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Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework

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  • Shang, Jingyi
  • Gao, Jinfeng
  • Jiang, Xin
  • Liu, Mingguang
  • Liu, Dunnan

Abstract

This paper develops a two-stage multi-objective bi-level framework to optimize the sizing of a grid-connected electricity-hydrogen system. Firstly, a multi-objective bi-level capacity configuration optimization model considering the different functional orientations of hydrogen energy and electricity-price prediction is established. Then, to solve the above multi-objective bi-level model, a two-stage solution algorithm is proposed. In stage one, the CPLEX solver and non-dominated sorting genetic algorithm II are employed to obtain the solutions of the developed optimization model. In stage two, an entropy method is applied to get the importance of the three objectives of the outer model, whereas a cumulative prospect theory is used to rank the best Pareto solution. Finally, an industrial park in Aksai Kazak Autonomous County is chosen for case study, the results show: (1) the best capacity configuration alternative, which includes 22 wind turbines, 210 photovoltaic panels, 2 gas turbines, 2 fuel cells, 1 electrolyzer, and 3 hydrogen tanks, owns the NPB of 161,503 CNY, the ACE of 93,111 kg, and the LOEC of 603,874 kWh. (2) the ACE with the weight of 0.527 is the most important objective. (3) Sensitivity analysis on electricity price fluctuations of ±5% and ±10% presents that the proposed approach is robust.

Suggested Citation

  • Shang, Jingyi & Gao, Jinfeng & Jiang, Xin & Liu, Mingguang & Liu, Dunnan, 2023. "Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework," Energy, Elsevier, vol. 263(PF).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pf:s0360544222029097
    DOI: 10.1016/j.energy.2022.126023
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    References listed on IDEAS

    as
    1. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    2. Xiang, Yue & Cai, Hanhu & Liu, Junyong & Zhang, Xin, 2021. "Techno-economic design of energy systems for airport electrification: A hydrogen-solar-storage integrated microgrid solution," Applied Energy, Elsevier, vol. 283(C).
    3. Akyuz, E. & Coskun, C. & Oktay, Z. & Dincer, I., 2012. "A novel approach for estimation of photovoltaic exergy efficiency," Energy, Elsevier, vol. 44(1), pages 1059-1066.
    4. Peláez-Peláez, Sofía & Colmenar-Santos, Antonio & Pérez-Molina, Clara & Rosales, Ana-Esther & Rosales-Asensio, Enrique, 2021. "Techno-economic analysis of a heat and power combination system based on hybrid photovoltaic-fuel cell systems using hydrogen as an energy vector," Energy, Elsevier, vol. 224(C).
    5. Wu, Yunna & Ke, Yiming & Xu, Chuanbo & Li, Lingwenying, 2019. "An integrated decision-making model for sustainable photovoltaic module supplier selection based on combined weight and cumulative prospect theory," Energy, Elsevier, vol. 181(C), pages 1235-1251.
    6. Shin, Joohyun & Lee, Jay H. & Realff, Matthew J., 2017. "Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 616-633.
    7. Hou, Hui & Xu, Tao & Wu, Xixiu & Wang, Huan & Tang, Aihong & Chen, Yangyang, 2020. "Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system," Applied Energy, Elsevier, vol. 271(C).
    8. Ramli, Makbul A.M. & Bouchekara, H.R.E.H. & Alghamdi, Abdulsalam S., 2018. "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 400-411.
    9. Li, Li & Hong, Xuefei & Wang, Jun, 2020. "Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach," Energy, Elsevier, vol. 195(C).
    10. Jaliliantabar, Farzad & Ghobadian, Barat & Najafi, Gholamhassan & Mamat, Rizalman & Carlucci, Antonio Paolo, 2019. "Multi-objective NSGA-II optimization of a compression ignition engine parameters using biodiesel fuel and exhaust gas recirculation," Energy, Elsevier, vol. 187(C).
    11. Chen, Peng & Han, Dezhi, 2022. "Effective wind speed estimation study of the wind turbine based on deep learning," Energy, Elsevier, vol. 247(C).
    12. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    13. Ahmed, Fawad & Zhu, Shunmin & Yu, Guoyao & Luo, Ercang, 2022. "A potent numerical model coupled with multi-objective NSGA-II algorithm for the optimal design of Stirling engine," Energy, Elsevier, vol. 247(C).
    14. Wang, Yongli & Li, Ruiwen & Dong, Huanran & Ma, Yuze & Yang, Jiale & Zhang, Fuwei & Zhu, Jinrong & Li, Shuqing, 2019. "Capacity planning and optimization of business park-level integrated energy system based on investment constraints," Energy, Elsevier, vol. 189(C).
    15. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    16. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    17. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).
    18. Yin, Junjie & Liu, Ming & Zhao, Yongliang & Wang, Chaoyang & Yan, Junjie, 2021. "Dynamic performance and control strategy modification for coal-fired power unit under coal quality variation," Energy, Elsevier, vol. 223(C).
    19. Ilbahar, Esra & Kahraman, Cengiz & Cebi, Selcuk, 2022. "Risk assessment of renewable energy investments: A modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP," Energy, Elsevier, vol. 239(PA).
    20. Guo, Zhongjie & Wei, Wei & Chen, Laijun & Zhang, Xiaoping & Mei, Shengwei, 2021. "Equilibrium model of a regional hydrogen market with renewable energy based suppliers and transportation costs," Energy, Elsevier, vol. 220(C).
    21. Zhou, Dengji & Yan, Siyun & Huang, Dawen & Shao, Tiemin & Xiao, Wang & Hao, Jiarui & Wang, Chen & Yu, Tianqi, 2022. "Modeling and simulation of the hydrogen blended gas-electricity integrated energy system and influence analysis of hydrogen blending modes," Energy, Elsevier, vol. 239(PA).
    22. Liu, Zifa & Chen, Yixiao & Zhuo, Ranqun & Jia, Hongjie, 2018. "Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling," Applied Energy, Elsevier, vol. 210(C), pages 1113-1125.
    23. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
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    Cited by:

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    2. Huo, Qunhai & Liu, Qiran & Deng, Huawei & Wang, Wenyong & Shi, Changli & Wei, Tongzhen, 2024. "Research on dual-layer optimization strategy of photovoltaic-storage-hydrogen system in coal chemical industry park," Renewable Energy, Elsevier, vol. 230(C).
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    5. Morteza Nazari-Heris & Atefeh Tamaskani Esfehankalateh & Pouya Ifaei, 2023. "Hybrid Energy Systems for Buildings: A Techno-Economic-Enviro Systematic Review," Energies, MDPI, vol. 16(12), pages 1-15, June.
    6. Zhiming Lu & Youting Li & Guying Zhuo & Chuanbo Xu, 2023. "Configuration Optimization of Hydrogen-Based Multi-Microgrid Systems under Electricity Market Trading and Different Hydrogen Production Strategies," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    7. Wen, Lei & Jiang, Wenkai, 2024. "Bi-level capacity optimization of electricity-hydrogen coupled energy system considering power curtailment constraint and technological advancement," Energy, Elsevier, vol. 307(C).
    8. Xu, Xuesong & Xu, Kai & Zeng, Ziyang & Tang, Jiale & He, Yuanxing & Shi, Guangze & Zhang, Tao, 2024. "Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 375(C).

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