: 77 start-page: 1654 year: 2007 end-page: 1664 ident: b13 article-title: Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm publication-title: Electr. Power Syst. Res. – volume: 88 year: 2020 ident: b31 article-title: Solution to economic emission dispatch problem including wind farms using Exchange Market Algorithm Method publication-title: Appl. Soft Comput. – volume: 118 start-page: 806 year: 2018 end-page: 827 ident: b3 article-title: A novel method to solve sustainable economic power loading dispatch problem publication-title: Ind. Manage. Data Syst. – volume: 9 start-page: 319 year: 2016 end-page: 328 ident: b32 article-title: Multiobjective power and emission dispatch using modified group search optimization method publication-title: Ain Shams Eng. J. – volume: 14 start-page: 347 year: 1990 end-page: 361 ident: b27 article-title: Genetic algorithms and neural networks: optimizing connections and connectivity publication-title: Parallel Comput. – volume: 12 start-page: 3972 year: 2018 end-page: 3984 ident: b50 article-title: Wind-thermal dynamic economic emission dispatch with a hybrid multi-objective algorithm based on wind speed statistical analysis publication-title: Iet Gener. Transm. Distrib. – volume: 28 year: 2019 ident: b2 article-title: Optimal Power Flow-based Combined Economic and Emission Dispatch problems using hybrid PSGWO Algorithm publication-title: J. Circuits Syst. Comput. – volume: 45 start-page: 748 year: 1999 end-page: 764 ident: b28 article-title: A simulated annealing algorithm with constant temperature for discrete stochastic optimization publication-title: Manage. Sci. – volume: 78 start-page: 735 year: 2016 end-page: 744 ident: b36 article-title: A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm publication-title: Int. J. Electr. Power Energy Syst. – volume: 270 year: 2020 ident: b44 article-title: Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm publication-title: J. Cleaner Prod. – volume: 101 year: 2021 ident: b42 article-title: Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization publication-title: Appl. Soft Comput. – volume: 186 year: 2019 ident: b10 article-title: An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch publication-title: Energy – volume: 31 start-page: 5857 year: 2018 end-page: 5869 ident: b6 article-title: Quantum-behaved bat algorithm for many-objective combined economic emission dispatch problem using cubic criterion function publication-title: Neural Comput. Appl. – volume: 30 start-page: 140 year: 2008 end-page: 149 ident: b48 article-title: Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II publication-title: Int. J. Electr. Power Energy Syst. – volume: 23 start-page: 21 year: 2017 end-page: 28 ident: b20 article-title: Moth Swarm Algorithm for solving Combined Economic and emission Dispatch Problem publication-title: Elektron. Elektrotech. – volume: 29 year: 2019 ident: b9 article-title: Chaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints publication-title: Int. Trans. Electr. Energy Syst. – volume: 9 start-page: 1327 year: 1994 end-page: 1336 ident: b23 article-title: The quadratic interior point method solving power system optimization problems publication-title: IEEE Trans. Power Syst. – volume: 90 year: 2020 ident: b18 article-title: Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization publication-title: Eng. Appl. Artif. Intell. – volume: 27 start-page: 1601 issue: 3 year: 2014 ident: 10.1016/j.asoc.2021.107504_b14 article-title: An intelligent approach based on bat algorithm for solving economic dispatch with practical constraints publication-title: J. Intell. Fuzzy Systems doi: 10.3233/IFS-141126 – volume: 12 start-page: 3972 issue: 17 year: 2018 ident: 10.1016/j.asoc.2021.107504_b50 article-title: Wind-thermal dynamic economic emission dispatch with a hybrid multi-objective algorithm based on wind speed statistical analysis publication-title: Iet Gener. Transm. Distrib. doi: 10.1049/iet-gtd.2018.5364 – volume: 61 start-page: 