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A Mathematical Optimization Framework for Managing the Renewable Energy to Attain Maximum Power

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Abstract

Renewable energy generation is a critical component of society’s long-term sustainability, and numerous energy sources, including biogas, solar, biomass, and wind, must be fully utilized to satisfy current demands. Different countries are off the grid. This study includes a real-life case study. Researchers created a mathematical optimization technique utilizing a CPLEX analyzer to produce the required solar capacity simultaneously reducing the cost of energy (COE) to match the requirements of this settlement. Various situations were examined in this research about present energy availability, for instance, at varying periods of each year, economic expenses, the feasibility of sources including such biomass and biogas, and the profitability of wind power generation given the related high prices. Lastly, the study investigated the impact of geothermal power generation on carbon dioxide emission.

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References

  1. Sanchirico, J.N.; Wilen, J.E.: Optimal spatial management of renewable resources: matching policy scope to ecosystem scale. J. Environ. Econ. Manag. 50(1), 23–46 (2005). https://doi.org/10.1016/j.jeem.2004.11.001

    Article  MATH  Google Scholar 

  2. Hilborn, R.; Walters, C.J.; Ludwig, D.: Sustainable exploitation of renewable resources. Annu. Rev. Ecol. Syst. 26(1), 45–67 (1995). https://doi.org/10.1146/annurev.es.26.110195.000401

    Article  Google Scholar 

  3. Clark, C.W.: Mathematical models in the economics of renewable resources. SIAM Rev. 21(1), 81–99 (1979). https://doi.org/10.1137/1021006

    Article  MathSciNet  MATH  Google Scholar 

  4. Cai, Y.P.; Huang, G.H.; Yang, Z.F.; Lin, Q.G.; Tan, Q.: Community-scale renewable energy systems planning under uncertainty—An interval chance-constrained programming approach. Renew. Sustain. Energy Rev. 13(4), 721–735 (2009). https://doi.org/10.1016/j.rser.2008.01.008

    Article  Google Scholar 

  5. Veluchamy, K.; Veluchamy, M.: A new energy management technique for microgrid system using muddy soil fish optimization algorithm. Int J Energy Res 45(10), 14824–14844 (2021). https://doi.org/10.1002/er.6758

    Article  Google Scholar 

  6. Wei, X.; Xiangning, X.; Pengwei, C.: Overview of key microgrid technologies. Int. Trans. Electr. Energy Syst. 28(7), e2566 (2018). https://doi.org/10.1002/etep.2566

    Article  Google Scholar 

  7. Bernal-Agustín, J.L.; Dufo-López, R.: Simulation and optimization of stand-alone hybrid renewable energy systems. Renew. Sustain. Energy Rev. 13(8), 2111–2118 (2009). https://doi.org/10.1016/j.rser.2009.01.010

    Article  Google Scholar 

  8. Alonso, M.; Amaris, H.; Alvarez-Ortega, C.: Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms. Expert Syst. Appl. 39(5), 5513–5522 (2012). https://doi.org/10.1016/j.eswa.2011.11.069

    Article  Google Scholar 

  9. Abdelshafy, A.M.; Jurasz, J.; Hassan, H.; Mohamed, A.M.: Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources. Energy 192, 116615 (2020). https://doi.org/10.1016/j.energy.2019.116615

    Article  Google Scholar 

  10. Restrepo, M.; Cañizares, C.A.; Simpson-Porco, J.W.; Su, P.; Taruc, J.: Optimization- and rule-based energy management systems at the canadian renewable energy laboratory microgrid facility. Appl. Energy 290, 116760 (2021). https://doi.org/10.1016/j.apenergy.2021.116760

    Article  Google Scholar 

  11. Takach, M.; Sarajlić, M.; Peters, D.; Kroener, M.; Schuldt, F.; von Maydell, K.: Review of hydrogen production techniques from water using renewable energy sources and its storage in Salt Caverns. Energies 15(4), 1415 (2022). https://doi.org/10.3390/en15041415

    Article  Google Scholar 

  12. Nagadurga, T.; Narasimham, P.; Vakula, V.: Global maximum power point tracking of solar PV strings using the teaching learning based optimisation technique. Int. J. Ambient Energy 43(1), 1883–1894 (2022). https://doi.org/10.1080/01430750.2020.1721327

