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Groundwater safe yield powered by clean wind energy

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Abstract

Wind energy has been used by humans for thousands of years. Yet, the relatively low economic cost and availability of fossil fuels upstaged the use of wind power. Fossil fuel resources are not renewable and will decline until exhaustion in the future. At the same time, humans have become aware of the adverse effects on the environment caused by reliance on fossil fuel energy. Wind, on the other hand, is a renewable energy source with minimal adverse environmental impacts that does not involve greenhouse gas emissions. Agricultural irrigation systems use fossil fuel energy resources in various forms. Groundwater withdrawal is central to supplying agricultural water demand in arid and semi-arid regions. Such withdrawal is mostly based on water extraction with pumps powered by diesel, gasoline, or electricity (which is commonly produced by fossil fuels). This paper coupled the non-sorted genetic algorithm (NSGA-II) as the optimization tool to the mathematical formulation of the wind-powered groundwater production problem to determine the potential of wind energy for groundwater withdrawal in an arid area. The optimal safe yield and the optimal size of regulation reservoir are determined considering two objectives: (1) maximizing total extraction of groundwater and (2) minimizing the cost of reservoir construction. The safe yield and the two objectives are optimized for periods lasting 1, 2, 3, 4, and 6 months over a 1-year planning horizon. This paper’s methodology is evaluated with groundwater and wind-power data pertinent to Eghlid, Iran. The optimal safe yield increases by increasing the period length. Specifically, increasing the period length from 1 to 6 months increases the safe yield from 12 to 29 m3. Application of the proposed NSGA-II-based optimization of groundwater production identifies the best design and operational variables with computational efficiency and accuracy.

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References

  • Ahmed, N. M., Farghally, H. M., & Fahmy, F. H. (2017). Optimal sizing and economical analysis of PV–wind hybrid power system for water irrigation using genetic algorithm. International Journal of Electrical & Computer Engineering, 7(4), 1797–1814.

    Google Scholar 

  • Al Suleimani, Z., & Rao, N. R. (2000). Wind-powered electric water-pumping system installed in a remote location. Applied Energy, 65(1-4), 339–347.

    Google Scholar 

  • Awad, S. A. (2019). Practical design and testing of wind driven water pumping systems. International Journal of Mechanical Engineering and Technology, 10(3), 1419–1430.

    Google Scholar 

  • Badran, O. (2003). Wind turbine utilization for water pumping in Jordan. Journal of Wind Engineering and Industrial Aerodynamics, 91(10), 1203–1214.

    Google Scholar 

  • Bakos, G. C. (2002). Feasibility study of a hybrid wind/hydro power-system for low-cost electricity production. Applied Energy, 72(3-4), 599–608.

    Google Scholar 

  • Bañosa, R., Manzano-Agugliarob, F., Montoyab, C. G., Alcaydeb, A., & Gómezc, J. (2011). Optimization methods applied to renewable and sustainable energy: a review. Renewable and Sustainable Energy Reviews, 15(4), 1753–1766.

    Google Scholar 

  • Bhuiyan, A. A., Sadrul Islam, A., & Ibne Alam, A. (2011). Application of wind resource assessment (WEA) tool: a case study in Kuakata, Bangladesh. International Journal of Renewable Energy Research, 1(3), 192–199.

    Google Scholar 

  • Bozorg-Haddad, O., Afshar, A., & Mariño, M. A. (2009). Optimization of non-convex water resource problems by honey-bee mating optimization (HBMO) algorithm. Engineering Computations (Swansea, Wales), 26(3), 267–280. https://doi.org/10.1108/02644400910943617.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Mirmomeni, M., & Mariño, M. A. (2010). Optimal design of stepped spillways using the HBMO algorithm. Civil Engineering and Environmental Systems, 27(1), 81–94. https://doi.org/10.1080/10286600802542465.

    Article  Google Scholar 

  • Brahmi, N., & Chaabene, M. (2012). Sizing optimization of a wind pumping plant: case study in Sfax, Tunisia. Journal of Renewable and Sustainable Energy, 4(1), 013114–013114.

    Google Scholar 

  • Brown-Manrique, C., Mendez-Jurio, N., & Espinosa, M. B. (2018). Evaluation of a micro irrigation system powered by wind energy. Revista Ciencias Técnicas Agropecuarias, 27(1), 13–21.

    Google Scholar 

  • Cloutier, M., & Rowley, P. (2011). The feasibility of renewable energy sources for pumping clean water in sub-Saharan Africa: a case study for Central Nigeria. Journal of Renewable Energy, 36(8), 2220–2226.

