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A comprehensive study on low-carbon impact of distributed generations on regional power grids: A case of Jiangxi provincial power grid in China

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  • Cao, Yijia
  • Wang, Xifan
  • Li, Yong
  • Tan, Yi
  • Xing, Jianbo
  • Fan, Ruixiang

Abstract

Electric power industry is a key to achieve the low-carbon social and economic development. Distributed generation (DG) has great potential in the development of low-carbon power grid because of its non-polluting property and large reserves. Therefore, it is of great importance to have a comprehensive investigation on low-carbon impact of DG on power grids. In this paper, regional power grids are focused as they are crucial supporters of regional economic and social activities and the primary undertakers of low-carbon development. Specifically, first, the carbon dioxide emission coefficient and energy consumption of DG are evaluated from the aspect of its life cycle. Then, the wind farm and photovoltaic power station are compared in terms of carbon dioxide emissions and energy consumption, indicated that wind power is superior in reducing the carbon dioxide emissions. Finally, the carbon intensity of provincial power grid is evaluated by grey prediction model with historical data such as electricity consumption, gross domestic product (GDP), carbon dioxide emission coefficient. Taking Jiangxi provincial power grid as the example, the results demonstrate that the proposed prediction method is of high accuracy and the integration of DGs can effectively reduce carbon dioxide emissions and carbon intensity, thus, DG should be widely integrated into provincial power grids.

Suggested Citation

  • Cao, Yijia & Wang, Xifan & Li, Yong & Tan, Yi & Xing, Jianbo & Fan, Ruixiang, 2016. "A comprehensive study on low-carbon impact of distributed generations on regional power grids: A case of Jiangxi provincial power grid in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 766-778.
  • Handle: RePEc:eee:rensus:v:53:y:2016:i:c:p:766-778
    DOI: 10.1016/j.rser.2015.09.008
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    Cited by:

    1. Wang, Junfeng & He, Shutong & Qiu, Ye & Liu, Nan & Li, Yongjian & Dong, Zhanfeng, 2018. "Investigating driving forces of aggregate carbon intensity of electricity generation in China," Energy Policy, Elsevier, vol. 113(C), pages 249-257.
    2. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    3. Prakash, Prem & Khatod, Dheeraj K., 2016. "Optimal sizing and siting techniques for distributed generation in distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 111-130.
    4. Guerrero-Rodríguez, N.F. & Rey-Boué, Alexis B. & Bueno, E.J. & Ortiz, Octavio & Reyes-Archundia, Enrique, 2017. "Synchronization algorithms for grid-connected renewable systems: Overview, tests and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 629-643.
    5. Burmester, Daniel & Rayudu, Ramesh & Seah, Winston & Akinyele, Daniel, 2017. "A review of nanogrid topologies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 760-775.
    6. Qingyou Yan & Yaxian Wang & Tomas Baležentis & Yikai Sun & Dalia Streimikiene, 2018. "Energy-Related CO 2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement," Energies, MDPI, Open Access Journal, vol. 11(5), pages 1-1, April.
    7. Tian-tian Feng & Yi-sheng Yang & Yu-heng Yang & Dan-dan Wang, 2017. "Application Status and Problem Investigation of Distributed Generation in China: The Case of Natural Gas, Solar and Wind Resources," Sustainability, MDPI, Open Access Journal, vol. 9(6), pages 1-1, June.
    8. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.

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