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Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism

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  • Nie, Qingyun
  • Zhang, Lihui
  • Tong, Zihao
  • Dai, Guyu
  • Chai, Jianxue

Abstract

Coordinating the charging and discharging behaviors of Plug-in electric vehicles (PEVs) to explore the dynamic economic dispatch (DED) of PEVs in power system has received widespread attention, but how to reasonably compensate for the battery degradation cost resulting from battery depletion during discharge is still in the research gap stage. This study reconstructs DED model and establishes a dynamic mechanism for compensating battery degradation cost with carbon trading revenue. Through 3 case simulations, the rationality of the model and solution algorithm in this study is first verified. Then, the negative impacts of battery degradation cost on dispatch are quantitatively verified from aspects of users' enthusiasm, users’ cost and grid load fluctuation. Finally, by setting 6 carbon prices, it is verified that the carbon trading revenue when carbon trading price is $0.07 and $0.08 achieves full compensation for battery degradation cost and eliminates their negative impacts. The further analysis discusses the possibility of this compensation method becoming a new subsidy policy for electric vehicles. The proposed strategy is an effective way to compensate for battery degradation cost and can significantly increase the motivation of vehicle owners to participate in microgrid dispatch, which will maximize the energy-saving and emission-reduction benefits of PEVs.

Suggested Citation

  • Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221019526
    DOI: 10.1016/j.energy.2021.121704
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    Cited by:

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    2. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2022. "Cooperative operation of battery swapping stations and charging stations with electricity and carbon trading," Energy, Elsevier, vol. 254(PA).
    3. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    4. He, Shuaijia & Gao, Hongjun & Tang, Zao & Chen, Zhe & Jin, Xiaolong & Liu, Junyong, 2023. "Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems," Renewable Energy, Elsevier, vol. 203(C), pages 255-266.
    5. Jin, Jingliang & Wen, Qinglan & Cheng, Siqi & Qiu, Yaru & Zhang, Xianyue & Guo, Xiaojun, 2022. "Optimization of carbon emission reduction paths in the low-carbon power dispatching process," Renewable Energy, Elsevier, vol. 188(C), pages 425-436.
    6. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    7. Wang, Yajun & Wang, Jidong & Cao, Man & Kong, Xiangyu & Abderrahim, Bouchedjira & Yuan, Long & Vartosh, Aris, 2023. "Dynamic emission dispatch considering the probabilistic model with multiple smart energy system players based on a developed fuzzy theory-based harmony search algorithm," Energy, Elsevier, vol. 269(C).
    8. Zhang, Wei & Li, Guoxiang & Guo, Fanyong, 2022. "Does carbon emissions trading promote green technology innovation in China?," Applied Energy, Elsevier, vol. 315(C).

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