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Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study

Author

Listed:
  • Fadi Kahwash

    (School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
    These authors contributed equally to this work.)

  • Basel Barakat

    (School of Computer Science, University of Sunderland, St Peter Campus, St Peters Way, Sunderland SR6 0DD, UK
    These authors contributed equally to this work.)

  • Ahmad Taha

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
    These authors contributed equally to this work.)

  • Qammer H. Abbasi

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Muhammad Ali Imran

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

Abstract

This study focuses on improving the sustainability of electrical supply in the healthcare system in the UK, to contribute to current efforts made towards the 2050 net-zero carbon target. As a case study, we propose a grid-connected hybrid renewable energy system (HRES) for a hospital in the south-east of England. Electrical consumption data were gathered from five wards in the hospital for a period of one year. PV-battery-grid system architecture was selected to ensure practical execution through the installation of PV arrays on the roof of the facility. Selection of the optimal system was conducted through a novel methodology combining multi-objective optimisation and data forecasting. The optimisation was conducted using a genetic algorithm with two objectives (1) minimisation of the levelised cost of energy and (2) CO 2 emissions. Advanced data forecasting was used to forecast grid emissions and other cost parameters at two year intervals (2023 and 2025). Several optimisation simulations were carried out using the actual and forecasted parameters to improve decision making. The results show that incorporating forecasted parameters into the optimisation allows to identify the subset of optimal solutions that will become sub-optimal in the future and, therefore, should be avoided. Finally, a framework for choosing the most suitable subset of optimal solutions was presented.

Suggested Citation

  • Fadi Kahwash & Basel Barakat & Ahmad Taha & Qammer H. Abbasi & Muhammad Ali Imran, 2021. "Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study," Energies, MDPI, vol. 14(21), pages 1-23, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7084-:d:668428
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    References listed on IDEAS

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    Cited by:

    1. Maria Psillaki & Nikolaos Apostolopoulos & Ilias Makris & Panagiotis Liargovas & Sotiris Apostolopoulos & Panos Dimitrakopoulos & George Sklias, 2023. "Hospitals’ Energy Efficiency in the Perspective of Saving Resources and Providing Quality Services through Technological Options: A Systematic Literature Review," Energies, MDPI, vol. 16(2), pages 1-21, January.
    2. Chro Hama Radha, 2023. "Retrofitting for Improving Indoor Air Quality and Energy Efficiency in the Hospital Building," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    3. Alfonso Angel Medina-Santana & Leopoldo Eduardo Cárdenas-Barrón, 2022. "Optimal Design of Hybrid Renewable Energy Systems Considering Weather Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 15(23), pages 1-28, November.

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