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Research Article

Capacity configuration optimization of a hybrid renewable energy system with hydrogen storage

, , , , &
Pages 1583-1599 | Received 31 Aug 2021, Accepted 07 Dec 2021, Published online: 19 Jan 2022

References

  • Al-Ghussain, L., A. D. Ahmad, A. M. Abubaker, M. Abujubbeh, A. Almalaq, and M. A. Mohamed. 2021a. A demand-supply matching-based approach for mapping renewable resources towards 100% renewable grids in 2050. IEEE Access 9:58634–51. doi:10.1109/ACCESS.2021.3072969.
  • Al-Ghussain, L., A. Darwish Ahmad, A. M. Abubaker, and M. A. Mohamed. 2021b. An integrated photovoltaic/wind/biomass and hybrid energy storage systems towards 100% renewable energy microgrids in university campuses. Sustainable Energy Technologies and Assessments 46:101273. doi:10.1016/j.seta.2021.101273.
  • Alkhayat, G., and R. Mehmood. 2021. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI 4:100060. doi:10.1016/j.egyai.2021.100060.
  • Alotto, P., M. Guarnieri, and F. Moro. 2014. Redox flow batteries for the storage of renewable energy: A review. Renewable and Sustainable Energy Reviews 29:325–35. doi:10.1016/j.rser.2013.08.001.
  • Aneke, M., and M. Wang. 2016. Energy storage technologies and real life applications – A state of the art review. Applied Energy 179:350–77. doi:10.1016/j.apenergy.2016.06.097.
  • Bahramara, S., M. P. Moghaddam, and S. E. M.R.J.R. Haghifam. 2016. Reviews, Optimal planning of hybrid renewable energy systems using HOMER: A review. Renewable and Sustainable Energy Reviews 62:609–20. doi:10.1016/j.rser.2016.05.039.
  • Bernal-Agustín, J. L., and R. Dufo-López. 2009. Efficient design of hybrid renewable energy systems using evolutionary algorithms. Energy Conversion and Management 50 (3):479–89. doi:10.1016/j.enconman.2008.11.007.
  • Bi, J., J. Tong, W. Chen, and M. J. E. Xian. 2013. Research on storage capacity of compressed air pumped hydro energy storage equipment Energy and Power Engineering 05:26–30. doi: 10.4236/epe.2013.54B005.
  • Boretti, A. 2021. Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city. Energy and AI 3:100038. doi:10.1016/j.egyai.2020.100038.
  • Borhanazad, H., S. Mekhilef, V. Gounder Ganapathy, M. Modiri-Delshad, and A. Mirtaheri. 2014. Optimization of micro-grid system using MOPSO. Renewable Energy 71:295–306. doi:10.1016/j.renene.2014.05.006.
  • Cano, A., F. Jurado, H. Sánchez, L. M. Fernández, and M. Castañeda. 2014. Optimal sizing of stand-alone hybrid systems based on PV/WT/FC by using several methodologies. Journal of the Energy Institute 87 (4):330–40. doi:10.1016/j.joei.2014.03.028.
  • Castañeda, M., A. Cano, F. Jurado, H. Sánchez, and L. M. Fernández. 2013. Sizing optimization, dynamic modeling and energy management strategies of a stand-alone PV/hydrogen/battery-based hybrid system. International Journal of Hydrogen Energy 38 (10):3830–45. doi:10.1016/j.ijhydene.2013.01.080.
  • Chengzhen, J., W. Lingmei, M. Enlong, Y. Derong, G. Dongjie, and L. Yushan. 2020. Optimal capacity configuration and day-ahead scheduling of wind-solar-hydrogen coupled power generation system. ELECTRIC POWER 53:80–87. doi:10.11930/j.1004-9649.201909003.
  • Dufo-López, R., and J. L. Bernal-Agustín. 2008. Multi-objective design of PV–wind–diesel–hydrogen–battery systems. Renewable Energy 33 (12):2559–72. doi:10.1016/j.renene.2008.02.027.
  • Eberhart, S. Y. 2001. Particle swarm optimization: Developments, applications and resources, Proceedings of the 2001 Congress on Evolutionary Computation, IEEE Cat. No. 01TH8546, Seoul, Korea (South). 81: 81–86. DOI: 10.1109/CEC.2001.934374
  • Ekren, O., and B. Y. Ekren. 2010. Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing. Applied Energy 87 (2):592–98. doi:10.1016/j.apenergy.2009.05.022.
  • Eltamaly, A. M., E. Ali, M. Bumazza, S. Mulyono, and M. Yasin. 2021a. Optimal design of hybrid renewable energy system for a reverse osmosis desalination system in arar, Saudi Arabia. Arabian Journal for Science and Engineering 46 (10):9879–97. doi:10.1007/s13369-021-05645-0.
