Multi-objective real-time integrated solar-wind-thermal power dispatch by using meta-heuristic technique

Sunimerjit Kaur; Yadwinder Singh Brar; Jaspreet Singh Dhillon; Sunimerjit Kaur; Yadwinder Singh Brar; Jaspreet Singh Dhillon

doi:10.3934/energy.2022043

AIMS Energy

2022, Volume 10, Issue 4: 943-971. doi: 10.3934/energy.2022043

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Research article

Multi-objective real-time integrated solar-wind-thermal power dispatch by using meta-heuristic technique

1.
Research Scholar, I.K. Gujral Punjab Technical University, Kapurthala 144603, Punjab, India
2.
vElectrical Engineering Department, I.K. Gujral Punjab Technical University, Kapurthala 144603, Punjab, India
3.
Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering and Technology, Sangrur 148106, Punjab, India

Received: 06 June 2022 Revised: 24 July 2022 Accepted: 29 July 2022 Published: 11 August 2022

The elevated demand for electrical power, expeditious expenditure of fossil fuels, and degradation of the environment because of power generation have renewed attentiveness to renewable energy resources (RER). The rapid augmentation of RER increases the convolutions in leveling the demand and generation of electrical power. In this paper, an elaborated $ \alpha $-constrained simplex method (ACSM) is recommended for multi-objective power dispatch problems. This methodology is devised after synthesizing the non-linear simplex method (SM) with the $ \alpha $-constrained method (ACM) and the evolutionary method (EM). ACSM can transfigure an optimization technique for the constrained problems by reinstating standard juxtapositions with $ \alpha $-level collations. The insertion of mutations and multi-simplexes can explore the periphery of the workable zone. It can also manage the fastness of convergence and therefore, the high precision solution can be obtained. A real-time multi-objective coordinated solar-wind-thermal power scheduling problem is framed. Two conflicting objectives (operating cost and emission) are satisfied. The case studies are carried out for Muppandal (Tamil Nadu), Jaisalmer (Rajasthan), and Okha (Gujarat), India. The annual solar and wind data are analyzed by using Normal Distribution and Weibull Distribution Density Factor, respectively. The presented technique is inspected on numerous archetype functions and systems. The results depict the prevalence of ACSM over particle swarm optimization (PSO), simplex method with mutations (SMM), SM, and EM.
- $\alpha$-constrained simplex method,
- $\alpha$-level comparisons,
- multi-simplexes,
- mutations,
- normal distribution,
- weibull distribution density factor,
- fuzzy cardinal priority ranking
Citation: Sunimerjit Kaur, Yadwinder Singh Brar, Jaspreet Singh Dhillon. Multi-objective real-time integrated solar-wind-thermal power dispatch by using meta-heuristic technique[J]. AIMS Energy, 2022, 10(4): 943-971. doi: 10.3934/energy.2022043

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Abstract

The elevated demand for electrical power, expeditious expenditure of fossil fuels, and degradation of the environment because of power generation have renewed attentiveness to renewable energy resources (RER). The rapid augmentation of RER increases the convolutions in leveling the demand and generation of electrical power. In this paper, an elaborated $ \alpha $-constrained simplex method (ACSM) is recommended for multi-objective power dispatch problems. This methodology is devised after synthesizing the non-linear simplex method (SM) with the $ \alpha $-constrained method (ACM) and the evolutionary method (EM). ACSM can transfigure an optimization technique for the constrained problems by reinstating standard juxtapositions with $ \alpha $-level collations. The insertion of mutations and multi-simplexes can explore the periphery of the workable zone. It can also manage the fastness of convergence and therefore, the high precision solution can be obtained. A real-time multi-objective coordinated solar-wind-thermal power scheduling problem is framed. Two conflicting objectives (operating cost and emission) are satisfied. The case studies are carried out for Muppandal (Tamil Nadu), Jaisalmer (Rajasthan), and Okha (Gujarat), India. The annual solar and wind data are analyzed by using Normal Distribution and Weibull Distribution Density Factor, respectively. The presented technique is inspected on numerous archetype functions and systems. The results depict the prevalence of ACSM over particle swarm optimization (PSO), simplex method with mutations (SMM), SM, and EM.