1186 year: 2017 ident: 10.1016/j.asoc.2021.107504_b37 article-title: A comprehensive study of practical economic dispatch problems by a new hybrid evolutionary algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.06.041 – volume: 12 start-page: 3844 issue: 16 year: 2018 ident: 10.1016/j.asoc.2021.107504_b22 article-title: Decentralised gradient projection method for economic dispatch problem with valve point effect publication-title: IET Gener. Transm. Distrib. doi: 10.1049/iet-gtd.2018.0369 – volume: 29 issue: 8 year: 2019 ident: 10.1016/j.asoc.2021.107504_b9 article-title: Chaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints publication-title: Int. Trans. Electr. Energy Syst. doi: 10.1002/2050-7038.12026 – volume: 22 start-page: 55 issue: 1 year: 2018 ident: 10.1016/j.asoc.2021.107504_b21 article-title: Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms publication-title: Eng. Sci. Technol. Int. J. – volume: 15 issue: 03 year: 2018 ident: 10.1016/j.asoc.2021.107504_b33 article-title: Quorum sensing driven Bacterial Swarm Optimization to Solve Practical Dynamic Power Ecological Emission Economic dispatch publication-title: Int. J. Comput. Methods doi: 10.1142/S0219876218500895 – volume: 78 start-page: 1466 issue: 8 year: 2008 ident: 10.1016/j.asoc.2021.107504_b17 article-title: Stochastic economic emission load dispatch through a modified particle swarm optimization algorithm publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2008.01.012 – volume: 3 start-page: 1486 issue: 8 year: 2008 ident: 10.1016/j.asoc.2021.107504_b26 article-title: Particle swarm optimization publication-title: Scholarpedia doi: 10.4249/scholarpedia.1486 – volume: 118 start-page: 806 issue: 4 year: 2018 ident: 10.1016/j.asoc.2021.107504_b3 article-title: A novel method to solve sustainable economic power loading dispatch problem publication-title: Ind. Manage. Data Syst. doi: 10.1108/IMDS-04-2017-0145 – volume: 29 start-page: 721 issue: 3 year: 2016 ident: 10.1016/j.asoc.2021.107504_b19 article-title: Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems publication-title: Neural Comput. Appl. doi: 10.1007/s00521-016-2481-7 – volume: 28 issue: 5 year: 2018 ident: 10.1016/j.asoc.2021.107504_b1 article-title: Environmental-economic dispatch using stochastic fractal search algorithm publication-title: Int. Trans. Electr. Energy Syst. doi: 10.1002/etep.2530 – volume: 88 year: 2020 ident: 10.1016/j.asoc.2021.107504_b31 article-title: Solution to economic emission dispatch problem including wind farms using Exchange Market Algorithm Method publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.106044 – volume: 9 start-page: 316 issue: 3 year: 2021 ident: 10.1016/j.asoc.2021.107504_b51 article-title: An enhanced multi-objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power publication-title: Energy Sci. Eng. doi: 10.1002/ese3.827 – volume: 45 start-page: 748 issue: 5 year: 1999 ident: 10.1016/j.asoc.2021.107504_b28 article-title: A simulated annealing algorithm with constant temperature for discrete stochastic optimization publication-title: Manage. Sci. doi: 10.1287/mnsc.45.5.748 – volume: 30 start-page: 140 issue: 2 year: 2008 ident: 10.1016/j.asoc.2021.107504_b48 article-title: Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2007.06.009 – volume: 28 issue: 09 year: 2019 ident: 10.1016/j.asoc.2021.107504_b2 article-title: Optimal Power Flow-based Combined Economic and Emission Dispatch problems using hybrid PSGWO Algorithm publication-title: J. Circuits Syst. Comput. – volume: 31 start-page: 5857 issue: 10 year: 2018 ident: 10.1016/j.asoc.2021.107504_b6 article-title: Quantum-behaved bat algorithm for many-objective combined economic emission dispatch problem using cubic criterion function publication-title: Neural Comput. Appl. doi: 10.1007/s00521-018-3399-z – volume: 8 issue: 4 year: 2007 ident: 10.1016/j.asoc.2021.107504_b40 article-title: Dynamic economic Emission Dispatch using Evolutionary Programming and Fuzzy Satisfying