    Article  Google Scholar 

  13. Femia, N.; Petrone, G.; Spagnuolo, G.; Vitelli, M.: Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron. 20(4), 963–973 (2005). https://doi.org/10.1109/TPEL.2005.850975

    Article  Google Scholar 

  14. Khan, M.J.; Mathew, L.: Comparative study of optimization techniques for renewable energy system. Arch Computat Methods Eng 27(2), 351–360 (2020). https://doi.org/10.1007/s11831-018-09306-8

    Article  Google Scholar 

  15. Wu, W.; Chen, J.; Zhang, B.; Sun, H.: A robust wind power optimization method for look-ahead power dispatch. IEEE Trans. Sustain. Energy 5(2), 507–515 (2014). https://doi.org/10.1109/TSTE.2013.2294467

    Article  Google Scholar 

  16. Kanase-Patil, A.; Saini, R.; Sharma, M.: Sizing of integrated renewable energy system based on load profiles and reliability index for the state of Uttarakhand in India. Renewable Energy 36(11), 2809–2821 (2011). https://doi.org/10.1016/j.renene.2011.04.022

    Article  Google Scholar 

  17. Maheshwari, Z.; Ramakumar, R.: Smart integrated renewable energy systems (SIRES): a novel approach for sustainable development. Energies 10(8), 1145 (2017). https://doi.org/10.3390/en10081145

    Article  Google Scholar 

  18. Masip, Y.; Gutierrez, A.; Morales, J.; Campo, A.; Valín, M.: Integrated renewable energy system based on IREOM model and spatial-temporal series for isolated rural areas in the region of valparaiso, Chile. Energies 12(6), 1110 (2019). https://doi.org/10.3390/en12061110

    Article  Google Scholar 

  19. Masip Macía, Y.; Rodríguez Machuca, P.; Rodríguez Soto, A.A.; Carmona Campos, R.: Green hydrogen value chain in the sustainability for port operations: case study in the region of valparaiso Chile. Sustainability 13(24), 13681 (2021). https://doi.org/10.3390/su132413681

    Article  Google Scholar 

  20. Gómez Sánchez, M.; Macia, Y.M.; Fernández Gil, A.; Castro, C.; Nuñez González, S.M.; Pedrera Yanes, J.: A mathematical model for the optimization of renewable energy systems. Mathematics 9(1), 39 (2020). https://doi.org/10.3390/math9010039

    Article  Google Scholar 

  21. Gómez Sánchez, M.; Macia, Y.M.; Fernández Gil, A.; Castro, C.; Nuñez González, S.M.; Pedrera Yanes, J.: A mathematical model for the optimization of renewable energy systems. Mathematics 9(1), 39 (2020). https://doi.org/10.3390/math9010039

    Article  Google Scholar 

  22. Tambunan, H.B., et al.: The challenges and opportunities of renewable energy source (RES) penetration in Indonesia: case study of Java-Bali power system. Energies 13(22), 5903 (2020). https://doi.org/10.3390/en13225903

    Article  Google Scholar 

  23. Fan, L.; Liu, G.; Wang, F.; Geissen, V.; Ritsema, C.J.: Factors affecting domestic water consumption in rural households upon access to improved water supply: Insights from the Wei River Basin, China. PLoS ONE 8(8), e71977 (2013). https://doi.org/10.1371/journal.pone.0071977

    Article  Google Scholar 

  24. Kang, D.; Jung, T.Y.: Renewable energy options for a rural village in North Korea. Sustainability 12(6), 2452 (2020)

    Article  Google Scholar 

  25. Albogamy, F.R., et al.: Real-time energy management and load scheduling with renewable energy integration in smart grid. Sustainability 14(3), 1792 (2022). https://doi.org/10.3390/su14031792

    Article  Google Scholar 

  26. Reddy, S.S.: Optimization of renewable energy resources in hybrid energy systems. J. Green Eng. 7(1), 43–60 (2017). https://doi.org/10.13052/jge1904-4720.7123

    Article  Google Scholar 

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Acknowledgements

This research is supported by Taif University Researchers Supporting Project number (TURSP-2020/305), Taif University, Taif, Saudi Arabia.

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Correspondence to Mahmoud M. Selim.

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Selim, M.M., Althobaiti, S. A Mathematical Optimization Framework for Managing the Renewable Energy to Attain Maximum Power. Arab J Sci Eng 48, 8021–8034 (2023). https://doi.org/10.1007/s13369-022-07396-y

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