  • Eltamaly, A. M., and M. A. Alotaibi. 2021. Novel fuzzy-swarm optimization for sizing of hybrid energy systems applying smart grid concepts. IEEE Access 9:93629–50. doi:10.1109/ACCESS.2021.3093169.
  • Eltamaly, A. M., M. A. Alotaibi, A. I. Alolah, and M. A. Ahmed. 2021b. A novel demand response strategy for sizing of hybrid energy system with smart grid concepts. IEEE Access 9:20277–94. doi:10.1109/ACCESS.2021.3052128.
  • Frank, M., R. Deja, R. Peters, L. Blum, and D. Stolten. 2018. Bypassing renewable variability with a reversible solid oxide cell plant. Applied Energy 217:101–12. doi:10.1016/j.apenergy.2018.02.115.
  • Fu, H., Q. He, J. Song, X. Shi, Y. Hao, D. Du, and W. Liu. 2021. Thermodynamic of a novel advanced adiabatic compressed air energy storage system with variable pressure ratio coupled organic rankine cycle. Energy 227:120411. doi:10.1016/j.energy.2021.120411.
  • Fu, P., D. Pudjianto, X. Zhang, and G. Strbac. 2020. Integration of hydrogen into multi-energy systems optimisation. Energies 13 (7):1606. doi:10.3390/en13071606.
  • Gharibi, M., and A. Askarzadeh. 2019. Size and power exchange optimization of a grid-connected diesel generator-photovoltaic-fuel cell hybrid energy system considering reliability, cost and renewability. International Journal of Hydrogen Energy 44 (47):25428–41. doi:10.1016/j.ijhydene.2019.08.007.
  • Hajiaghasi, S., A. Salemnia, and M. Hamzeh. 2019. Hybrid energy storage system for microgrids applications: A review. Journal of Energy Storage 21:543–70. doi:10.1016/j.est.2018.12.017.
  • HassanzadehFard, H., F. Tooryan, E. R. Collins, S. Jin, and B. Ramezani. 2020. Design and optimum energy management of a hybrid renewable energy system based on efficient various hydrogen production. International Journal of Hydrogen Energy 45 (55):30113–28. doi:10.1016/j.ijhydene.2020.08.040.
  • Hernández-Pacheco, E., D. Singh, P. N. Hutton, N. Patel, and M. D. Mann. 2004. A macro-level model for determining the performance characteristics of solid oxide fuel cells. Journal of Power Sources 138 (1–2):174–86. doi:10.1016/j.jpowsour.2004.06.051.
  • Hussain, M. M., X. Li, and I. Dincer. 2009. A general electrolyte–electrode-assembly model for the performance characteristics of planar anode-supported solid oxide fuel cells. Journal of Power Sources 189 (2):916–28. doi:10.1016/j.jpowsour.2008.12.121.
  • Ismail, T. M., K. Ramzy, B. E. Elnaghi, M. N. Abelwhab, and M. Abd El-Salam. 2019. Using matlab to model and simulate a photovoltaic system to produce hydrogen. Energy Conversion and Management 185:101–29. doi:10.1016/j.enconman.2019.01.108.
  • Jintian, L. Y. Y. I. N., L. I. U. Li, and W. U. Tiebin. 2014. Efficient hybrid bat algorithm. Computer Engineering and Applications 50:62–66. doi:10.3778/j.1002-8331.1308-0334.
  • Kashefi Kaviani, A., G. H. Riahy, and S. M. Kouhsari. 2009. Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages. Renewable Energy 34 (11):2380–90. doi:10.1016/j.renene.2009.03.020.
  • Kong, L., G. Cai, S. Xue, and S. J. M. P. I. E. Li. 2015. Modeling and coordinated control strategy of large scale grid-connected wind/photovoltaic/energy storage hybrid energy conversion system. Mathematical Problems in Engineering 2015:1–14. doi:10.1155/2015/682321.
  • Koutroulis, E., D. Kolokotsa, A. Potirakis, and K. Kalaitzakis. 2006. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Solar Energy 80 (9):1072–88. doi:10.1016/j.solener.2005.11.002.
  • Lagorse, J., M. G. Simões, A. Miraoui, and P. Costerg. 2008. Energy cost analysis of a solar-hydrogen hybrid energy system for stand-alone applications. International Journal of Hydrogen Energy 33 (12):2871–79. doi:10.1016/j.ijhydene.2008.03.054.
  • Li, Z., C. Xie, P. Peng, X. Gao, Q. J. E. S. Wan, and P. Research. 2021. Multi-objective location-scale optimization model and solution methods for large-scale emergency rescue resources. Environmental Science and Pollution Research:1–14. doi: 10.1007/s11356-021-12753-9.