References

[1]	Ministry of New and Renewable Energy, Government of India. Annual Report 2018–19. Available from: https://mnre.gov.in/.
[2]	Liaquat S, Fakhar MS, Kashif SAR, et al. (2021) Application of dynamically search space squeezed modified firefly algorithm to a novel short term economic dispatch of multi-generation systems. IEEE Access 9: 1918–1939. https://doi.org/10.1109/ACCESS.2020.3046910 doi: 10.1109/ACCESS.2020.3046910
[3]	Rahimi M, Ardakani FJ, Ardakani AJ (2021) Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant. Int J Elect Power Energy Syst 127: 106658. https://doi.org/10.1016/j.ijepes.2020.106658 doi: 10.1016/j.ijepes.2020.106658
[4]	Naversen CØ, Helseth A, Li B, et al. (2020) Hydrothermal scheduling in the continuous-time framework. Electr Power Sys Res 189: 106787. https://doi.org/10.1016/j.epsr.2020.106787 doi: 10.1016/j.epsr.2020.106787
[5]	Narang N, Dhillon JS, Kothari DP (2014) Scheduling short-term hydrothermal generation using predator prey optimization technique. Appl Soft Comp 21: 298–308. https://doi.org/10.1016/j.asoc.2014.03.029 doi: 10.1016/j.asoc.2014.03.029
[6]	Singh NJ, Dhillon JS, Kothari DP (2018) Multiobjective thermal power load dispatch using adaptive predator‑prey optimization. Appl Soft Comp 66: 370–383. https://doi.org/10.1016/j.asoc.2018.02.006 doi: 10.1016/j.asoc.2018.02.006
[7]	Mondal S, Bhattacharya A, Dey SHN (2013) Multiobjective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration. Int J Elect Power Energy Syst 44: 282–292. https://doi.org/10.1016/j.ijepes.2012.06.049 doi: 10.1016/j.ijepes.2012.06.049
[8]	Ansari MM, Guo C, Shaikh M, et al. (2020) Considering the uncertainty of hydrothermal wind and solar-based DG. Alex Eng J 59: 4211–4236. https://doi.org/10.1016/j.aej.2020.07.026 doi: 10.1016/j.aej.2020.07.026
[9]	Das S, Bhattacharya A, Chakraborty AK (2018) Fixed head short-term hydrothermal scheduling in presence of solar and wind power. Energy Strat Re 22: 47–60. https://doi.org/10.1016/j.esr.2018.08.001 doi: 10.1016/j.esr.2018.08.001
[10]	Dasgupta K, Roy PK, Mukherjee V (2020) Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm. Electr Power Syst Res 178: 106018. https://doi.org/10.1016/j.epsr.2019.106018 doi: 10.1016/j.epsr.2019.106018
[11]	Ji B, Zhang B, Yu SS, et al. (2021) An enhanced borg algorithmic framework for solving the hydrothermal-wind co-scheduling problem. Energy 218: 119512. https://doi.org/10.1016/j.energy.2020.119512 doi: 10.1016/j.energy.2020.119512
[12]	Reddy SS, Praveen P, Kumari S (2009) Micro genetic algorithm based optimal power dispatch in multimode electricity market. Int J Recent Trends Eng 2: 298–302. Available from: https://www.researchgate.net/publication/265432818.
[13]	Reddy SS (2016) Congestion management using multi-objective grenade explosion method. WSEAS Transactions power syst 11: 81–89. Available from: https://www.semanticscholar.org/paper/Congestion-Management-Using-Multi-Objective-Grenade-Reddy/9772b35c435bf21151376567f091b81860066523.
[14]	Reddy SS (2018) Multi-objective optimization considering cost, emission and loss objectives using PSO and fuzzy approach. Int J Eng Tech 7: 1552–1557. https://doi.org10.14419/ijet.v7i3.11203 doi: 10.14419/ijet.v7i3.11203
