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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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    Cited by:

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    3. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    4. 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.
    5. 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.

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