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A Multi-Objective Optimization Model for a Non-Traditional Energy System in Beijing under Climate Change Conditions

Author

Listed:
  • Xiaowen Ding

    () (MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China)

  • Lin Liu

    () (MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China)

  • Guohe Huang

    () (Faculty of Engineering, University of Regina, Regina, SK S4S 0A2, Canada)

  • Ye Xu

    () (MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China)

  • Junhong Guo

    () (MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

In recent years, with the increase of annual average temperature and the decrease of annual precipitation in Beijing, the fragility of Beijing’s energy system has become more and more prominent, especially the balance of electricity supply and demand in extreme weather. In the context of unstable supply of new and renewable energies, it is imperative to strengthen the ability of the energy system to adapt to climate change. This study first simulated climate change in Beijing based on regional climate data. At the same time, the Statistical Program for Social Sciences was used to perform multiple linear regression analysis on Beijing’s future power demand and to analyze the impact of climate change on electricity supply in both the RCP4.5 and RCP8.5 (representative concentration pathway 4.5 and 8.5) scenarios. Based on the analysis of the impact of climate change on energy supply, a multi-objective optimization model for new and renewable energy structure adjustment combined with climate change was proposed. The model was then used to predict the optimal power generation of the five energy types under different conditions in 2020. Through comparison of the results, it was found that the development amount and development ratio of various energy forms underwent certain changes. In the case of climate change, the priority development order of new and renewable energies in Beijing was: external electricity > other renewable energy > solar energy > wind energy > biomass energy. The energy structure adjustment program in the context of climate change will contribute to accelerating the development and utilization of new and renewable energies, alleviating the imbalance between power supply and demand and improving energy security.

Suggested Citation

  • Xiaowen Ding & Lin Liu & Guohe Huang & Ye Xu & Junhong Guo, 2019. "A Multi-Objective Optimization Model for a Non-Traditional Energy System in Beijing under Climate Change Conditions," Energies, MDPI, Open Access Journal, vol. 12(9), pages 1-21, May.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1692-:d:228347
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    References listed on IDEAS

    as
    1. Jing-Li Fan & Bao-Jun Tang & Hao Yu & Yun-Bing Hou & Yi-Ming Wei, 2015. "Impact of climatic factors on monthly electricity consumption of China’s sectors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 2027-2037, January.
    2. repec:eee:enepol:v:115:y:2018:i:c:p:353-365 is not listed on IDEAS
    3. Pašičko, Robert & Branković, Čedo & Šimić, Zdenko, 2012. "Assessment of climate change impacts on energy generation from renewable sources in Croatia," Renewable Energy, Elsevier, vol. 46(C), pages 224-231.
    4. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun & Wu, Qiong, 2010. "Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 87(12), pages 3642-3651, December.
    5. Jing-Li Fan & Bao-Jun Tang & Hao Yu & Yun-Bing Hou & Yi-Ming Wei, 2015. "Impacts of socioeconomic factors on monthly electricity consumption of China’s sectors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 2039-2047, January.
    6. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2016. "Optimization modeling to support renewables integration in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 316-325.
    7. Dong, Cong & Huang, Guohe & Cai, Yanpeng & Cheng, Guanhui & Tan, Qian, 2016. "Bayesian interval robust optimization for sustainable energy system planning in Qiqihar City, China," Energy Economics, Elsevier, vol. 60(C), pages 357-376.
    8. Hdidouan, Daniel & Staffell, Iain, 2017. "The impact of climate change on the levelised cost of wind energy," Renewable Energy, Elsevier, vol. 101(C), pages 575-592.
    9. Amutha, W. Margaret & Rajini, V., 2016. "Cost benefit and technical analysis of rural electrification alternatives in southern India using HOMER," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 236-246.
    10. Schmidt, Johannes & Cancella, Rafael & Pereira, Amaro O., 2016. "An optimal mix of solar PV, wind and hydro power for a low-carbon electricity supply in Brazil," Renewable Energy, Elsevier, vol. 85(C), pages 137-147.
    11. Zhou, Zhongren & Wu, Wenliang & Chen, Qun & Chen, Shufeng, 2008. "Study on sustainable development of rural household energy in northern China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2227-2239, October.
    12. Prebeg, Pero & Gasparovic, Goran & Krajacic, Goran & Duic, Neven, 2016. "Long-term energy planning of Croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 1493-1507.
    13. de Lucena, André Frossard Pereira & Szklo, Alexandre Salem & Schaeffer, Roberto & de Souza, Raquel Rodrigues & Borba, Bruno Soares Moreira Cesar & da Costa, Isabella Vaz Leal & Júnior, Amaro Olimpio P, 2009. "The vulnerability of renewable energy to climate change in Brazil," Energy Policy, Elsevier, vol. 37(3), pages 879-889, March.
    14. repec:eee:eneeco:v:70:y:2018:i:c:p:116-131 is not listed on IDEAS
    15. Ruth, Matthias & Lin, Ai-Chen, 2006. "Regional energy demand and adaptations to climate change: Methodology and application to the state of Maryland, USA," Energy Policy, Elsevier, vol. 34(17), pages 2820-2833, November.
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    More about this item

    Keywords

    electricity demand; optimization model; climate change; energy structure; energy; power generation;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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