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

Fadi Kahwash, Basel Barakat, Ahmad Taha, Qammer H. Abbasi and Muhammad Ali Imran
Additional contact information
Fadi Kahwash: School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Basel Barakat: School of Computer Science, University of Sunderland, St Peter Campus, St Peters Way, Sunderland SR6 0DD, UK
Ahmad Taha: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
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

Energies, 2021, vol. 14, issue 21, 1-23

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.

Keywords: grid-connected; hybrid renewable energy systems; multi-objective optimisation; machine learning; forecasting; NHS; CO 2 emissions; net-zero systems; hospital (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/21/7084/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/21/7084/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:21:p:7084-:d:668428

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().