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Coupling biogeochemical simulation and mathematical optimisation towards eco-industrial energy systems design

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  • Jing, Rui
  • Li, Yubing
  • Wang, Meng
  • Chachuat, Benoit
  • Lin, Jianyi
  • Guo, Miao

Abstract

Process industry remains one of the difficult-to-decarbonise sectors globally. To mitigate industrial greenhouse gas (GHGs) emissions, an eco-industrial energy systems (e-IES) optimisation framework is proposed by coupling mathematical optimisation with clustering algorithms and first principle modelling. Within the framework, a rooftop farming database was developed using biogeochemical simulations, which models seven crop growth in response to 10 cultivation conditions. Clustering algorithm was applied to analyse energy system data, along with the rooftop farming database, to inform the optimisation model. A Mixed Integer Linear Programming optimisation model was developed to optimize system design considering the trade-off between economic and environmental objectives. The implications of rooftop design on e-IES and their interactive effects on industrial decarbonisation were addressed. A case study at an industrial park in Suzhou China reveals that rooftop farming could generate mutual benefits from both cost and GHG reduction perspectives. Planting lettuce indicates a cost-efficient solution, and planting tomato could contribute the most to GHG emission reduction. Compared to the rooftop PV and the spare rooftop, 2.4% and 5.6% cost savings, as well as 10.2% and 16.3% emission savings, could be achieved respectively by implementing rooftop farming. Overall, this study demonstrates an emerging perspective on decarbonising the industrial sector by coupling biogeochemical simulation and energy system optimisation and adopting cross-disciplinary approaches.

Suggested Citation

  • Jing, Rui & Li, Yubing & Wang, Meng & Chachuat, Benoit & Lin, Jianyi & Guo, Miao, 2021. "Coupling biogeochemical simulation and mathematical optimisation towards eco-industrial energy systems design," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921002798
    DOI: 10.1016/j.apenergy.2021.116773
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    References listed on IDEAS

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    1. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    2. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    3. Gabrielli, Paolo & Fürer, Florian & Mavromatidis, Georgios & Mazzotti, Marco, 2019. "Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis," Applied Energy, Elsevier, vol. 238(C), pages 1192-1210.
    4. Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    5. Jing, Rui & Kuriyan, Kamal & Kong, Qingyuan & Zhang, Zhihui & Shah, Nilay & Li, Ning & Zhao, Yingru, 2019. "Exploring the impact space of different technologies using a portfolio constraint based approach for multi-objective optimization of integrated urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Jing, Rui & Wang, Meng & Liang, Hao & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2018. "Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints," Applied Energy, Elsevier, vol. 231(C), pages 534-548.
    7. Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2018. "Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach," Applied Energy, Elsevier, vol. 230(C), pages 1157-1171.
    8. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    9. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Jing, Rui & Kuriyan, Kamal & Lin, Jian & Shah, Nilay & Zhao, Yingru, 2020. "Quantifying the contribution of individual technologies in integrated urban energy systems – A system value approach," Applied Energy, Elsevier, vol. 266(C).
    11. Zhu, Xu & Yang, Jun & Pan, Xueli & Li, Gaojunjie & Rao, Yingqing, 2020. "Regional integrated energy system energy management in an industrial park considering energy stepped utilization," Energy, Elsevier, vol. 201(C).
    12. A. T. D. Perera & Vahid M. Nik & Deliang Chen & Jean-Louis Scartezzini & Tianzhen Hong, 2020. "Quantifying the impacts of climate change and extreme climate events on energy systems," Nature Energy, Nature, vol. 5(2), pages 150-159, February.
    13. Liu, Xuezhi & Yan, Zheng & Wu, Jianzhong, 2019. "Optimal coordinated operation of a multi-energy community considering interactions between energy storage and conversion devices," Applied Energy, Elsevier, vol. 248(C), pages 256-273.
    14. Wang, Wei & Jing, Rui & Zhao, Yingru & Zhang, Chuan & Wang, Xiaonan, 2020. "A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization," Applied Energy, Elsevier, vol. 270(C).
    15. Lee, Inkyu & Tester, Jefferson William & You, Fengqi, 2019. "Systems analysis, design, and optimization of geothermal energy systems for power production and polygeneration: State-of-the-art and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 551-577.
    16. Wang, Meng & Yu, Hang & Lin, Xiaoyu & Jing, Rui & He, Fangjun & Li, Chaoen, 2020. "Comparing stochastic programming with posteriori approach for multi-objective optimization of distributed energy systems under uncertainty," Energy, Elsevier, vol. 210(C).
    17. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
    18. Gilhespy, Sarah L. & Anthony, Steven & Cardenas, Laura & Chadwick, David & del Prado, Agustin & Li, Changsheng & Misselbrook, Thomas & Rees, Robert M. & Salas, William & Sanz-Cobena, Alberto & Smith, , 2014. "First 20 years of DNDC (DeNitrification DeComposition): Model evolution," Ecological Modelling, Elsevier, vol. 292(C), pages 51-62.
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    Cited by:

    1. Yan, Kun & Gao, Hanbo & Liu, Rui & Lyu, Yizheng & Wan, Mei & Tian, Jinping & Chen, Lyujun, 2024. "Review on low-carbon development in Chinese industrial parks driven by bioeconomy strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    2. Jing, Rui & Hua, Weiqi & Lin, Jian & Lin, Jianyi & Zhao, Yingru & Zhou, Yue & Wu, Jianzhong, 2022. "Cost-efficient decarbonization of local energy systems by whole-system based design optimization," Applied Energy, Elsevier, vol. 326(C).
    3. Jing, Rui & Liu, Jiahui & Zhang, Haoran & Zhong, Fenglin & Liu, Yupeng & Lin, Jianyi, 2022. "Unlock the hidden potential of urban rooftop agrivoltaics energy-food-nexus," Energy, Elsevier, vol. 256(C).
    4. Wang, Meng & Yu, Hang & Yang, Yikun & Lin, Xiaoyu & Guo, Haijin & Li, Chaoen & Zhou, Yue & Jing, Rui, 2021. "Unlocking emerging impacts of carbon tax on integrated energy systems through supply and demand co-optimization," Applied Energy, Elsevier, vol. 302(C).

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