Implementation of coyote optimization algorithm for solving unit commitment problem in power systems
The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time inter...
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| Veröffentlicht in: | Energy (Oxford) Jg. 263; S. 125697 |
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15.01.2023
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| ISSN: | 0360-5442 |
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| Abstract | The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as Coyote Optimization Algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.
•Coyote optimization algorithm is proposed for unit commitment problem.•The objective function is designed to minimize the total operating costs.•The efficacy of the algorithm is confirmed by the total cost, and computational time.•The percentage reduction of total cost can reach 5.4%.•The stability is proved by the difference between the worst and the best cost. |
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| AbstractList | The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as Coyote Optimization Algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.
•Coyote optimization algorithm is proposed for unit commitment problem.•The objective function is designed to minimize the total operating costs.•The efficacy of the algorithm is confirmed by the total cost, and computational time.•The percentage reduction of total cost can reach 5.4%.•The stability is proved by the difference between the worst and the best cost. The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as Coyote Optimization Algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis. |
| ArticleNumber | 125697 |
| Author | Elazim, S.M. Abd Ali, E.S. Balobaid, A.S. |
| Author_xml | – sequence: 1 givenname: E.S. surname: Ali fullname: Ali, E.S. email: esalama@jazanu.edu.sa, ehabsalimalisalama@yahoo.com organization: Electrical Engineering Department, Faculty of Engineering, Jazan University, Saudi Arabia – sequence: 2 givenname: S.M. Abd surname: Elazim fullname: Elazim, S.M. Abd organization: Computer Science Department, Faculty of Computer Science and Information Technology, Jazan University, Saudi Arabia – sequence: 3 givenname: A.S. surname: Balobaid fullname: Balobaid, A.S. organization: Computer Science Department, Faculty of Computer Science and Information Technology, Jazan University, Saudi Arabia |
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| Cites_doi | 10.1016/j.ijepes.2014.11.025 10.1504/IJBIC.2014.060609 10.1109/59.982197 10.1049/iet-gtd.2013.0436 10.3390/en13174473 10.1016/j.energy.2015.04.102 10.3390/su13063131 10.1109/TPWRS.2009.2021216 10.1109/59.867163 10.1109/TPWRS.2011.2158010 10.1023/B:HEUR.0000012449.84567.1a 10.1007/s00521-016-2650-8 10.1016/j.egypro.2011.12.1201 10.1016/j.asoc.2008.11.010 10.1109/TPAS.1983.318063 10.1049/iet-gtd:20070367 10.1080/02533839.2014.999865 10.3906/elk-2004-144 10.1016/j.ijepes.2014.03.061 10.1109/TPWRS.2009.2038921 10.1016/j.epsr.2011.09.022 10.1016/j.eswa.2008.10.047 10.1016/j.epsr.2007.02.011 10.1016/j.egyr.2020.04.032 10.1016/j.ijepes.2012.10.042 10.1080/15325000801911377 10.1109/ACCESS.2020.3010275 10.1109/TPWRS.2005.860922 10.1080/01430750.2017.1423384 10.1016/j.energy.2019.01.155 10.1016/S0378-7796(97)01175-9 10.1016/0142-0615(95)00013-5 10.1109/MPER.1987.5527261 10.4018/IJSIR.2015040104 10.1016/0378-7796(95)00954-G 10.1007/s00521-021-06175-4 10.1109/59.485989 10.1109/TPWRS.2010.2059716 10.1109/ACCESS.2018.2861319 10.1016/j.apenergy.2009.10.013 10.1016/0378-7796(94)90006-X 10.1016/S0142-0615(98)00013-1 10.1016/j.energy.2016.02.041 10.1016/j.swevo.2017.08.002 10.7763/IJCEE.2011.V3.427 10.1016/j.energy.2019.116001 10.1109/TENCON.1993.320573 10.1016/j.ijepes.2009.06.019 10.1109/59.801925 10.1057/jors.1988.54 10.1049/iet-gtd.2015.0201 10.1016/j.asoc.2010.05.006 10.1177/0020294019890630 10.1016/j.epsr.2005.07.002 |
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| References | Juste, Kita, Tanaka, Hasegawa (bib74) 1999; 14 Ali, Abd-Elazim (bib54) 2018; 30 Singhal, Naresh, Sharma, Kumar (bib57) 2014 Asokan, Ashokkumar (bib34) 2014; 13 Zand, Bigdeli, Azizian (bib38) July 2016; 3 Moores (bib49) 1988; 39 Yu, Zhang (bib70) 2014; 61 Panwar, Reddy, Verma, Panigrahi, Kumar (bib67) 2018; 38 Arfaoui, Hegazy, Al-Dhaifallah, Ibrahim, Mami (bib43) 2020; 13 Guesmi, Alshammari, Almalaq, Alateeq, Alqunun (bib45) 2021; 13 Elsayed, Maklad, Farrag (bib53) December 2017 Muralikrishan, Jebaraj, Rajan (bib3) 2020; 8 Nieva, Inda, Guillen (bib48) 1987; 7 Patra, Goswami, Goswami (bib64) 2008; 36 Wood, Wollenberg, Sheble (bib1) 2013 Eslamian, Hosseinian, Vahidi (bib75) 2009; 24 Chandrasekaran, Hemamalini, Simon, Padhy (bib77) 2012; 84 Shahid, Malik, Said (bib63) 2021; 29 Pierezan, Coelho, Mariani, Segundo, Prayogo (bib40) 2020; 242 Ma, El-Keib, Smith, Ma (bib15) Jul. 1995; 34 Wang, Singh (bib19) Jun. 2009; 9 Darvishan, Mollashahi, Ghaffari, Lariche (bib24) Aug. 2019; 40 Abdelaziz, Ali, Abd-Elazim (bib55) 2016; 101C Yuan, Nie, Su, Wang, Yuan (bib69) 2009; 36 Mallipeddi, Suganthan (bib2) 2014; 6 Barati, Farsangi (bib27) 2014; 8 Senthil Kumar, Mohan (bib18) Feb. 2010; 32 Singhal, Sharma (bib51) 2011 Senjyu, Yamashiro, Uezato, Funabashi (bib73) 2002; vol. 1 Purl, Narang, Jain, Chauhan (bib58) 2012; 2 Pourjamal, Ravadanegh (bib76) 2013; 46 Dieu, Ongsakul (bib12) Mar. 2008; 78 Hadji, Vahidi (bib59) Feb. 2012; 27 Yuan, Wang, Wang, Yildizbasi (bib42) Nov 2020; 6 Sheble, Maifeld, Brittig, Fahd, Fukurozaki-Coppinger (bib16) 1996; 18 Merlin, Sandrin (bib47) 1983; 102 Jacob Raglend, Raghuveer, Rakesh Avinash, Padhy, Kothari (bib20) 2010; 10 Pierezan, Coelho (bib39) July 2018 Rong, Luh, Lahdelma (bib11) 2016; 10 Hussein, Jaber (bib62) 2020; 53 Cheng, Liu, Liu (bib72) May 2000; 15 Abu Jasser (bib7) December 2011; 3 Abdelaziz, Kamh, Mekhamer, Badr (bib71) 2010; 2 Senjyu, Miyagi, Saber, Urasaki, Funabashi (bib8) Mar. 2006; 76 Kazarlis, Bakirtzis, Petridis (bib68) Feb. 1996; 11 Pappala, Erlich (bib21) 2010; 25 Chung, Han, Kit Po (bib65) 2011; 26 Benhamida, Abdallah, Rashed (bib60) 2007 Kuo, Lu (bib13) Jul. 2015; 38 Georgopoulou, Giannakoglou (bib25) May 2010; 87 Grey, Sekar (bib10) Nov. 2008; 2 Montero, Bello, Reneses (bib4) 2022; 15 Quan, Jian, Yang (bib9) May 2015; 67 D. P. Kothari, and A. Ahmad, “An expert system Approach to unit commitment problem”, IEEE TENCON '93/Beifng, pp. 5-8. Singhal (bib56) Dec 2011 Orero, Irving (bib32) Dec. 1997; 43 Qais, Hasanien, Alghuwainem, Nouh (bib44) 2019; 187 Ting, Rao, Loo, Ngu (bib33) Dec. 2003; 9 Pappala, Erlich (bib61) 2008 Yuan, Nie, Su, Wang, Yuan (bib22) 2009; 36 Nguyen, Nguyen, Nguyen (bib46) 2021; 33 Najafi, pourjamal (bib36) 2012; 14 Simopoulos, Kavatza, Vournas (bib37) 2006; 21 Anand, Narang, Dhillon (bib23) Apr. 2019; 172 Swarup, Yamashiro (bib17) 2002; 17 Yalcinoz, Short, Cory (bib6) 1999 Daimari, Goswami (bib28) December-2016; 5 Surekha, Archana, Sumathi (bib26) 2012; 7 Han, Wang, Zhang, Chen (bib29) 2013 Panwar, Reddy, Kumar (bib66) 2015; 6 Singhal, Sharma, Naresh (bib30) 2015; 9 Chang, Chen (bib35) Nov. 2007; 28 Sheble, Maifeld (bib14) Jul. 1994; 30 Zhao, Liu, Zhou, Guo, Qi (bib31) 2018; 6 Sen, Kothari (bib50) 1998; 20 Moradi, Khanmohammadi, Hagh, Mohammadiivatloo (bib52) 2015; 88 Pierezan, Coelho, Mariani, Lebensztajn (bib41) July 2019 Quan (10.1016/j.energy.2022.125697_bib9) 2015; 67 Eslamian (10.1016/j.energy.2022.125697_bib75) 2009; 24 Singhal (10.1016/j.energy.2022.125697_bib51) 2011 Orero (10.1016/j.energy.2022.125697_bib32) 1997; 43 Chung (10.1016/j.energy.2022.125697_bib65) 2011; 26 Simopoulos (10.1016/j.energy.2022.125697_bib37) 2006; 21 Asokan (10.1016/j.energy.2022.125697_bib34) 2014; 13 Chang (10.1016/j.energy.2022.125697_bib35) 2007; 28 Han (10.1016/j.energy.2022.125697_bib29) 2013 Singhal (10.1016/j.energy.2022.125697_bib30) 2015; 9 Pierezan (10.1016/j.energy.2022.125697_bib39) 2018 Jacob Raglend (10.1016/j.energy.2022.125697_bib20) 2010; 10 Yu (10.1016/j.energy.2022.125697_bib70) 2014; 61 Kazarlis (10.1016/j.energy.2022.125697_bib68) 1996; 11 Guesmi (10.1016/j.energy.2022.125697_bib45) 2021; 13 Ma (10.1016/j.energy.2022.125697_bib15) 1995; 34 Darvishan (10.1016/j.energy.2022.125697_bib24) 2019; 40 Wood (10.1016/j.energy.2022.125697_bib1) 2013 Abdelaziz (10.1016/j.energy.2022.125697_bib55) 2016; 101C Panwar (10.1016/j.energy.2022.125697_bib66) 2015; 6 Singhal (10.1016/j.energy.2022.125697_bib57) 2014 Zand (10.1016/j.energy.2022.125697_bib38) 2016; 3 Surekha (10.1016/j.energy.2022.125697_bib26) 2012; 7 Hadji (10.1016/j.energy.2022.125697_bib59) 2012; 27 Shahid (10.1016/j.energy.2022.125697_bib63) 2021; 29 Senjyu (10.1016/j.energy.2022.125697_bib8) 2006; 76 Sheble (10.1016/j.energy.2022.125697_bib14) 1994; 30 Merlin (10.1016/j.energy.2022.125697_bib47) 1983; 102 Najafi (10.1016/j.energy.2022.125697_bib36) 2012; 14 Dieu (10.1016/j.energy.2022.125697_bib12) 2008; 78 Senthil Kumar (10.1016/j.energy.2022.125697_bib18) 2010; 32 Moradi (10.1016/j.energy.2022.125697_bib52) 2015; 88 Muralikrishan (10.1016/j.energy.2022.125697_bib3) 2020; 8 Nguyen (10.1016/j.energy.2022.125697_bib46) 2021; 33 Anand (10.1016/j.energy.2022.125697_bib23) 2019; 172 Mallipeddi (10.1016/j.energy.2022.125697_bib2) 2014; 6 Daimari (10.1016/j.energy.2022.125697_bib28) 2016; 5 Grey (10.1016/j.energy.2022.125697_bib10) 2008; 2 Pierezan (10.1016/j.energy.2022.125697_bib40) 2020; 242 Sen (10.1016/j.energy.2022.125697_bib50) 1998; 20 Abu Jasser (10.1016/j.energy.2022.125697_bib7) 2011; 3 Kuo (10.1016/j.energy.2022.125697_bib13) 2015; 38 Patra (10.1016/j.energy.2022.125697_bib64) 2008; 36 Yuan (10.1016/j.energy.2022.125697_bib42) 2020; 6 Ting (10.1016/j.energy.2022.125697_bib33) 2003; 9 Swarup (10.1016/j.energy.2022.125697_bib17) 2002; 17 Pierezan (10.1016/j.energy.2022.125697_bib41) 2019 Pappala (10.1016/j.energy.2022.125697_bib21) 2010; 25 Singhal (10.1016/j.energy.2022.125697_bib56) 2011 Montero (10.1016/j.energy.2022.125697_bib4) 2022; 15 Zhao (10.1016/j.energy.2022.125697_bib31) 2018; 6 Senjyu (10.1016/j.energy.2022.125697_bib73) 2002; vol. 1 Barati (10.1016/j.energy.2022.125697_bib27) 2014; 8 Yuan (10.1016/j.energy.2022.125697_bib69) 2009; 36 Nieva (10.1016/j.energy.2022.125697_bib48) 1987; 7 Abdelaziz (10.1016/j.energy.2022.125697_bib71) 2010; 2 10.1016/j.energy.2022.125697_bib5 Cheng (10.1016/j.energy.2022.125697_bib72) 2000; 15 Juste (10.1016/j.energy.2022.125697_bib74) 1999; 14 Pourjamal (10.1016/j.energy.2022.125697_bib76) 2013; 46 Yalcinoz (10.1016/j.energy.2022.125697_bib6) 1999 Chandrasekaran (10.1016/j.energy.2022.125697_bib77) 2012; 84 Moores (10.1016/j.energy.2022.125697_bib49) 1988; 39 Purl (10.1016/j.energy.2022.125697_bib58) 2012; 2 Hussein (10.1016/j.energy.2022.125697_bib62) 2020; 53 Arfaoui (10.1016/j.energy.2022.125697_bib43) 2020; 13 Yuan (10.1016/j.energy.2022.125697_bib22) 2009; 36 Qais (10.1016/j.energy.2022.125697_bib44) 2019; 187 Rong (10.1016/j.energy.2022.125697_bib11) 2016; 10 Benhamida (10.1016/j.energy.2022.125697_bib60) 2007 Pappala (10.1016/j.energy.2022.125697_bib61) 2008 Sheble (10.1016/j.energy.2022.125697_bib16) 1996; 18 Ali (10.1016/j.energy.2022.125697_bib54) 2018; 30 Wang (10.1016/j.energy.2022.125697_bib19) 2009; 9 Panwar (10.1016/j.energy.2022.125697_bib67) 2018; 38 Georgopoulou (10.1016/j.energy.2022.125697_bib25) 2010; 87 Elsayed (10.1016/j.energy.2022.125697_bib53) 2017 |
