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Design of hybrid energy systems using bat search optimization algorithm
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Ali Goldoust1    , Mehrdad Hojjat *1 , Saeed Seyyed Mahdavi2 |
1- Islamic Azad University, Shahrood Branch
2- Islamic Azad University, Bojnourd Branch |
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Abstract: (4055 Views) |
| This paper presents a framework for the optimal design of hybrid energy systems with a bat search optimization algorithm. Wind turbines, solar panels, and combined heat and power (CHP) systems are considered power generators. The proposed framework can exchange power with the upstream network, taking into account the limitations of power exchange, the cost of installing wind turbines, solar panels, CHP systems, battery storage, and converters in the form of investment costs plus maintenance costs, CHP system fuel costs, expected load removal costs, and power exchange costs as target functions. Also, a complete set of constraints related to the hybrid energy system is included in the problem framework. Uncertainties related to the production capacity of wind turbines and solar panels along with the uncertainty of consumer power demand using the two-point estimation method are also considered in the proposed framework. The bat search algorithm is used to find the optimal solution to the optimization problem. The results of the proposed framework in the form of different scenarios are compared with the results obtained by the genetic algorithm and particle swarm, which indicates its efficiency and optimal performance in designing hybrid energy systems. |
Article number: 75 |
| Keywords: Bat search algorithm, solar panels, two-point estimation, wind turbine, battery storage, hybrid energy system, Combined heat and power (CHP) system. |
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Full-Text [PDF 1222 kb]
(1129 Downloads)
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Type of Study: Research |
Received: 2020/12/29 | Accepted: 2021/09/8 | Published: 2021/12/2
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| References |
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[ DOI:10.1016/S0951-8320(97)00071-9] 20. [1] F. Jamalzadeh, A. Hajiseyed-Mirzahosseini, F. Faghihi, M. Panahi, "Optimal operation of energy hub system using hybrid stochastic-interval optimization approach" Sustainable Cities and Society, Vol. 54, pp. 101998, 2020. [ DOI:10.1016/j.scs.2019.101998] 21. [2] T. Wai-Lip, J. Shiun-Lim, Wai. Shin-Ho, H. Hashim, C. Tin-Lee "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods" Renewable and Sustainable Energy Reviews. Vol. 67, pp. 531-573, 2017. [ DOI:10.1016/j.rser.2016.09.063] 22. [3] M. Roustai, M. Rayati, M. Sheikhi, A. Ranjbar, "A scenario-based optimization of smart energy hub operation in a stochastic environment using conditional-value-at-risk" Sustainable Cities and Society, Vol. 39, pp. 309-316, 2018. [ DOI:10.1016/j.scs.2018.01.045] 23. [4] M.J. Vahid-Pakdel, S. Nojavan, B. Mohammadi-ivatloo, K. Zare, "Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response" Energy Conversion and Management, Vol. 145, pp. 117-128, 2017. [ DOI:10.1016/j.enconman.2017.04.074] 24. [5] M. Salimi, H. Ghasemi, M. Adelpour, S. Vaez-Zadeh, "Optimal planning of energy hubs in interconnected energy systems: a case study for natural gas and electricity" IET Generation, Transmission & Distribution, Vol. 9, No. 8, pp. 695-707, 2015. [ DOI:10.1049/iet-gtd.2014.0607] 25. [6] X. Zhang, L. Che, M. Shahidehpour, A.S. Alabdulwahab, A. Abusorrah, "Reliability-based optimal planning of electricity and natural gas interconnections for multiple energy hubs" IEEE Transactions on Smart Grid, Vol. 8, No. 4 pp. 1658-1667, 2015. [ DOI:10.1109/TSG.2015.2498166] 26. [7] M. Schulze, L. Friedrich, M. Gautschi, "Modeling and optimization of renewables: applying the energy hub approach" IEEE International Conference on Sustainable Energy Technologies, 2008. [ DOI:10.1109/ICSET.2008.4746977] 27. [8] M.H. Barmayoon, M. Fotuhi-Firuzabad, A. Rajabi-Ghahnavieh, M. Moeini-Aghtaie, "Energy storage in renewable-based residential energy hubs" IET Generation, Transmission & Distribution, Vol. 10, No. 13, pp. 3127-3134, 2016. [ DOI:10.1049/iet-gtd.2015.0957] 28. [9] L. Ju, Z. Tan, J. Yuan, Q. Tan, H. Li, F. Dong, "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind-photovoltaic-energy storage system considering the uncertainty and demand response" Applied energy, Vol. 171, pp. 184-199, 2016. [ DOI:10.1016/j.apenergy.2016.03.020] 29. [10] A. Saleh-Aziz, M. Faridun-Naim-Tajuddin, M. Rafi-Adzman, A. Azmi, M. A-M-Ramli, "Optimization and sensitivity analysis of standalone hybrid energy systems for rural electrification: A case study of Iraq" Renewable Energy, Vol. 138, pp. 775-792, 2019. [ DOI:10.1016/j.renene.2019.02.004] 30. [11] P. Elia-Campana, L. Wästhage, W. Nookuea, Y. Tan, J. Yan, "Optimization and assessment of floating and floating-tracking PV systems integrated in on-and off-grid hybrid energy systems" Solar Energy, Vol. 177, pp. 782-795, 2019. [ DOI:10.1016/j.solener.2018.11.045] 31. [12] R. Rajbongshi, B. Devashree, S. Mahapatra, "Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER" Energy, Vol. 126, pp. 461-474, 2017. [ DOI:10.1016/j.energy.2017.03.056] 32. [13] X. Lu, Z. Liu, L. Mi, L. Wang, K. Zhou, N. Feng, "A robust optimization approach for optimal load dispatch of community energy hub" Applied Energy, Vol. 259 pp. 114195, 2020. [ DOI:10.1016/j.apenergy.2019.114195] 33. [14] A. Dolatabadi, B. Mohammadi-Ivatloo, "Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response" Applied Thermal Engineering, Vol. 123, pp. 40-49, 2017. [ DOI:10.1016/j.applthermaleng.2017.05.069] 34. [15] R. Makbul. AM. Bouchekari, H.R.E.H. Alghamdi, S. Abdulsalam, "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm" Renewable Energy, Vol. 10, pp. 14-27, 2018. 35. [16] Z. Jia, H. Yu, Y. Li, Y. Tan, Y. Cao, C. Huang, "CHP-based DG allocation considering the operation constraints of heating and gas systems" 3rd International Conference on Systems and Informatics, Vol. 123, pp. 301-305, 2016. [ DOI:10.1109/ICSAI.2016.7810972] 36. [17] X.S. Yang, X. He, "Bat algorithm: Literature review and applications" Int. J. Bio-Inspired Computation, Vol. 5, No. 3, pp. 141-149, 2013. [ DOI:10.1504/IJBIC.2013.055093] 37. [18] S. Yılmaz, E.U. Küçüksille, "A new modification approach on bat algorithm for solving optimization problems" Applied Soft Computing, Vol. 28, pp. 259-275, 2015. [ DOI:10.1016/j.asoc.2014.11.029] 38. [19] H.P. Hong, "An efficient point estimate method for probabilistic analysis. Reliability Engineering Systems Safety, Vol. 59, No. 3, pp. 261-267, 1998. [ DOI:10.1016/S0951-8320(97)00071-9] |
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