Method publication-title: Int. J. Emerging Electr. Power Syst. – volume: 77 start-page: 1654 issue: 12 year: 2007 ident: 10.1016/j.asoc.2021.107504_b13 article-title: Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2006.11.012 – volume: 101 year: 2021 ident: 10.1016/j.asoc.2021.107504_b42 article-title: Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.107061 – volume: 6 start-page: 1240 issue: 5 year: 2017 ident: 10.1016/j.asoc.2021.107504_b4 article-title: Solving multi-area environmental economic dispatch by Pareto-based chemical-reaction optimization algorithm publication-title: IEEE/CAA J. Automat. Sinica doi: 10.1109/JAS.2017.7510454 – volume: 43 start-page: 77 issue: S1 year: 2018 ident: 10.1016/j.asoc.2021.107504_b7 article-title: Bare bones teaching learning-based Optimization Technique for Economic Emission Load Dispatch Problem Considering Transmission losses publication-title: Iran. J. Sci. Technol. Trans. Electr. Eng. doi: 10.1007/s40998-018-0158-1 – volume: 270 start-page: 188 issue: 12 year: 2017 ident: 10.1016/j.asoc.2021.107504_b49 article-title: Multi-objective dynamic economic emission dispatch using particle swarm optimization variants publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.03.086 – volume: 177 year: 2019 ident: 10.1016/j.asoc.2021.107504_b39 article-title: A hybrid optimization framework for the non-convex economic dispatch problem via meta-heuristic algorithms publication-title: Electr. Power Syst. Res. – volume: 101 start-page: 103 year: 2018 ident: 10.1016/j.asoc.2021.107504_b5 article-title: A multiobjective hybrid bat algorithm for combined economic/emission dispatch publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2018.03.019 – volume: 100 start-page: 201 year: 2018 ident: 10.1016/j.asoc.2021.107504_b46 article-title: A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2018.02.021 – volume: 12 start-page: 104 issue: 1 year: 2017 ident: 10.1016/j.asoc.2021.107504_b8 article-title: Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints publication-title: IET Gener. Transm. Distrib. doi: 10.1049/iet-gtd.2017.0257 – volume: 49 start-page: 1 issue: 6 year: 2012 ident: 10.1016/j.asoc.2021.107504_b15 article-title: Combined economic emission dispatch problem of thermal generating units using particle swarm optimization publication-title: Int. J. Comput. Appl. – volume: 3 start-page: 476 issue: 4 year: 2008 ident: 10.1016/j.asoc.2021.107504_b11 article-title: Hybrid differential evolution technique for economic dispatch problems publication-title: J. Electr. Eng. Technol. doi: 10.5370/JEET.2008.3.4.476 – volume: 186 year: 2019 ident: 10.1016/j.asoc.2021.107504_b10 article-title: An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch publication-title: Energy doi: 10.1016/j.energy.2019.07.131 – volume: 14 start-page: 347 issue: 3 year: 1990 ident: 10.1016/j.asoc.2021.107504_b27 article-title: Genetic algorithms and neural networks: optimizing connections and connectivity publication-title: Parallel Comput. doi: 10.1016/0167-8191(90)90086-O – volume: 12 start-page: 25 issue: 9 year: 2017 ident: 10.1016/j.asoc.2021.107504_b45 article-title: Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty publication-title: Plos One doi: 10.1371/journal.pone.0185454 – volume: 14 start-page: 1222 issue: 5 year: 2021 ident: 10.1016/j.asoc.2021.107504_b43 article-title: Solving single- and multi-objective optimal reactive power dispatch problems using an Improved Salp Swarm Algorithm publication-title: Energies doi: 10.3390/en14051222 – volume: 32 start-page: 163 issue: 2 year: 2004 ident: 10.1016/j.asoc.2021.107504_b29 article-title: An interactive fuzzy satisfying-based simulated annealing technique for economic emission load dispatch with non-smooth fuel cost and emission level functions publication-title: Electr. Power Compon. Syst. doi: 10.1080/15325000490195871 – volume: 84 start-page: 18 year: 2019 ident: 10.1016/j.asoc.2021.107504_b34 article-title: A harmony search variant and a useful