| References_xml | – start-page: 19 year: December 2017 end-page: 21 ident: bib53 article-title: A new priority list unit commitment method for large-scale power systems publication-title: Proceedings of the 2017 nineteenth international Middle East Power Systems Conference (MEPCON), Cairo, Egypt – volume: 20 start-page: 443 year: 1998 end-page: 451 ident: bib50 article-title: Optimal thermal generating unit commitment: a review publication-title: Int J Electr Power Energy Syst – volume: 13 start-page: 3131 year: 2021 ident: bib45 article-title: New coordinated tuning of SVC and PSSs in multimachine power system using coyote optimization algorithm publication-title: Sustainability – volume: 61 start-page: 510 year: 2014 end-page: 522 ident: bib70 article-title: Unit commitment using Lagrangian relaxation and particle swarm optimization publication-title: Electr. Power Energy Syst. – volume: 40 start-page: 594 year: Aug. 2019 end-page: 599 ident: bib24 article-title: Unit commitment-based load uncertainties based on improved particle swarm optimisation publication-title: Int J Ambient Energy – volume: 14 start-page: 2005 year: 2012 end-page: 2011 ident: bib36 article-title: A new heuristic algorithm for unit commitment problem publication-title: Energy Proc – volume: 46 start-page: 211 year: 2013 end-page: 220 ident: bib76 article-title: HSA based solution to the UC problem publication-title: Int J Electr Power Energy Syst – volume: 18 start-page: 339 year: 1996 end-page: 346 ident: bib16 article-title: Unit commitment by genetic algorithm with penalty methods and a comparison of Lagrangian search and genetic algorithm-economic dispatch example publication-title: Elect. Power Energy Syst. – volume: 9 start-page: 1697 year: 2015 end-page: 1707, Oct ident: bib30 article-title: Binary fish swarm algorithm for profit-based unit commitment problem in competitive electricity market with ramp rate constraints publication-title: IET Gener, Transm Distrib – volume: 6 start-page: 87 year: 2015 end-page: 101 ident: bib66 article-title: Binary fireworks algorithm based thermal unit commitment publication-title: Int J Swarm Intell Res (IJSIR) – volume: 8 start-page: 132980 year: 2020 end-page: 133014 ident: bib3 article-title: A comprehensive review on evolutionary optimization techniques applied for unit commitment problem publication-title: IEEE Access – volume: 88 start-page: 244 year: 2015 end-page: 259 ident: bib52 article-title: Semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem publication-title: Energy – volume: 7 start-page: 52 year: 1987 ident: bib48 article-title: Lagrangian reduction of search-range for large scale unit commitment publication-title: IEEE Power Eng Rev – start-page: 714 year: 2011 end-page: 717 ident: bib51 article-title: Dynamic programming approach for large scale unit commitment problem publication-title: 2011 International Conference – volume: 38 start-page: 547 year: Jul. 2015 end-page: 561 ident: bib13 article-title: Random feasible directions algorithm with a generalized Lagrangian relaxation algorithm for solving unit commitment problem publication-title: J Chin Inst Eng – volume: 14 start-page: 1452 year: 1999 end-page: 1459, Nov ident: bib74 article-title: An evolutionary programming solution to the unit commitment problem publication-title: IEEE Trans Power Syst – volume: 17 start-page: 87 year: 2002 end-page: 91 ident: bib17 article-title: Unit commitment solution methodology using genetic algorithm publication-title: IEEE Trans Power Syst – volume: 32 start-page: 117 year: Feb. 2010 end-page: 125 ident: bib18 article-title: Solution to security constrained unit commitment problem using genetic algorithm publication-title: Int J Electr Power Energy Syst – start-page: 2633 year: July 2018 end-page: 2640 ident: bib39 article-title: Coyote optimization algorithm: a new metaheuristic for global optimization problems publication-title: Proceedings of the IEEE congress on evolutionary computation (CEC) – volume: 36 start-page: 8049 year: 2009 end-page: 8055 ident: bib69 article-title: An improved binary particle swarm optimization for unit commitment problem publication-title: Expert Syst Appl – volume: 6 start-page: 43535 year: 2018 end-page: 43545 ident: bib31 article-title: An improved binary cuckoo search algorithm for solving unit commitment problems: methodological description publication-title: IEEE Access – volume: 13 start-page: 4473 year: 2020 ident: bib43 article-title: Simulation- based coyote optimization algorithm to determine gains of PI controller for enhancing the performance of solar PV water-pumping system publication-title: Energies – volume: 242 year: 2020 ident: bib40 article-title: Chaotic coyote algorithm applied to truss optimization problems publication-title: Comput Struct – volume: 2 start-page: 9 year: 2012 end-page: 16 ident: bib58 article-title: Unit commitment using particle swarm optimization publication-title: BIOINFO Comput Optim – volume: 10 start-page: 1247 year: 2010 end-page: 1256, Sep ident: bib20 article-title: Solution to profit based unit commitment problem using particle swarm optimization publication-title: Appl Soft Comput – start-page: 1 year: 2008 end-page: 6 ident: bib61 article-title: A new approach for solving the unit commitment problem by adaptive particle swarm optimization publication-title: Power and energy society general meeting-conversion and delivery of electrical energy in the 21st century – volume: 30 start-page: 115 year: Jul. 1994 end-page: 121 ident: bib14 article-title: Unit commitment by genetic algorithm and expert system publication-title: Elec Power Syst Res – start-page: 1 year: Dec 2011 end-page: 6 ident: bib56 article-title: Generation scheduling methodology for thermal units using Lagrangian relaxation publication-title: Proc. 2nd IEEE int. Conf. Current trends in technology – volume: 87 start-page: 1782 year: May 2010 end-page: 1792 ident: bib25 article-title: Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages publication-title: Appl Energy – volume: 28 start-page: 965 year: Nov. 2007 end-page: 984 ident: bib35 article-title: Optimal unit commitment decision with risk assessment using tabu search publication-title: J Inf Optim Sci – volume: 43 start-page: 149 year: Dec. 1997 end-page: 156 ident: bib32 article-title: A combination of the genetic algorithm and Lagrangian relaxation decomposition techniques for the generation unit commitment problem publication-title: Elec Power Syst Res – volume: vol. 1 start-page: 58 year: 2002 end-page: 63 ident: bib73 article-title: A unit commitment problem by using genetic algorithm based on characteristic classification publication-title: Proc. IEEE/Power eng Soc Winter meet – volume: 24 start-page: 1478 year: 2009 end-page: 1488, Aug ident: bib75 article-title: Bacterial foraging-based solution to the unit commitment problem publication-title: IEEE Trans Power Syst – volume: 13 start-page: 523 year: 2014 end-page: 542 ident: bib34 article-title: Emission controlled profit based unit commitment for GENCOs using MPPD table with ABC algorithm under competitive environment publication-title: WSEAS Trans Syst – volume: 11 start-page: 83 year: Feb. 1996 end-page: 92 ident: bib68 article-title: A genetic algorithm solution to the unit commitment problem publication-title: IEEE Trans Power Syst – volume: 36 start-page: 771 year: 2008 end-page: 787 ident: bib64 article-title: Differential evolution algorithm for solving unit commitment with ramp constraints publication-title: Elec Power Compon Syst – volume: 53 start-page: 320 year: 2020 end-page: 327 ident: bib62 article-title: Unit commitment based on modified firefly algorithm publication-title: Measurem Control – volume: 5 start-page: 221 year: December-2016 end-page: 225 ident: bib28 article-title: Firefly based unit commitment publication-title: Int J Eng Res Technol – volume: 10 start-page: 1054 year: 2016 end-page: 1061 ident: bib11 article-title: Dynamic programming based algorithm for the unit commitment of the transmission-constrained multi-site combined heat and power system publication-title: Int. J Comput Syst Eng – volume: 34 start-page: 29 year: Jul. 1995 end-page: 36 ident: bib15 article-title: A genetic algorithm based approach to thermal unit commitment of electric power systems publication-title: Elec Power Syst Res – volume: 33 start-page: 12209 year: 2021 end-page: 12236 ident: bib46 article-title: Optimal radial topology of electric unbalanced and balanced distribution system using improved coyote optimization algorithm for power loss reduction publication-title: Neural Comput Appl – volume: 9 start-page: 947 year: Jun. 2009 end-page: 953 ident: bib19 article-title: Unit commitment considering generator outages through a mixed-integer particle swarm optimization algorithm publication-title: Appl Soft Comput – volume: 3 year: July 2016 ident: bib38 article-title: A modified ant colony algorithm for solving the unit commitment problem publication-title: Advanced Energy: Int J – volume: 187 start-page: 116001 year: 2019 ident: bib44 article-title: Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules publication-title: Energy – volume: 8 start-page: 1050 year: 2014 end-page: 1060, Jun ident: bib27 article-title: Solving unit commitment problem by a binary shuffled frog leaping algorithm publication-title: IET Gener, Transm Distrib – volume: 6 start-page: 71 year: 2014 end-page: 90 ident: bib2 article-title: Unit commitment -A survey and comparison of conventional and nature inspired algorithms publication-title: Int J Bio-Inspired Comput – volume: 76 start-page: 283 year: Mar. 2006 end-page: 292 ident: bib8 article-title: Emerging solution of large-scale unit commitment problem by stochastic priority list publication-title: Elec Power Syst Res – volume: 26 start-page: 847 year: 2011 end-page: 854 ident: bib65 article-title: An advanced quantum-inspired evolutionary algorithm for unit commitment publication-title: IEEE Trans Power Syst – volume: 27 start-page: 117 year: Feb. 2012 end-page: 124 ident: bib59 article-title: A solution to the unit commitment problem using imperialistic competition algorithm publication-title: IEEE Trans Power Syst – volume: 15 start-page: 1 year: 2022 end-page: 40 ident: bib4 article-title: A review on the unit commitment problem: approaches, techniques, and resolution methods publication-title: Energies – volume: 6 start-page: 1106 year: Nov 2020 end-page: 1117 ident: bib42 article-title: Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC mode publication-title: Energy Rep – volume: 2 start-page: 37 year: 2010 end-page: 49 ident: bib71 article-title: An augmented hopfield neural network for optimal thermal unit commitment publication-title: Int. J. of Power System Optimization, Vo. – volume: 15 start-page: 707 year: May 2000 end-page: 714 ident: bib72 article-title: Unit commitment by Lagrangian relaxation and genetic algorithms publication-title: IEEE Trans Power Syst – start-page: 15 year: July 2019 end-page: 19 ident: bib41 article-title: Multiobjective coyote algorithm applied to electromagnetic optimization publication-title: 