  • Ligang, W., P.-F. Mar, M. Hossein, Diethelm, V. Stefan, and J. J. A. Herle. 2018. Optimal design of solid-oxide electrolyzer based power-to-methane systems: Acomprehensive comparison between steam electrolysis and co-electrolysis . Applied Energy 211: 1060–1079. doi: 10.1016/j.apenergy.2017.11.050.
  • Ma, T., H. Yang, and L. Lu. 2014. A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote Island. Applied Energy 121:149–58. doi:10.1016/j.apenergy.2014.01.090.
  • Maleki, A., and A. Askarzadeh. 2014a. Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering LPSP concept. Solar Energy 107:227–35. doi:10.1016/j.est.2015.05.006.
  • Maleki, A., and A. Askarzadeh. 2014b. Comparative study of artificial intelligence techniques for sizing of a hydrogen-based stand-alone photovoltaic/wind hybrid system. International Journal of Hydrogen Energy 39 (19):9973–84. doi:10.1016/j.ijhydene.2014.04.147.
  • Maleki, A., and F. Pourfayaz. 2015. Sizing of stand-alone photovoltaic/wind/diesel system with battery and fuel cell storage devices by harmony search algorithm. Journal of Energy Storage 2:30–42. doi:10.1016/j.est.2015.05.006.
  • Nelson, D. B., M. H. Nehrir, and C. Wang. 2006. Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems. Renewable Energy 31 (10):1641–56. doi:10.1016/j.renene.2005.08.031.
  • Ni, M., M. K. H. Leung, and D. Y. C. Leung. 2006. An electrochemical model of a solid oxide steam electrolyzer for hydrogen production. Chemical Engineering & Technology 29 (5):636–42. doi:10.1002/ceat.200500378.
  • Ni, M., M. Leung, and D. Leung. 2007. Parametric study of solid oxide steam electrolyzer for hydrogen production. International Journal of Hydrogen Energy 32 (13):2305–13. doi:10.1016/j.ijhydene.2007.03.001.
  • Obara, S. Y., and S. Watanabe. 2012. Optimization of equipment capacity and an operational method based on cost analysis of a fuel cell microgrid. International Journal of Hydrogen Energy 37 (9):7814–30. doi:10.1016/j.ijhydene.2012.02.005.
  • Rezaei, M., U. Dampage, B. K. Das, O. Nasif, P. F. Borowski, and M. A. Mohamed. 2021. Investigating the impact of economic uncertainty on optimal sizing of grid-independent hybrid renewable energy systems. Processes 9 (8):1468. doi:10.3390/pr9081468.
  • Ruiming, F. 2019. Multi-objective optimized operation of integrated energy system with hydrogen storage. International Journal of Hydrogen Energy 44 (56):29409–17. doi:10.1016/j.iihydene.2019.02.168.
  • Samy, M. M., S. Barakat, and H. S. Ramadan. 2019. A flower pollination optimization algorithm for an off-grid PV-Fuel cell hybrid renewable system. International Journal of Hydrogen Energy 44 (4):2141–52. doi:10.1016/j.ijhydene.2018.05.127.
  • Shenwen, W., D. Lixin, Z. Wensheng, G. Zhaolu, and X. Chengwang. 2014. Survey of differential evolution. J. Wuhan Univ 60:283–92. doi:10.14188/j.1671-8836.2014.04.016.
  • Xia, T., M. Rezaei, U. Dampage, S. A. Alharbi, O. Nasif, P. F. Borowski, and M. A. Mohamed. 2021. Techno-economic assessment of a grid-independent hybrid power plant for co-supplying a remote micro-community with electricity and hydrogen. Processes 9 (8):1375. doi:10.3390/pr9081375.
  • Xing, X., J. Lin, Y. Song, Q. Hu, Y. Zhou, and S. Mu. 2018. Optimization of hydrogen yield of a high-temperature electrolysis system with coordinated temperature and feed factors at various loading conditions: A model-based study. Applied Energy 232:368–85. doi:10.1016/j.apenergy.2018.09.020.
  • Yao, E., H. Wang, L. Wang, G. Xi, and F. Maréchal. 2017. Multi-objective optimization and exergoeconomic analysis of a combined cooling, heating and power based compressed air energy storage system. Energy Conversion and Management 138:199–209. doi:10.1016/j.enconman.2017.01.071.
  • Zhijian, Q., Z. Xianwei, C. Yanfeng, L. Xiaohong, and F. Xiaohua. 2015. Research on genetic algorithm based on adaptive mechanism. Application Research of Computers 32:3222–3225+3229. doi:10.3969/j.1001-3695.2015.11.004.
  • Zhu, H., and R. J. Kee. 2003. A general mathematical model for analyzing the performance of fuel-cell membrane-electrode assemblies. Journal of Power Sources 117 (1–2):61–74. doi:10.1016/S0378-7753(03)00358-6.

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