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A low-carbon planning method for joint regional-district multi-energy systems: From the perspective of privacy protection

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  • Gan, Wei
  • Yan, Mingyu
  • Wen, Jianfeng
  • Yao, Wei
  • Zhang, Jing

Abstract

The construction of the multi-energy system (MES) is regarded as one of the silver bullets that help construct a low-carbon and high-efficiency energy system. In addition to the synergy of multiple energy systems, the coordination of regional and district energy systems can further improve flexibility. However, current studies rarely focus on the joint planning of regional-district MES. Additionally, privacy protection has not been considered in multi-energy system planning yet. This paper proposes a novel low-carbon planning method for joint regional-district MES which ensures the privacy of regional and district energy systems based on the enhanced Benders decomposition. A new Benders cut generation method with refined iteration and improved convergence is designed for the planning model where the subproblem itself is the mixed-integer linear programming. To ensure convergence and optimality, supplementary Benders cuts for convergence restoration are also generated. Numerical results tested on a real-world MES in North China and a modified IEEE RTS-79 40-node MES show the effectiveness of the proposed planning method and solution technique. The simulation results validate that the proposed joint planning method can enhance the economic benefit of planning and reduce carbon emission, and the computational performance of the enhanced Benders decomposition is also validated from the perspectives of both computational accuracy and time. In the real-world MES, the joint planning method saves 8.8% of the total cost and reduces carbon emission by 11.1 % compared to the separate planning method.

Suggested Citation

  • Gan, Wei & Yan, Mingyu & Wen, Jianfeng & Yao, Wei & Zhang, Jing, 2022. "A low-carbon planning method for joint regional-district multi-energy systems: From the perspective of privacy protection," Applied Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922000745
    DOI: 10.1016/j.apenergy.2022.118595
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    References listed on IDEAS

    as
    1. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    2. Qadrdan, Meysam & Cheng, Meng & Wu, Jianzhong & Jenkins, Nick, 2017. "Benefits of demand-side response in combined gas and electricity networks," Applied Energy, Elsevier, vol. 192(C), pages 360-369.
    3. Liu, Xuezhi & Wu, Jianzhong & Jenkins, Nick & Bagdanavicius, Audrius, 2016. "Combined analysis of electricity and heat networks," Applied Energy, Elsevier, vol. 162(C), pages 1238-1250.
    4. Zhang, Ning & Lu, Xi & McElroy, Michael B. & Nielsen, Chris P. & Chen, Xinyu & Deng, Yu & Kang, Chongqing, 2016. "Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage," Applied Energy, Elsevier, vol. 184(C), pages 987-994.
    5. Wang, Yi & Zhang, Ning & Zhuo, Zhenyu & Kang, Chongqing & Kirschen, Daniel, 2018. "Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch," Applied Energy, Elsevier, vol. 210(C), pages 1141-1150.
    6. Gan, Wei & Yan, Mingyu & Yao, Wei & Wen, Jinyu, 2021. "Peer to peer transactive energy for multiple energy hub with the penetration of high-level renewable energy," Applied Energy, Elsevier, vol. 295(C).
    7. Chen, Jian & Yao, Wei & Zhang, Chuan-Ke & Ren, Yaxing & Jiang, Lin, 2019. "Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control," Renewable Energy, Elsevier, vol. 134(C), pages 478-495.
    8. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    9. Chaudry, Modassar & Jenkins, Nick & Qadrdan, Meysam & Wu, Jianzhong, 2014. "Combined gas and electricity network expansion planning," Applied Energy, Elsevier, vol. 113(C), pages 1171-1187.
    10. Wang, Qin & Yao, Wei & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & Yang, Xiaobo & Xie, Hailian & Huang, Xing, 2020. "Dynamic modeling and small signal stability analysis of distributed photovoltaic grid-connected system with large scale of panel level DC optimizers," Applied Energy, Elsevier, vol. 259(C).
    11. Gan, Wei & Ai, Xiaomeng & Fang, Jiakun & Yan, Mingyu & Yao, Wei & Zuo, Wenping & Wen, Jinyu, 2019. "Security constrained co-planning of transmission expansion and energy storage," Applied Energy, Elsevier, vol. 239(C), pages 383-394.
    12. Cui, Hantao & Li, Fangxing & Hu, Qinran & Bai, Linquan & Fang, Xin, 2016. "Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants," Applied Energy, Elsevier, vol. 176(C), pages 183-195.
    13. Li, Chengzhou & Wang, Ningling & Wang, Zhuo & Dou, Xiaoxiao & Zhang, Yumeng & Yang, Zhiping & Maréchal, François & Wang, Ligang & Yang, Yongping, 2022. "Energy hub-based optimal planning framework for user-level integrated energy systems: Considering synergistic effects under multiple uncertainties," Applied Energy, Elsevier, vol. 307(C).
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