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG) – volume: 101C start-page: 506 year: 2016 end-page: 518 ident: bib55 article-title: Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems publication-title: Energy – volume: 78 start-page: 291 year: Mar. 2008 end-page: 301 ident: bib12 article-title: Ramp rate constrained unit commitment by improved priority list and augmented Lagrange hopfield network publication-title: Elec Power Syst Res – volume: 21 start-page: 193 year: 2006 end-page: 201 ident: bib37 article-title: Unit commitment by an enhanced simulated annealing algorithm publication-title: IEEE Trans Power Syst – volume: 38 start-page: 251 year: 2018 end-page: 266 ident: bib67 article-title: Binary grey wolf optimizer for large scale unit commitment problem publication-title: Swarm Evol Comput – year: 2007 ident: bib60 article-title: Thermal unit commitment solution using an improved Lagrangian relaxation publication-title: Int. Conference on renewable energies and power quality (ICREPQ), sevilla, Spain – start-page: 649 year: 1999 end-page: 654 ident: bib6 article-title: Application of neural networks to unit commitment” – volume: 102 start-page: 1218 year: 1983 end-page: 1225 ident: bib47 article-title: A new method for unit commitment at electricite de France publication-title: IEEE Trans Power Apparatus Syst – volume: 25 start-page: 1696 year: 2010 end-page: 1704, Aug ident: bib21 article-title: A variable-dimension optimization approach to unit commitment problem publication-title: IEEE Trans Power Syst – volume: 29 start-page: 944 year: 2021 end-page: 961 ident: bib63 article-title: Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem publication-title: Turk J Electr Eng Comput Sci – volume: 84 start-page: 109 year: 2012 end-page: 119 ident: bib77 article-title: Thermal unit commitment using binary/real coded artificial bee colony algorithm publication-title: Elec Power Syst Res – volume: 67 start-page: 278 year: May 2015 end-page: 285 ident: bib9 article-title: An improved priority list and neighborhood search method for unit commitment publication-title: Elect. Power Energy Syst. – reference: D. P. Kothari, and A. Ahmad, “An expert system Approach to unit commitment problem”, IEEE TENCON '93/Beifng, pp. 5-8. – year: 2014 ident: bib57 article-title: Solution of unit commitment problem using enhanced genetic algorithm publication-title: 2014 eighteenth national power systems conference – volume: 7 start-page: 159 year: 2012 end-page: 171 ident: bib26 article-title: Unit commitment and economic load dispatch using self adaptive differential evolution publication-title: WSEAS Trans Power Syst – start-page: 1 year: 2013 end-page: 11 ident: bib29 article-title: A unit commitment model with implicit reserve constraint based on an improved artificial fish swarm algorithm publication-title: Math Probl Eng – volume: 9 start-page: 507 year: Dec. 2003 end-page: 520 ident: bib33 article-title: Solving unit commitment problem using hybrid particle swarm optimization publication-title: J Heuristics – volume: 36 start-page: 8049 year: 2009 end-page: 8055 ident: bib22 article-title: An improved binary particle swarm optimization for unit commitment problem publication-title: Expert Syst Appl – volume: 3 start-page: 824 year: December 2011 end-page: 829 ident: bib7 article-title: Solving the unit commitment problem using fuzzy logic publication-title: Int J Comput Electric Eng – volume: 39 start-page: 322 year: 1988 ident: bib49 article-title: Dynamic programming versus conventional optimization: response publication-title: J Oper Res Soc – year: 2013 ident: bib1 article-title: Power generation, operation and control – volume: 2 start-page: 856 year: Nov. 2008 end-page: 867 ident: bib10 article-title: Unified solution of security-constrained unit commitment problem using a linear programming methodology publication-title: IET Gener, Transm Distrib – volume: 172 start-page: 794 year: Apr. 2019 end-page: 807 ident: bib23 article-title: Multi-objective combined heat and power unit commitment using particle swarm optimization publication-title: Energy – volume: 30 start-page: 261 year: 2018 end-page: 270 ident: bib54 article-title: Mine blast algorithm for environmental economic load dispatch with valve loading effect publication-title: Neural Comput Appl – volume: 67 start-page: 278 year: 2015 ident: 10.1016/j.energy.2022.125697_bib9 article-title: An improved priority list and neighborhood search method for unit commitment publication-title: Elect. Power Energy Syst. doi: 10.1016/j.ijepes.2014.11.025 – volume: 13 start-page: 523 year: 2014 ident: 10.1016/j.energy.2022.125697_bib34 article-title: Emission controlled profit based unit commitment for GENCOs using MPPD table with ABC algorithm under competitive environment publication-title: WSEAS Trans Syst – volume: 6 start-page: 71 issue: No. 2 year: 2014 ident: 10.1016/j.energy.2022.125697_bib2 article-title: Unit commitment -A survey and comparison of conventional and nature inspired algorithms publication-title: Int J Bio-Inspired Comput doi: 10.1504/IJBIC.2014.060609 – start-page: 15 year: 2019 ident: 10.1016/j.energy.2022.125697_bib41 article-title: Multiobjective coyote algorithm applied to electromagnetic optimization – volume: 17 start-page: 87 issue: No. 1 year: 2002 ident: 10.1016/j.energy.2022.125697_bib17 article-title: Unit commitment solution methodology using genetic algorithm publication-title: IEEE Trans Power Syst doi: 10.1109/59.982197 – volume: 8 start-page: 1050 issue: No. 6 year: 2014 ident: 10.1016/j.energy.2022.125697_bib27 article-title: Solving unit commitment problem by a binary shuffled frog