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Design, and optimization of COVID-19 hospital wards to produce Oxygen and electricity through solar PV panels with hydrogen storage systems by neural network-genetic algorithm

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  • Izadi, Ali
  • Shahafve, Masoomeh
  • Ahmadi, Pouria
  • Hanafizadeh, Pedram

Abstract

COVID-19 has affected energy consumption and production pattern in various sectors in both rural and urban areas. Consequently, energy demand has increased. Therefore, most health care centers report a shortage of energy, particularly during the summer seasons. Therefore, integrating renewable energies into hospitals is a promising method that can generate electricity demand reliably and emits less CO2. In this research paper, a hybrid renewable energy system (HRES) with hydrogen energy storage is simulated to cover the energy demand of sections and wards of a hospital that dealt with COVID-19 patients. Produced Oxygen from the hydrogen storage system is captured and stored in medical capsules to generate the oxygen demand for the patients. Results indicate that 29.64% of the annual consumed energy is utilized in COVID-19 sections. Afterward, modeled system has been optimized with a neural network-genetic algorithm to compute the optimum amount of the demand power from the grid, CO2 emission, oxygen capsules, and cost rate. Results determine that by having 976 PV panels, 179 kW fuel cell, and 171.2 kW electrolyzer, annual CO2 emission is 315.8 tons and 67,833 filled medical oxygen capsules can be achieved. The cost rate and demand electricity from the grid for the described system configuration are 469.07 MWh/year and 18.930 EUR/hr, respectively.

Suggested Citation

  • Izadi, Ali & Shahafve, Masoomeh & Ahmadi, Pouria & Hanafizadeh, Pedram, 2023. "Design, and optimization of COVID-19 hospital wards to produce Oxygen and electricity through solar PV panels with hydrogen storage systems by neural network-genetic algorithm," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222024641
    DOI: 10.1016/j.energy.2022.125578
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    References listed on IDEAS

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    3. Han, Yixiao & Liao, Yanfen & Ma, Xiaoqian & Guo, Xing & Li, Changxin & Liu, Xinyu, 2023. "Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network," Renewable Energy, Elsevier, vol. 215(C).

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