constraint handling method for the dynamic economic emission dispatch problems considering transmission loss publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2019.05.005 – year: 2021 ident: 10.1016/j.asoc.2021.107504_b25 article-title: Evaluation of whale and Particle Swarm Optimization Algorithms in Optimal Allocation of water resources of irrigation network to maximize net benefit case study: Salman Farsi publication-title: Int. J. Hydrol. Sci. Technol. doi: 10.1504/IJHST.2021.117554 – volume: 90 year: 2020 ident: 10.1016/j.asoc.2021.107504_b18 article-title: Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103541 – volume: 11 start-page: 141 issue: 05 year: 2016 ident: 10.1016/j.asoc.2021.107504_b16 article-title: Combined economic Emission Dispatch Solution using Simulated Annealing Algorithm publication-title: IOSR J. Electr. Electron. Eng. doi: 10.9790/1676-110502141148 – volume: 8 start-page: 94678 year: 2020 ident: 10.1016/j.asoc.2021.107504_b47 article-title: Dynamic economic emission dispatch considering wind uncertainty using non-Dominated Sorting Crisscross Optimization publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2995213 – volume: 34 start-page: 343 issue: 3 year: 2006 ident: 10.1016/j.asoc.2021.107504_b24 article-title: Emission Constrained Economic Dispatch—A new recursive approach publication-title: Electr. Power Compon. Syst. doi: 10.1080/15325000500241225 – volume: 270 issue: 12 year: 2020 ident: 10.1016/j.asoc.2021.107504_b44 article-title: Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm publication-title: J. Cleaner Prod. – volume: 29 issue: 1 year: 2019 ident: 10.1016/j.asoc.2021.107504_b12 article-title: Multi-objective economic emission dispatch using interior search algorithm publication-title: Int. Trans. Electr. Energy Syst. doi: 10.1002/etep.2683 – volume: 23 start-page: 21 issue: 5 year: 2017 ident: 10.1016/j.asoc.2021.107504_b20 article-title: Moth Swarm Algorithm for solving Combined Economic and emission Dispatch Problem publication-title: Elektron. Elektrotech. doi: 10.5755/j01.eie.23.5.19267 – year: 2020 ident: 10.1016/j.asoc.2021.107504_b35 article-title: An improved particle swarm optimization with clone selection principle for dynamic economic emission dispatch publication-title: Soft Comput. doi: 10.1007/s00500-020-04861-4 – volume: 172 year: 2021 ident: 10.1016/j.asoc.2021.107504_b41 article-title: An improved elephant herding optimization using sine-cosine mechanism and opposition based learning for global optimization problems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114607 – volume: 78 start-page: 735 year: 2016 ident: 10.1016/j.asoc.2021.107504_b36 article-title: A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2015.11.121 – volume: 31 start-page: 8547 issue: 12 year: 2019 ident: 10.1016/j.asoc.2021.107504_b38 article-title: Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04151-7 – volume: 9 start-page: 319 issue: 3 year: 2016 ident: 10.1016/j.asoc.2021.107504_b32 article-title: Multiobjective power and emission dispatch using modified group search optimization method publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2016.03.001 – volume: 9 start-page: 1327 issue: 3 year: 1994 ident: 10.1016/j.asoc.2021.107504_b23 article-title: The quadratic interior point method solving power system optimization problems publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.336133 – volume: 34 start-page: 1015 issue: 9 year: 2006 ident: 10.1016/j.asoc.2021.107504_b30 article-title: Particle Swarm Optimization based Goal-attainment method for Dynamic Economic Emission Dispatch publication-title: Electr. Power Compon. Syst. doi: 10.1080/15325000600596759 |
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| SubjectTerms | Dynamic economic emission dispatch Fuel cost Improved tunicate swarm algorithm Power system Soft-computing |
| Title | Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems |
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