leaping algorithm publication-title: IET Gener, Transm Distrib doi: 10.1049/iet-gtd.2013.0436 – volume: 13 start-page: 4473 year: 2020 ident: 10.1016/j.energy.2022.125697_bib43 article-title: Simulation- based coyote optimization algorithm to determine gains of PI controller for enhancing the performance of solar PV water-pumping system publication-title: Energies doi: 10.3390/en13174473 – volume: 88 start-page: 244 year: 2015 ident: 10.1016/j.energy.2022.125697_bib52 article-title: Semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem publication-title: Energy doi: 10.1016/j.energy.2015.04.102 – start-page: 1 year: 2008 ident: 10.1016/j.energy.2022.125697_bib61 article-title: A new approach for solving the unit commitment problem by adaptive particle swarm optimization – volume: 7 start-page: 159 issue: No. 1 year: 2012 ident: 10.1016/j.energy.2022.125697_bib26 article-title: Unit commitment and economic load dispatch using self adaptive differential evolution publication-title: WSEAS Trans Power Syst – volume: 13 start-page: 3131 issue: No. 6 year: 2021 ident: 10.1016/j.energy.2022.125697_bib45 article-title: New coordinated tuning of SVC and PSSs in multimachine power system using coyote optimization algorithm publication-title: Sustainability doi: 10.3390/su13063131 – volume: 2 start-page: 37 issue: No. 1 year: 2010 ident: 10.1016/j.energy.2022.125697_bib71 article-title: An augmented hopfield neural network for optimal thermal unit commitment publication-title: Int. J. of Power System Optimization, Vo. – volume: 24 start-page: 1478 issue: No. 3 year: 2009 ident: 10.1016/j.energy.2022.125697_bib75 article-title: Bacterial foraging-based solution to the unit commitment problem publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2009.2021216 – volume: 15 start-page: 707 issue: No. 2 year: 2000 ident: 10.1016/j.energy.2022.125697_bib72 article-title: Unit commitment by Lagrangian relaxation and genetic algorithms publication-title: IEEE Trans Power Syst doi: 10.1109/59.867163 – volume: 27 start-page: 117 issue: No. 1 year: 2012 ident: 10.1016/j.energy.2022.125697_bib59 article-title: A solution to the unit commitment problem using imperialistic competition algorithm publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2011.2158010 – start-page: 649 year: 1999 ident: 10.1016/j.energy.2022.125697_bib6 – volume: 9 start-page: 507 issue: No. 6 year: 2003 ident: 10.1016/j.energy.2022.125697_bib33 article-title: Solving unit commitment problem using hybrid particle swarm optimization publication-title: J Heuristics doi: 10.1023/B:HEUR.0000012449.84567.1a – volume: 10 start-page: 1054 issue: No.8 year: 2016 ident: 10.1016/j.energy.2022.125697_bib11 article-title: Dynamic programming based algorithm for the unit commitment of the transmission-constrained multi-site combined heat and power system publication-title: Int. J Comput Syst Eng – volume: 30 start-page: 261 issue: No. 1 year: 2018 ident: 10.1016/j.energy.2022.125697_bib54 article-title: Mine blast algorithm for environmental economic load dispatch with valve loading effect publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2650-8 – year: 2014 ident: 10.1016/j.energy.2022.125697_bib57 article-title: Solution of unit commitment problem using enhanced genetic algorithm – volume: 14 start-page: 2005 year: 2012 ident: 10.1016/j.energy.2022.125697_bib36 article-title: A new heuristic algorithm for unit commitment problem publication-title: Energy Proc doi: 10.1016/j.egypro.2011.12.1201 – volume: 242 issue: Jan year: 2020 ident: 10.1016/j.energy.2022.125697_bib40 article-title: Chaotic coyote algorithm applied to truss optimization problems publication-title: Comput Struct – volume: 9 start-page: 947 issue: No. 3 year: 2009 ident: 10.1016/j.energy.2022.125697_bib19 article-title: Unit commitment considering generator outages through a mixed-integer particle swarm optimization algorithm publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2008.11.010 – volume: 102 start-page: 1218 issue: No. 5 year: 1983 ident: 10.1016/j.energy.2022.125697_bib47 article-title: A new method for unit commitment at electricite de France publication-title: IEEE Trans Power Apparatus Syst doi: 10.1109/TPAS.1983.318063 – start-page: 1 year: 2013 ident: 10.1016/j.energy.2022.125697_bib29 article-title: A unit commitment model with implicit reserve constraint based on an improved artificial fish swarm algorithm publication-title: Math Probl Eng – volume: 2 start-page: 856 issue: No. 6 year: 2008 ident: 10.1016/j.energy.2022.125697_bib10 article-title: Unified solution of security-constrained unit commitment problem using a linear programming methodology publication-title: IET Gener, Transm Distrib doi: 10.1049/iet-gtd:20070367 – volume: 38 start-page: 547 issue: 5 year: 2015 ident: 10.1016/j.energy.2022.125697_bib13 article-title: Random feasible directions algorithm with a generalized Lagrangian relaxation algorithm for solving unit commitment problem publication-title: J Chin Inst Eng doi: 10.1080/02533839.2014.999865 – volume: 29 start-page: 944 year: 2021 ident: 10.1016/j.energy.2022.125697_bib63 article-title: Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem publication-title: Turk J Electr Eng Comput Sci doi: 10.3906/elk-2004-144 – volume: 61 start-page: 510 year: 2014 ident: 10.1016/j.energy.2022.125697_bib70 article-title: Unit commitment using Lagrangian relaxation and particle swarm optimization publication-title: Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2014.03.061 – volume: 25 start-page: 1696 issue: No. 3 year: 2010 ident: 10.1016/j.energy.2022.125697_bib21 article-title: