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Optimal configuration of double carbon energy system considering climate change

Author

Listed:
  • Zhang, Zhonglian
  • Yang, Xiaohui
  • Yang, Li
  • Wang, Zhaojun
  • Huang, Zezhong
  • Wang, Xiaopeng
  • Mei, Linghao

Abstract

How to achieve the “double carbon” goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the “double carbon” goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China’s “double carbon” goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively.

Suggested Citation

  • Zhang, Zhonglian & Yang, Xiaohui & Yang, Li & Wang, Zhaojun & Huang, Zezhong & Wang, Xiaopeng & Mei, Linghao, 2023. "Optimal configuration of double carbon energy system considering climate change," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025823
    DOI: 10.1016/j.energy.2023.129188
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    References listed on IDEAS

    as
    1. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    2. Ma, Weiwu & Fang, Song & Liu, Gang, 2017. "Hybrid optimization method and seasonal operation strategy for distributed energy system integrating CCHP, photovoltaic and ground source heat pump," Energy, Elsevier, vol. 141(C), pages 1439-1455.
    3. Wang, Han & Chaffart, Donovan & Ricardez-Sandoval, Luis A., 2019. "Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural networks," Energy, Elsevier, vol. 188(C).
    4. Finkenrath, Matthias & Faber, Till & Behrens, Fabian & Leiprecht, Stefan, 2022. "Holistic modelling and optimisation of thermal load forecasting, heat generation and plant dispatch for a district heating network," Energy, Elsevier, vol. 250(C).
    5. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    6. Li, Peidu & Gao, Xiaoqing & Li, Zhenchao & Zhou, Xiyin, 2022. "Effect of the temperature difference between land and lake on photovoltaic power generation," Renewable Energy, Elsevier, vol. 185(C), pages 86-95.
    7. Patrón, Gabriel D. & Ricardez-Sandoval, Luis, 2022. "An integrated real-time optimization, control, and estimation scheme for post-combustion CO2 capture," Applied Energy, Elsevier, vol. 308(C).
    8. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
    9. Wang, Rutian & Wen, Xiangyun & Wang, Xiuyun & Fu, Yanbo & Zhang, Yu, 2022. "Low carbon optimal operation of integrated energy system based on carbon capture technology, LCA carbon emissions and ladder-type carbon trading," Applied Energy, Elsevier, vol. 311(C).
    10. Yang, Xiaohui & Liu, Kang & Leng, Zhengyang & Liu, Tao & Zhang, Liufang & Mei, Linghao, 2022. "Multi-dimensions analysis of solar hybrid CCHP systems with redundant design," Energy, Elsevier, vol. 253(C).
    11. Li, Chuang & Li, Guojie & Wang, Keyou & Han, Bei, 2022. "A multi-energy load forecasting method based on parallel architecture CNN-GRU and transfer learning for data deficient integrated energy systems," Energy, Elsevier, vol. 259(C).
    12. Wei, Xintong & Qiu, Rui & Liang, Yongtu & Liao, Qi & Klemeš, Jiří Jaromír & Xue, Jinjun & Zhang, Haoran, 2022. "Roadmap to carbon emissions neutral industrial parks: Energy, economic and environmental analysis," Energy, Elsevier, vol. 238(PA).
    13. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    14. Seferlis, Panos & Varbanov, Petar Sabev & Papadopoulos, Athanasios I. & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2021. "Sustainable design, integration, and operation for energy high-performance process systems," Energy, Elsevier, vol. 224(C).
    15. Laith Abualigah & Raed Abu Zitar & Khaled H. Almotairi & Ahmad MohdAziz Hussein & Mohamed Abd Elaziz & Mohammad Reza Nikoo & Amir H. Gandomi, 2022. "Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques," Energies, MDPI, vol. 15(2), pages 1-26, January.
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