    Google Scholar 

  • Deb, K., Agrawal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9, 115–148.

  • Deb, K., Goyal, M. (1999). A robust optimization procedure for mechanical component design based on genetic adaptive search. ASME Journal of Mechanical Design.

  • Fallah-Mehdipour, E., Bozorg-Haddad, O., & Mariño, M. A. (2013). Extraction of optimal operation rules in an aquifer–dam system: genetic programming approach. Journal of Irrigation and Drainage Engineering, 139(10), 872–879. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000628.

    Article  Google Scholar 

  • Fallah-Mehdipour, E., Bozorg-Haddad, O., & Mariño, M. A. (2014). Genetic programming in groundwater modeling. Journal of Hydrologic Engineering, 19(12), 04014031. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000987.

    Article  Google Scholar 

  • Fripp, M. (2011). Greenhouse gas emissions from operating reserves used to backup large-scale wind power. Environmental Science and Technology, 45(20), 9405–9412.

    CAS  Google Scholar 

  • Garcia-Gonzalez, J., Moraga, R., Matres, L., & Mateo, A. (2008). Stochastic joint optimization of wind generation and pumped storage units in an electricity market. IEEE Transactions on Power Systems, 23(2), 460–468.

    Google Scholar 

  • Isaias, D., Cuamba, B., & Leao, A. (2019). A review on renewable energy systems for irrigation in arid and semi-arid regions. Journal of Power and Energy Engineering, 7(10), 21–58. https://doi.org/10.4236/jpee.2019.710002.

    Article  Google Scholar 

  • Jahandideh-Tehrani, M., Bozorg-Haddad, O., & Loáiciga, H. A. (2019). Application of non-animal-inspired evolutionary algorithms to reservoir operation: an overview. Environmental Monitoring and Assessment, 191(7), 439. https://doi.org/10.1007/s10661-019-7581-2.

    Article  Google Scholar 

  • Jain, P. (2011). Wind energy engineering. USA: McGraw-Hill Companies.

    Google Scholar 

  • Keshtkar, H., Bozorg-Haddad, O., Jalali, M. R., & Loáiciga, H. A. (2015). Evaluation of the safe yield of groundwater production derived from wind energy. Journal of Energy Engineering, 141(4), 04014045. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000240.

    Article  Google Scholar 

  • Keshtkar, H., Bozorg Haddad, O., Jalali, M.-R., & Loáiciga, H. A. (2016). Application of wind energy to withdraw groundwater for irrigation management. Journal of Water Resources Planning and Management, 142(12). https://doi.org/10.1061/(ASCE)WR.1943-5452.0000706.

  • Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., & Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Journal of Energy, 35(1), 188–201.

    Google Scholar 

  • Khattab, N.M., Badr, M. A., El Shenawy, E.T., Sharawy, H.H., and Shalaby, M.S. (2020). Economic analysis of stand-alone hybrid wind/PV/diesel water pumping system: a case study in Egypt. In: Modeling, simulation and optimization of wind farms and hybrid systems. DOI: https://doi.org/10.5772/intechopen.89161

  • Kose, F., Aksoy, M. H., & Ozgoren, M. (2019). Experimental investigation of solar/wind hybrid system for irrigation in Konya, Turkey. Thermal Science, 23(6B), 4129–4139.

    Google Scholar 

  • Kusiak, A., Zheng, H., & Song, Z. (2009). Models for monitoring wind farm power. Renewable Energy, 34(3), 583–590.

    Google Scholar 

  • Lara, D., Merino, G. G., Pavez, B. J., & Tapia, J. A. (2010). Efficiency assessment of a wind pumping system. Energy Conversion and Management, 52(2), 795–803.

    Google Scholar 

  • Loáiciga, H. A. (2011). Challenges to phasing out fossil fuels as the major source of the world’s energy. Energy & Environment, 22(11), 659–679.

    Google Scholar 

  • Lu, L., Yang, H., & Burnett, J. (2002). Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics. Renewable Energy, 27(1), 1–12.

    CAS  Google Scholar 

  • Mohsen, M. S., & Akash, B. A. (1998). Potentials of wind energy development for water pumping in Jordan. Renewable Energy, 14(1-4), 441–446.

    Google Scholar 

  • Mustakerov, I., & Borissova, D. (2010). Wind turbines type and number choice using combinatorial optimization. Renewable Energy, 35(9), 1887–1894.

