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Peak shaving strategy for renewable hybrid system driven by solar and radiative cooling integrating carbon capture and sewage treatment

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  • Su, Zixiang
  • Yang, Liu

Abstract

Although considered to be one of the most valuable alternative energy sources, the utilization of renewable energy still confronts challenges such as low conversion efficiency and large transient fluctuations. To address this issue, we propose a renewable hybrid system driven by solar and radiative cooling, develop advanced energy management and peak shaving strategies. After establishing and validating the mathematical model, the renewable energy recovery, subsystem variation law analysis, and hybrid system performance calculation are computed. Three-objective optimization under four cases is implemented after understanding the change rules. Subsequently, the energy management and peak shaving are performed, as well as the positive contribution of the storage system to the renewable hybrid system is discussed. Based on the equivalent coefficient, the renewable hybrid system benefits are finally assessed. The numerical results conclude that the renewable hybrid system has excellent peak shaving characteristics, can fulfill dynamically changing energy demands. Its equivalent work, energy efficiency, and exergy efficiency reach 155.11 kW, 23.17%, and 70.61%, respectively, with additional emission reduction, carbon capture, and sewage treatment capacities of 131.69 kg/h, 131.82 kg/h, and 1.509 kg/s, can recover the cost within 4.29 years. It complies with sustainable development models, conforms to carbon neutrality requirements, as well as provides meaningful guidelines for renewable energy utilization.

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  • Su, Zixiang & Yang, Liu, 2022. "Peak shaving strategy for renewable hybrid system driven by solar and radiative cooling integrating carbon capture and sewage treatment," Renewable Energy, Elsevier, vol. 197(C), pages 1115-1132.
    • Handle: RePEc:eee:renene:v:197:y:2022:i:c:p:1115-1132
    DOI: 10.1016/j.renene.2022.08.011
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    2. Xie, Xing & Xu, Bin & Fei, Yue & Chen, Xing-ni & Pei, Gang & Ji, Jie, 2024. "Passive energy-saving design strategy and realization on high window-wall ratio buildings in subtropical regions," Renewable Energy, Elsevier, vol. 229(C).
    3. Ouyang, Tiancheng & Pan, Mingming & Huang, Youbin & Tan, Xianlin & Qin, Peijia, 2023. "Thermodynamic design and power prediction of a solar power tower integrated system using neural networks," Energy, Elsevier, vol. 278(PA).
    4. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.

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