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Low-carbon robust economic dispatch of park-level integrated energy system considering price-based demand response and vehicle-to-grid

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  • Lyu, Xiangmei
  • Liu, Tianqi
  • Liu, Xuan
  • He, Chuan
  • Nan, Lu
  • Zeng, Hong

Abstract

With the aggravation of energy crisis and greenhouse effect, energy transformation is imperative. The problems of renewable energy uncertainties and carbon emission need to be solved urgently. Therefore, to deal with uncertainties and cut down carbon emission in the park-level integrated energy system (IES), this paper proposes a low-carbon robust economic dispatch model considering price-based integrated thermo-electric demand response (DR) and vehicle-to-grid (V2G). Firstly, The V2G model, price-based integrated thermo-electric DR model and carbon trading model are established. Then, considering energy balance constraints of power/natural gas/heat, operation constraints and energy exchange constraints, a deterministic day-ahead scheduling model for low-carbon operation is presented with the goal of maximizing system social welfare. Finally, considering the uncertainties of wind/solar generation and electricity/heat load, a two-stage robust economic dispatch model is proposed. Case studies verify that the deterministic scheduling model could effectively increase system flexibility, while reducing the total production cost by 18.58% and carbon emission by 16.77%. Furthermore, the proposed robust scheduling model sacrifices 12.65% economy to maintain system security against various uncertainties and maintain carbon emission in the worst-case scenario with an acceptable range.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222026251
    DOI: 10.1016/j.energy.2022.125739
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    Cited by:

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    3. 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).
    4. Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
    5. Huang, Shangjiu & Lu, Hao & Chen, Maozhi & Zhao, Wenjun, 2023. "Integrated energy system scheduling considering the correlation of uncertainties," Energy, Elsevier, vol. 283(C).
    6. Wu, Qiong & Chen, Min & Ren, Hongbo & Li, Qifen & Gao, Weijun, 2024. "Collaborative modeling and optimization of energy hubs and multi-energy network considering hydrogen energy," Renewable Energy, Elsevier, vol. 227(C).
    7. Zhang, Jinliang & Liu, Ziyi, 2024. "Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen," Energy, Elsevier, vol. 290(C).
    8. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky, 2024. "Towards energy transition: A novel day-ahead operation scheduling strategy for demand response and hybrid energy storage systems in smart grid," Energy, Elsevier, vol. 293(C).
    9. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).

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