    Google Scholar 

  • Notton, G., Lazarov, V., & Stoyanov, L. (2011). Analysis of pumped hydroelectric storage for a wind/PV system for grid integration. Journal of Ecological Engineering and Environment Protection, 1(8), 64–74.

    Google Scholar 

  • Paul, S. S., Oyedep, S. O., & Adaramola, M. S. (2012). Economic assessment of water pumping systems using wind energy conversion systems in the southern part of Nigeria. Energy Exploration & Exploitation, 30(1), 1–18.

    Google Scholar 

  • Ramoji, S. K., & Kumar, B. J. (2014). Optimal economical sizing of a PV–wind hybrid energy system using genetic algorithm and teaching learning based optimization. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(2), 7353–7367.

    Google Scholar 

  • Rao, R. V., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535–560.

    Google Scholar 

  • Sabbaghpour, S., Naghashzadehgan, M., Javaherdeh, K., & Bozorg-Haddad, O. (2012). HBMO algorithm for calibrating water distribution network of Langarud city. Water Science and Technology, 65(9), 1564–1569. https://doi.org/10.2166/wst.2012.045.

    Article  Google Scholar 

  • Sarzaeim, P., Bozorg-Haddad, O., & Chu, X. (2018). Teaching–learning-based optimization (TLBO) algorithm. In O. Bozorg-Haddad (Ed.), Advanced optimization by nature-inspired algorithms. Studies in computational intelligence, vol. 720. Singapore: Springer.

    Google Scholar 

  • Senjyua, T., Kanekoa, T., & Ueharaa, A. (2009). Output power control for large wind power penetration in small power system. Renewable Energy, 34(11), 2334–2343.

    Google Scholar 

  • Shaik, N., Pavan, K. B., Arun, B. B., Jnanendra, A., & Vijay, K. K. (2019). Design and fabrication of water pumping system using wind mill. International Journal of Management, IT and Engineering, 9(5), 140–152.

    Google Scholar 

  • Singh, S. S., & Fernandez, E. (2018). Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system. Energy, 143, 719–731.

    Google Scholar 

  • Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Journal of Evolutionary Computation, 2(3), 221–248.

    Google Scholar 

  • Ssenyimba, S., Kiggundu, N., & Banadda, N. (2020). Designing a solar and wind hybrid system for small-scale irrigation: a case study for Kalangala district in Uganda. Energy, Sustainability and Society, 10(6). https://doi.org/10.1186/s13705-020-0240-1.

  • Sun, X., Zhou, W., Huang, D., & Wu, G. (2011). Preliminary study on the matching characteristics between wind wheel and pump in a wind-powered water pumping system. Journal of Renewable and Sustainable Energy, 3(2), 023109.

    Google Scholar 

  • Tahani, M., Servati, P., Hajinezhad, A., Nooraollahi, Y., & Ziaee, E. (2015). Assessment of wind energy use to store the water for generation power with two stage optimization method. Journal of Renewable Energy and Environment (JREE), 2(2), 23–28.

    Google Scholar 

  • The MathWorks. (1993). MATLAB User's Guide. Natick: The MathWorks, Inc.

  • Valdès, L. C., & Raniriharinosy, K. (2001). Low technical wind pumping of high efficiency. Renewable Energy, 24, 275–301.

    Google Scholar 

  • Vieira, F., & Ramos, H. M. (2009). Optimization of operational planning for wind/hydro hybrid water supply systems. Renewable Energy, 34(3), 928–936.

    CAS  Google Scholar 

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Acknowledgments

The authors thank Iran’s National Science Foundation (INSF) for its financial support of this research.

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Authors and Affiliations

  1. Department of Irrigation and Reclamation, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, Iran

    Hilda Keshtkar & Omid Bozorg-Haddad

  2. Department of Water and Energy, Moshanir Consultant Co., Tehran, Iran

    Elahe Fallah-Mehdipour

  3. Department of Geography, University of California, Santa Barbara, CA, USA

    Hugo A. Loáiciga

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  1. Hilda Keshtkar
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  2. Omid Bozorg-Haddad
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  3. Elahe Fallah-Mehdipour
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Correspondence to Omid Bozorg-Haddad.

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Keshtkar, H., Bozorg-Haddad, O., Fallah-Mehdipour, E. et al. Groundwater safe yield powered by clean wind energy. Environ Monit Assess 192, 419 (2020). https://doi.org/10.1007/s10661-020-08372-5

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