[15]	Salkuti SR (2020) Optimal day-ahead renewable power generation scheduling of hybrid electrical power system. In: Ray P, Biswal M, Microgrid: Operation, Control, Monitoring and Protection. Lecture Notes in Electrical Engineering 625: 27–49. https://doi.org/10.1007/978-981-15-1781-5_2
[16]	Salkuti SR (2020) Multi-objective based economic environmental dispatch with stochastic solar-wind-thermal power system. Int J Elect Comp Eng 10: 4543–4551. https://doi.org/10.11591/ijece.v10i5.pp4543-4551 doi: 10.11591/ijece.v10i5.pp4543-4551
[17]	Salkuti SR (2021) Multi-objective based optimal network reconfiguration using crow search algorithm. Int J Adv Comp Sci Appl 12: 86–95. https://doi.org/10.14569/IJACSA.2021.0120310 doi: 10.14569/IJACSA.2021.0120310
[18]	Zhang X, Xu H, Yu T, et al. (2016) Robust collaborative consensus algorithm for decentralized economic dispatch with a practical communication network. Elect Power Syst Res 140: 597–610. https://doi.org/10.1016/j.epsr.2016.05.014 doi: 10.1016/j.epsr.2016.05.014
[19]	Zhang X, Yu T, Yang B, et al. (2016) Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid. Energy 101: 34–51. https://doi.org/10.1016/j.energy.2016.02.009 doi: 10.1016/j.energy.2016.02.009
[20]	Zhang X, Yu T, Xu Z, et al. (2018) A cyber-physical-social system with parallel learning for distributed energy management of a microgrid. Energy 165: 205–221. https://doi.org/10.1016/j.energy.2018.09.069 doi: 10.1016/j.energy.2018.09.069
[21]	Tan Z, Zhang X, Xie B, et al. (2018) Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid Source. IET Generation Trans Distrib 12: 2977–2987. https://doi.org/10.1049/iet-gtd.2017.1983 doi: 10.1049/iet-gtd.2017.1983
[22]	Biswas PP, Suganthan PN, Amaratunga GAJ (2017) Optimal power flow solutions incorporating stochastic wind and solar power. Energy Conv Manage 148: 1194–1207. https://doi.org/10.1016/j.enconman.2017.06.071 doi: 10.1016/j.enconman.2017.06.071
[23]	Das S, Bhattacharya A, Chakraborty AK (2018) Solution of short-term hydrothermal scheduling problem using quasi-reflected symbiotic organisms search algorithm considering multi-fuel cost characteristics of thermal generator. Ara J Sci Eng 43: 2931–2960. https://doi.org/10.1007/s13369-017-2973-5 doi: 10.1007/s13369-017-2973-5
[24]	He Z, Zhou J, Sun N, et al. (2019) Integrated scheduling of hydro, thermal and wind power with spinning reserve. Energy Procedia 158: 6302–6308. https://doi.org/10.1016/j.egypro.2019.01.409 doi: 10.1016/j.egypro.2019.01.409
[25]	Kansal V, Dhillon JS (2020) Emended salp swarm algorithm for multiobjective electric power dispatch problem. Appl Soft Comput 90: 106172. https://doi.org/10.1016/j.asoc.2020.106172 doi: 10.1016/j.asoc.2020.106172
[26]	Li J, Lu J, Yao L, et al. (2019) Wind-solar-hydro power optimal scheduling model based on multi-objective dragonfly algorithm. Energy Procedia 158: 6217–6224. http://dx.doi.org/10.1016/j.egypro.2019.01.476 doi: 10.1016/j.egypro.2019.01.476
