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Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization

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  • Zhang, Xiaoshun
  • Guo, Zhengxun
  • Pan, Feng
  • Yang, Yuyao
  • Li, Chuansheng

Abstract

To reduce the peak-valley difference of power consumption, the distribution system operator (DSO) usually guides the electricity consumers to change their load profiles based on the time of use electricity prices. However, electricity consumers will also pay attention to their carbon emissions except the electricity cost in a carbon emission trading market. It easily causes an adverse influence on the peak-valley difference of power consumption. Therefore, this work constructs a new interactive control between DSO and electricity consumers based on the dynamic carbon emission factor (CEF). The interactive control is a hierarchical optimization, including an upper-layer optimization and a lower-layer optimization. The upper-layer optimization is served for DSO, which aims to minimize the peak-valley difference of power consumption. The low-layer optimization is served for an electric vehicle (EV) aggregator with multiple EV groups, which attempts to minimize the electricity and carbon emission costs. To avoid the frequent and time-consuming interactions between DSO and EV aggregator, a generalized regression neural network is used to generate an accurate surrogate model for the upper-layer optimization. Finally, the proposed technique is verified on an extended IEEE 33-bus system and an extended IEEE 69-bus system with multiple distributed generators and EV groups.

Suggested Citation

  • Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025264
    DOI: 10.1016/j.energy.2023.129132
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    References listed on IDEAS

    as
    1. Zhang, Xiaoshun & Bao, Tao & Yu, Tao & Yang, Bo & Han, Chuanjia, 2017. "Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid," Energy, Elsevier, vol. 133(C), pages 348-365.
    2. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators’ perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    3. Cai, Liya & Luo, Ji & Wang, Minghui & Guo, Jianfeng & Duan, Jinglin & Li, Jingtao & Li, Shuo & Liu, Liting & Ren, Dangpei, 2023. "Pathways for municipalities to achieve carbon emission peak and carbon neutrality: A study based on the LEAP model," Energy, Elsevier, vol. 262(PB).
    4. Lyu, Xiangmei & Liu, Tianqi & Liu, Xuan & He, Chuan & Nan, Lu & Zeng, Hong, 2023. "Low-carbon robust economic dispatch of park-level integrated energy system considering price-based demand response and vehicle-to-grid," Energy, Elsevier, vol. 263(PB).
    5. Chen, Yuzhu & Hu, Xiaojian & Xu, Wentao & Xu, Qiliang & Wang, Jun & Lund, Peter D., 2022. "Multi-objective optimization of a solar-driven trigeneration system considering power-to-heat storage and carbon tax," Energy, Elsevier, vol. 250(C).
    6. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, December.
    7. Jiang, Kai & Yan, Xiaohe & Liu, Nian & Wang, Peng, 2022. "Energy trade-offs in coupled ICM and electricity market under dynamic carbon emission intensity," Energy, Elsevier, vol. 260(C).
    8. Leng, Ya-Jun & Peng, Dan & Zhang, Huan, 2023. "Integrated energy system evaluation method based on dimensionality reduction and indexes updating with incomplete information," Energy, Elsevier, vol. 277(C).
    9. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).
    10. Bendu, Harisankar & Deepak, B.B.V.L. & Murugan, S., 2017. "Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO," Applied Energy, Elsevier, vol. 187(C), pages 601-611.
    11. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    12. Baumgärtner, Nils & Delorme, Roman & Hennen, Maike & Bardow, André, 2019. "Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management," Applied Energy, Elsevier, vol. 247(C), pages 755-765.
    13. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan, 2022. "Robust energy systems scheduling considering uncertainties and demand side emission impacts," Energy, Elsevier, vol. 239(PD).
    14. Nafisi, Amin & Arababadi, Reza & Moazami, Amin & Mahapatra, Krushna, 2022. "Economic and emission analysis of running emergency generators in the presence of demand response programs," Energy, Elsevier, vol. 255(C).
    15. Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).
    16. Yong, Jin Yi & Tan, Wen Shan & Khorasany, Mohsen & Razzaghi, Reza, 2023. "Electric vehicles destination charging: An overview of charging tariffs, business models and coordination strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
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    7. Zhang, Junda & Chen, Yinuo & Liu, Xiaojing & Chai, Xiang & He, Hui & Xiong, Jinbiao & Zhang, Tengfei, 2025. "Optimizing thermal stress distribution in heat-pipe-cooled microreactors using multi-physics data-driven methods," Energy, Elsevier, vol. 323(C).

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