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Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties

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  • Wang, Yuwei
  • Song, Minghao
  • Jia, Mengyao
  • Li, Bingkang
  • Fei, Haoran
  • Zhang, Yiyue
  • Wang, Xuejie

Abstract

Hydrogen is one type of clean renewable energy. By introducing hydrogen production, storage and conversion devices into the combined cooling, heating and power (CCHP) system to replace its fossil fuel components, the hydrogen-involved total renewable energy CCHP (H-RE-CCHP) system is formed, thus bringing an effective way to promote energy transition towards low carbon. However, the conflict between economy and environment, and interference of source-load uncertainties hinder the low-cost construction as well as economic, stable, and low-carbon operation of H-RE-CCHP. In view of this, this paper proposes a multi-objective distributionally robust optimization (DRO) model for H-RE-CCHP planning. Firstly, a moment-based ambiguity set is constructed to define the possible range for the joint distribution of solar irradiation and multiple loads. Secondly, considering the “worst-case” distribution within the ambiguity set, the economic and environmental objective functions are presented for minimizing system construction, maintenance and operation costs while reducing carbon emission. Furthermore, the sizing- and operation-related constraints are formulated according to such principles as maintaining multi-energy balance when the forecasting errors of uncertainties occur. Finally, by applying strong duality and augmented ε-constraint for model solving, Pareto optimal solution set is obtained, and from which final planning and operation scheme is selected according to decision preference and interactions between objectives. Simulations mainly verify: 1) due to considering multiple objectives, H-RE-CCHP planning and operation reach the reasonable trade-off between economy and environment, that is, carbon emission/economic cost can be reduced by 99.81 %/66.70 % comparing with single economic/environmental objective; 2) with applying DRO in resisting uncertainties, 100 % renewable energy accommodation is achieved while meeting stable H-RE-CCHP operation; etc. Accordingly, this paper provides an effective approach for renewable energy system planning, thus contributing to the low carbon development of economic society.

Suggested Citation

  • Wang, Yuwei & Song, Minghao & Jia, Mengyao & Li, Bingkang & Fei, Haoran & Zhang, Yiyue & Wang, Xuejie, 2023. "Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties," Applied Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923005767
    DOI: 10.1016/j.apenergy.2023.121212
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    2. Huang, Z.F. & Chen, W.D. & Wan, Y.D. & Shao, Y.L. & Islam, M.R. & Chua, K.J., 2024. "Techno-economic comparison of different energy storage configurations for renewable energy combined cooling heating and power system," Applied Energy, Elsevier, vol. 356(C).
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    4. Jimiao Zhang & Jie Li, 2024. "Revolution in Renewables: Integration of Green Hydrogen for a Sustainable Future," Energies, MDPI, vol. 17(16), pages 1-26, August.
    5. Zhu, Mengshu & Ai, Xiaomeng & Fang, Jiakun & Cui, Shichang & Wu, Kejing & Zheng, Lufan & Wen, Jinyu, 2024. "Optimal scheduling of hydrogen energy hub for stable demand with uncertain photovoltaic and biomass," Applied Energy, Elsevier, vol. 360(C).
    6. Yuyang Zhao & Yifan Wei & Shuaiqi Zhang & Yingjun Guo & Hexu Sun, 2024. "Multi-Objective Robust Optimization of Integrated Energy System with Hydrogen Energy Storage," Energies, MDPI, vol. 17(5), pages 1-20, February.
    7. Fan, Guozhu & Peng, Chunhua & Wang, Xuekui & Wu, Peng & Yang, Yifan & Sun, Huijuan, 2024. "Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC," Renewable Energy, Elsevier, vol. 226(C).
    8. Jia, Dongqing & Li, Xingmei & Gong, Xu & Lv, Xiaoyan & Shen, Zhong, 2024. "Bi-level strategic bidding model of novel virtual power plant aggregating waste gasification in integrated electricity and hydrogen markets," Applied Energy, Elsevier, vol. 357(C).
    9. Zhao, Xiangming & Guo, Jianxiang & He, Maogang, 2023. "Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification," Renewable Energy, Elsevier, vol. 217(C).

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