[27]	Panda A, Tripathy M (2016) Solution of wind integrated thermal generation system for environmental optimal power flow using hybrid algorithm. J Elect Syst Inform Tech 3: 151–160. https://doi.org/10.1016/j.jesit.2016.01.004 doi: 10.1016/j.jesit.2016.01.004
[28]	Takahama T, Sakai S (2005) Constrained optimization by applying the /spl alpha/ constrained method to the nonlinear simplex method with mutations. IEEE Tran Evol Comput 9: 437–450. https://doi.org/10.1109/TEVC.2005.850256 doi: 10.1109/TEVC.2005.850256
[29]	Brar YS, Dhillon JS, Kothari DP (2005) Fuzzy satisfying multi-objective generation scheduling based on simplex weightage pattern search. Int J Elect Power Ener Syst 27: 518–527. http://dx.doi.org/10.1016/j.ijepes.2005.06.002 doi: 10.1016/j.ijepes.2005.06.002
[30]	Kothari DP, Dhillon JS (2011) Power systems optimization, 2nd Ed, New Delhi: PHI Learning. Available from: http://www.kopykitab.com/product/7450.
[31]	Kaur S, Brar YS, Dhillon JS (2020) Solar-thermal power scheduling by inserting α-constrained method to nonlinear simplex method with mutations. In: 2020 Inter Conference on Smart Grid and Clean Energy Tech (ICSGCE), 27–34. http://dx.doi.org/10.1109/ICSGCE49177.2020.9275629
[32]	Kaur S, Brar YS, Dhillon JS (2020) Multi-objective power scheduling of wind‑thermal integrated system by using α-constrained simplex method. In: 2020 Inter Conference on Smart Grid and Clean Energy Tech (ICSGCE), 112–119. https://doi.org/10.1109/ICSGCE49177.2020.9275653
[33]	Kaur S, Brar YS, Dhillon JS (2021) Optimal scheduling of solar-wind-thermal integrated system using α-constrained simplex method. Int J Renewable Energy Dev 10: 47–59. https://doi.org/10.14710/ijred.2021.32245 doi: 10.14710/ijred.2021.32245
[34]	Kaur S, Brar YS, Dhillon JS (2021) Real-time short-term hydro-thermal-wind-solar power scheduling using meta-heuristic optimization technique. Int J Renewable Energy Dev 10: 635–651. https://doi.org/10.14710/ijred.2021.35558 doi: 10.14710/ijred.2021.35558
[35]	Reddy SS (2017) Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization. Elec Eng 99: 535–550. https://doi.org/10.1007/s00202-016-0382-5 doi: 10.1007/s00202-016-0382-5
[36]	Nagababu G, Baivishi D, Kachhwaha SS, et al. (2015) Evaluation of wind resource in selected locations in Gujarat. Energy Procedia 79: 212–219. https://doi.org/10.1016/j.egypro.2015.11.467 doi: 10.1016/j.egypro.2015.11.467
[37]	Singal RK (2010) Non-conventional energy resources, 3rd Ed, New Delhi: SK Kataria & Sons. Available from: https://www.skkatariaandsons.com/view_book.aspx?productid=8063.
[38]	NCERT India. Available from: https://ncert.nic.in/textbook/pdf/iess101.pdf.
[39]	Muppandal wind farm. Wikipedia the free encyclopedia. Available from: https://en.wikipedia.org/wiki/Muppandal_Wind_Farm.
[40]	Sukkiramathi K, Seshaiah CV (2020) Analysis of wind power potential by the three-parameter weibull distribution to install a wind turbine. Energy Exp Exploit 38: 158–174. https://doi.org/10.1177%2F0144598719871628
[41]	Jaisalmer wind park. Wikipedia the free encyclopedia. Available from: https://en.wikipedia.org/wiki/Jaisalmer_Wind_Park.

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