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A feasibility study of microgrids for reducing energy use and GHG emissions in an industrial application

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  • Li, Mengyu
  • Zhang, Xiongwen
  • Li, Guojun
  • Jiang, Chaoyang

Abstract

Microgrids provide a new energy paradigm with the benefits of higher energy supply reliability, lower greenhouse gas (GHG) emissions through a higher penetration of renewable sources, higher energy efficiencies through the use of local waste heat and the avoidance of losses in transmission and distribution. This study reports a life cycle assessment (LCA) of microgrids for an industry application of an ammonia plant in central Inner Mongolia, China. The life cycle energy use and GHG emissions of the microgrids are evaluated and compared to the existing fossil fuel-based energy system. The electricity, heat and hydrogen fuel loads of the ammonia plant are all modelled in the study. An optimization model is developed to estimate the minimum life cycle energy use and GHG emissions with the microgrids under three scenarios (natural gas (NG)-based, optimized, and maximum renewable energy microgrids). The results indicate that the use of wind and solar in the NG-based microgrid can only slightly reduce the energy use and GHG emissions. If there are no land area limitations on the deployment of solar and wind power, the maximum renewable energy microgrid offers significant reductions of fossil fuel energy of up to 56.9% and GHG emissions reductions of up to 66.3% compared to the existing energy system.

Suggested Citation

  • Li, Mengyu & Zhang, Xiongwen & Li, Guojun & Jiang, Chaoyang, 2016. "A feasibility study of microgrids for reducing energy use and GHG emissions in an industrial application," Applied Energy, Elsevier, vol. 176(C), pages 138-148.
    • Handle: RePEc:eee:appene:v:176:y:2016:i:c:p:138-148
    DOI: 10.1016/j.apenergy.2016.05.070
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    4. Lamnatou, Chr. & Chemisana, D. & Cristofari, C., 2022. "Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment," Renewable Energy, Elsevier, vol. 185(C), pages 1376-1391.
    5. de la Hoz, Jordi & Martín, Helena & Alonso, Alex & Carolina Luna, Adriana & Matas, José & Vasquez, Juan C. & Guerrero, Josep M., 2019. "Regulatory-framework-embedded energy management system for microgrids: The case study of the Spanish self-consumption scheme," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2018. "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, Elsevier, vol. 222(C), pages 1033-1055.
    7. Mendecka, Barbara & Tribioli, Laura & Cozzolino, Raffaello, 2020. "Life Cycle Assessment of a stand-alone solar-based polygeneration power plant for a commercial building in different climate zones," Renewable Energy, Elsevier, vol. 154(C), pages 1132-1143.
    8. Ren, Lingyu & Qin, Yanyuan & Li, Yan & Zhang, Peng & Wang, Bing & Luh, Peter B. & Han, Song & Orekan, Taofeek & Gong, Tao, 2018. "Enabling resilient distributed power sharing in networked microgrids through software defined networking," Applied Energy, Elsevier, vol. 210(C), pages 1251-1265.
    9. dos Santos Neto, Pedro J. & Barros, Tárcio A.S. & Silveira, Joao P.C. & Ruppert Filho, Ernesto & Vasquez, Juan C. & Guerrero, Josep M., 2020. "Power management techniques for grid-connected DC microgrids: A comparative evaluation," Applied Energy, Elsevier, vol. 269(C).
    10. Fang, Xinli & Yang, Qiang & Dong, Wei, 2018. "Fuzzy decision based energy dispatch in offshore industrial microgrid with desalination process and multi-type DGs," Energy, Elsevier, vol. 148(C), pages 744-755.
    11. Alejandra Tabares & Pablo Cortés, 2024. "Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids," Energies, MDPI, vol. 17(11), pages 1-24, May.
    12. Comodi, Gabriele & Bartolini, Andrea & Carducci, Francesco & Nagaranjan, Balamurugan & Romagnoli, Alessandro, 2019. "Achieving low carbon local energy communities in hot climates by exploiting networks synergies in multi energy systems," Applied Energy, Elsevier, vol. 256(C).
    13. Kou, Peng & Liang, Deliang & Gao, Lin, 2017. "Distributed EMPC of multiple microgrids for coordinated stochastic energy management," Applied Energy, Elsevier, vol. 185(P1), pages 939-952.
    14. Liu, Yixin & Guo, Li & Wang, Chengshan, 2018. "A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 228(C), pages 130-140.
    15. Razeghi, Ghazal & Gu, Fei & Neal, Russell & Samuelsen, Scott, 2018. "A generic microgrid controller: Concept, testing, and insights," Applied Energy, Elsevier, vol. 229(C), pages 660-671.

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