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Agent-Based Modeling for Water–Energy–Food Nexus and Its Application in Ningdong Energy and Chemical Base

Author

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  • Meilian Zhu

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China)

  • Guoli Yang

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China)

  • Yanan Jiang

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
    Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest A&F University, Ministry of Education, Xianyang 712100, China)

  • Xiaojun Wang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
    Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China)

Abstract

Water, Energy and Food (WEF) are coordinated and constrained by each other, constituting a multivariate coupled feed-forward dynamical system. Traditional modeling and simulation methods struggle to model and simulate complex interactions in the WEF nexus. Therefore, we proposed and developed an agent-based model, which is one of the most effective tools for simulating complex systems. It also has unique advantages in simulating WEF allocation, which is very helpful in improving regional WEF use efficiency. By taking Ningdong Energy and Chemical Base as the research area, an agent-based water–energy–food model based on MESA library was developed using Python 3.9 language, which includes six types of agents and can explore and simulate the complex dynamic interactions in the supply and demand process of WEF sectors. Different behavior rules were proposed to quantify the interactions between WEF sectors of Ningdong Energy and Chemical Base. Four different scenarios were set up, namely, the baseline scenario, the water conservation scenario, the new reservoir scenario and the integrated scenario, and the uncertain system evolution processes between departments and resources under the four different scenarios were analyzed in detail to quantitatively analyze the evolution of the water–energy–food complex system of Ningdong Energy and Chemical Base, which has proven the effectiveness of the proposed model. The results show that: water allocation, energy consumption and food consumption in the domestic sector have similar degrees of impact, because the natural population growth rate does not change under different scenarios; water allocation in the food sector shows a trend corresponding to changes in crop yields; water allocation in the energy management sector shows an upward trend, the water allocation in the actual years 2016–2020 is almost the same, and in the forecast years 2021–2025, the baseline scenario and the water conservation scenario can’t meet the demand volume of the energy management sector due to limited water sources, so the total allocated water is lower than that in the increased reservoir and comprehensive scenario; the water allocated to ecological sector has a balanced situation, and the annual growth of the ecological greening coverage area is also balanced; the total water allocation also shows a trend of annual growth; regarding the annual energy volume that can be delivered to the area outside the base, the curve first grows sharply with a growth rate of about 19.85%, and then becomes slowly with a growth rate of about 3.53%. The total volume is expected to increase to 4.96 × 10 7 tce by 2025; the total energy, consumed energy and output energy, in general, shows a growing trend, and with the development of the economy and technology, the total energy of the base will reach 7.96 × 10 7 tce by 2025.

Suggested Citation

  • Meilian Zhu & Guoli Yang & Yanan Jiang & Xiaojun Wang, 2023. "Agent-Based Modeling for Water–Energy–Food Nexus and Its Application in Ningdong Energy and Chemical Base," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11428-:d:1200673
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    References listed on IDEAS

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    1. Lubega, William N. & Farid, Amro M., 2014. "Quantitative engineering systems modeling and analysis of the energy–water nexus," Applied Energy, Elsevier, vol. 135(C), pages 142-157.
    2. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    3. Bieber, Niclas & Ker, Jen Ho & Wang, Xiaonan & Triantafyllidis, Charalampos & van Dam, Koen H. & Koppelaar, Rembrandt H.E.M. & Shah, Nilay, 2018. "Sustainable planning of the energy-water-food nexus using decision making tools," Energy Policy, Elsevier, vol. 113(C), pages 584-607.
    4. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    5. Kaegi, M. & Mock, R. & Kröger, W., 2009. "Analyzing maintenance strategies by agent-based simulations: A feasibility study," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1416-1421.
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