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

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-ea...

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Autores principales:	Fadi Kahwash, Basel Barakat, Ahmad Taha, Qammer H. Abbasi, Muhammad Ali Imran
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	grid-connected hybrid renewable energy systems multi-objective optimisation machine learning forecasting NHS Technology T
Acceso en línea:	https://doaj.org/article/76bad0db26b34747a1cec50434d90cf0
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spelling	oai:doaj.org-article:76bad0db26b34747a1cec50434d90cf02021-11-11T15:53:18ZOptimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study10.3390/en142170841996-1073https://doaj.org/article/76bad0db26b34747a1cec50434d90cf02021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7084https://doaj.org/toc/1996-1073This 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) <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>CO</mi><mn>2</mn></msub></semantics></math></inline-formula> 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.Fadi KahwashBasel BarakatAhmad TahaQammer H. AbbasiMuhammad Ali ImranMDPI AGarticlegrid-connectedhybrid renewable energy systemsmulti-objective optimisationmachine learningforecastingNHSTechnologyTENEnergies, Vol 14, Iss 7084, p 7084 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	grid-connected hybrid renewable energy systems multi-objective optimisation machine learning forecasting NHS Technology T
spellingShingle	grid-connected hybrid renewable energy systems multi-objective optimisation machine learning forecasting NHS Technology T Fadi Kahwash Basel Barakat Ahmad Taha Qammer H. Abbasi Muhammad Ali Imran Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study
description	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) <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>CO</mi><mn>2</mn></msub></semantics></math></inline-formula> 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.
format	article
author	Fadi Kahwash Basel Barakat Ahmad Taha Qammer H. Abbasi Muhammad Ali Imran
author_facet	Fadi Kahwash Basel Barakat Ahmad Taha Qammer H. Abbasi Muhammad Ali Imran
author_sort	Fadi Kahwash
title	Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study
title_short	Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study
title_full	Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study
title_fullStr	Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study
title_full_unstemmed	Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study
title_sort	optimising electrical power supply sustainability using a grid-connected hybrid renewable energy system—an nhs hospital case study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/76bad0db26b34747a1cec50434d90cf0
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Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study

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