A variable-dimension optimization approach to unit commitment problem publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2009.2038921 – volume: 84 start-page: 109 year: 2012 ident: 10.1016/j.energy.2022.125697_bib77 article-title: Thermal unit commitment using binary/real coded artificial bee colony algorithm publication-title: Elec Power Syst Res doi: 10.1016/j.epsr.2011.09.022 – volume: 2 start-page: 9 issue: Issue 1 year: 2012 ident: 10.1016/j.energy.2022.125697_bib58 article-title: Unit commitment using particle swarm optimization publication-title: BIOINFO Comput Optim – volume: 36 start-page: 8049 year: 2009 ident: 10.1016/j.energy.2022.125697_bib69 article-title: An improved binary particle swarm optimization for unit commitment problem publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2008.10.047 – volume: 78 start-page: 291 issue: 3 year: 2008 ident: 10.1016/j.energy.2022.125697_bib12 article-title: Ramp rate constrained unit commitment by improved priority list and augmented Lagrange hopfield network publication-title: Elec Power Syst Res doi: 10.1016/j.epsr.2007.02.011 – volume: 6 start-page: 1106 year: 2020 ident: 10.1016/j.energy.2022.125697_bib42 article-title: Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC mode publication-title: Energy Rep doi: 10.1016/j.egyr.2020.04.032 – volume: 46 start-page: 211 year: 2013 ident: 10.1016/j.energy.2022.125697_bib76 article-title: HSA based solution to the UC problem publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2012.10.042 – start-page: 2633 year: 2018 ident: 10.1016/j.energy.2022.125697_bib39 article-title: Coyote optimization algorithm: a new metaheuristic for global optimization problems – volume: 36 start-page: 8049 issue: No. 4 year: 2009 ident: 10.1016/j.energy.2022.125697_bib22 article-title: An improved binary particle swarm optimization for unit commitment problem publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2008.10.047 – volume: 36 start-page: 771 issue: No. 8 year: 2008 ident: 10.1016/j.energy.2022.125697_bib64 article-title: Differential evolution algorithm for solving unit commitment with ramp constraints publication-title: Elec Power Compon Syst doi: 10.1080/15325000801911377 – volume: 8 start-page: 132980 year: 2020 ident: 10.1016/j.energy.2022.125697_bib3 article-title: A comprehensive review on evolutionary optimization techniques applied for unit commitment problem publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3010275 – volume: 21 start-page: 193 issue: No.1 year: 2006 ident: 10.1016/j.energy.2022.125697_bib37 article-title: Unit commitment by an enhanced simulated annealing algorithm publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2005.860922 – volume: 40 start-page: 594 issue: No. 6 year: 2019 ident: 10.1016/j.energy.2022.125697_bib24 article-title: Unit commitment-based load uncertainties based on improved particle swarm optimisation publication-title: Int J Ambient Energy doi: 10.1080/01430750.2017.1423384 – volume: 172 start-page: 794 year: 2019 ident: 10.1016/j.energy.2022.125697_bib23 article-title: Multi-objective combined heat and power unit commitment using particle swarm optimization publication-title: Energy doi: 10.1016/j.energy.2019.01.155 – volume: 43 start-page: 149 issue: No. 3 year: 1997 ident: 10.1016/j.energy.2022.125697_bib32 article-title: A combination of the genetic algorithm and Lagrangian relaxation decomposition techniques for the generation unit commitment problem publication-title: Elec Power Syst Res doi: 10.1016/S0378-7796(97)01175-9 – volume: 18 start-page: 339 issue: No. 6 year: 1996 ident: 10.1016/j.energy.2022.125697_bib16 article-title: Unit commitment by genetic algorithm with penalty methods and a comparison of Lagrangian search and genetic algorithm-economic dispatch example publication-title: Elect. Power Energy Syst. doi: 10.1016/0142-0615(95)00013-5 – volume: 7 start-page: 52 issue: No. 5 year: 1987 ident: 10.1016/j.energy.2022.125697_bib48 article-title: Lagrangian reduction of search-range for large scale unit commitment publication-title: IEEE Power Eng Rev doi: 10.1109/MPER.1987.5527261 – volume: 6 start-page: 87 issue: No. 2 year: 2015 ident: 10.1016/j.energy.2022.125697_bib66 article-title: Binary fireworks algorithm based thermal unit commitment publication-title: Int J Swarm Intell Res (IJSIR) doi: 10.4018/IJSIR.2015040104 – volume: 3 issue: No. 2/3 year: 2016 ident: 10.1016/j.energy.2022.125697_bib38 article-title: A modified ant colony algorithm for solving the unit commitment problem publication-title: Advanced Energy: Int J – volume: 34 start-page: 29 issue: No. 1 year: 1995 ident: 10.1016/j.energy.2022.125697_bib15 article-title: A genetic algorithm based approach to thermal unit commitment of electric power systems publication-title: Elec Power Syst Res doi: 10.1016/0378-7796(95)00954-G – volume: 33 start-page: 12209 year: 2021 ident: 10.1016/j.energy.2022.125697_bib46 article-title: Optimal radial topology of electric unbalanced and balanced distribution system using improved coyote optimization algorithm for power loss reduction publication-title: Neural Comput Appl doi: 10.1007/s00521-021-06175-4 – volume: 11 start-page: 83 issue: No. 1 year: 1996 ident: 10.1016/j.energy.2022.125697_bib68 article-title: A genetic algorithm solution to the unit commitment problem publication-title: IEEE Trans Power Syst doi: 10.1109/59.485989 – volume: 26 start-page: 847 issue: No. 2 year: 2011 ident: 10.1016/j.energy.2022.125697_bib65 article-title: An advanced quantum-inspired evolutionary algorithm for unit commitment publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2010.2059716 – volume: 6 start-page: 43535 year: 2018 ident: 10.1016/j.energy.2022.125697_bib31 article-title: An improved binary cuckoo search algorithm for solving unit commitment problems: methodological description publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2861319 – start-page: 1 year: 2011 ident: 10.1016/j.energy.2022.125697_bib56 article-title: Generation scheduling methodology for thermal units using Lagrangian relaxation – volume: 87 start-page: 1782 issue: No. 5 year: 2010 ident: 10.1016/j.energy.2022.125697_bib25 article-title: Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages publication-title: Appl Energy doi: 10.1016/j.apenergy.2009.10.013 – volume: vol. 1 start-page: 58 year: 2002 ident: 10.1016/j.energy.2022.125697_bib73 article-title: A unit commitment problem by using genetic algorithm based on characteristic classification – volume: 30 start-page: 115 issue: No. 2 year: 1994 ident: 10.1016/j.energy.2022.125697_bib14 article-title: Unit commitment by genetic algorithm and expert system publication-title: Elec Power Syst Res doi: 10.1016/0378-7796(94)90006-X – volume: 20 start-page: 443 issue: No.7 year: 1998 ident: 10.1016/j.energy.2022.125697_bib50 article-title: Optimal thermal generating unit commitment: a review publication-title: Int J Electr Power Energy Syst doi: 10.1016/S0142-0615(98)00013-1 – year: 2007 ident: 10.1016/j.energy.2022.125697_bib60 article-title: Thermal unit commitment solution using an improved Lagrangian relaxation – volume: 101C start-page: 506 year: 2016 ident: 10.1016/j.energy.2022.125697_bib55 article-title: Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems publication-title: Energy doi: 10.1016/j.energy.2016.02.041 – start-page: 714 year: 2011 ident: 10.1016/j.energy.2022.125697_bib51 article-title: Dynamic programming approach for large scale unit commitment problem – volume: 15 start-page: 1 issue: No. 1296 year: 2022 ident: 10.1016/j.energy.2022.125697_bib4 article-title: A review on the unit commitment problem: approaches, techniques, and resolution methods publication-title: Energies – volume: 38 start-page: 251 year: 2018 ident: 10.1016/j.energy.2022.125697_bib67 article-title: Binary grey wolf optimizer for large scale unit commitment problem publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2017.08.002 – year: 2013 ident: 10.1016/j.energy.2022.125697_bib1 – volume: 3 start-page: 824 issue: No. 6 year: 2011 ident: 10.1016/j.energy.2022.125697_bib7 article-title: Solving the unit commitment problem using fuzzy logic publication-title: Int J Comput Electric Eng doi: 10.7763/IJCEE.2011.V3.427 – volume: 187 start-page: 116001 issue: No.15 year: 2019 ident: 10.1016/j.energy.2022.125697_bib44 article-title: Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules publication-title: Energy doi: 10.1016/j.energy.2019.116001 – ident: 10.1016/j.energy.2022.125697_bib5 doi: 10.1109/TENCON.1993.320573 – volume: 32 start-page: 117 issue: No. 2 year: 2010 ident: 10.1016/j.energy.2022.125697_bib18 article-title: Solution to security constrained unit commitment problem using genetic algorithm publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2009.06.019 – volume: 14 start-page: 1452 year: 1999 ident: 10.1016/j.energy.2022.125697_bib74 article-title: An evolutionary programming solution to the unit commitment problem publication-title: IEEE Trans Power Syst doi: 10.1109/59.801925 – start-page: 19 year: 2017 ident: 10.1016/j.energy.2022.125697_bib53 article-title: A new priority list unit commitment method for large-scale power systems – volume: 5 start-page: 221 issue: No. 12 year: 2016 ident: 10.1016/j.energy.2022.125697_bib28 article-title: Firefly based unit commitment publication-title: Int J Eng Res Technol – volume: 39 start-page: 322 issue: 3 year: 1988 ident: 10.1016/j.energy.2022.125697_bib49 article-title: Dynamic programming versus conventional optimization: response publication-title: J Oper Res Soc doi: 10.1057/jors.1988.54 – volume: 9 start-page: 1697 issue: No. 13 year: 2015 ident: 10.1016/j.energy.2022.125697_bib30 article-title: Binary fish swarm algorithm for profit-based unit commitment problem in competitive electricity market with ramp rate constraints publication-title: IET Gener, Transm Distrib doi: 10.1049/iet-gtd.2015.0201 – volume: 10 start-page: 1247 issue: No. 4 year: 2010 ident: 10.1016/j.energy.2022.125697_bib20 article-title: Solution to profit based unit commitment problem using particle swarm optimization publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2010.05.006 – volume: 28 start-page: 965 issue: No. 6 year: 2007 ident: 10.1016/j.energy.2022.125697_bib35 article-title: Optimal unit commitment decision with risk assessment using tabu search publication-title: J Inf Optim Sci – volume: 53 start-page: 320 year: 2020 ident: 10.1016/j.energy.2022.125697_bib62 article-title: Unit commitment based on modified firefly algorithm publication-title: Measurem Control doi: 10.1177/0020294019890630 – volume: 76 start-page: 283 issue: No. 5 year: 2006 ident: 10.1016/j.energy.2022.125697_bib8 article-title: Emerging solution of large-scale unit commitment problem by stochastic priority list publication-title: Elec Power Syst Res doi: 10.1016/j.epsr.2005.07.002 |
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| SubjectTerms | algorithms Canis latrans Coyote optimization algorithm electric power energy Generation scheduling statistical analysis Unit commitment |
| Title | Implementation of coyote optimization algorithm for solving unit commitment problem in power systems |
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