Seven new feasibility studies will explore how digital technologies, business model innovation and analysis can reduce energy demand and carbon production.
The studies, supported with a £340,000 investment from the Engineering and Physical Sciences Research Council (EPSRC) and Siemens, include using digital technologies to monitor and ultimately reduce emissions at ports and supermarkets.
Other projects intend to improve the forecasting of solar energy production and explore how digital twins can help to decarbonise the industrial and electricity sectors.
Industry collaboration
Siemens is one of EPSRC’s strategic business partners. Both organisations are committed to sharing strategic information and working on areas of shared interest.
EPSRC Executive Chair, Professor Dame Lynn Gladden, said:
Digital technologies will play an important role in making our use of energy more efficient, reducing the impact on the environment.
The feasibility studies announced today demonstrate EPSRC’s ability to build exciting partnerships between industry and academia.
The studies will explore innovative new approaches to ensure we have the digital infrastructure we need to deliver on the UK’s net zero ambitions.
UK CEO, Siemens plc, Carl Ennis, said:
Data driven innovation is vital to our ability to find technology solutions that can help the country decarbonise and save energy.
It is through the close collaboration between business and academia that we can prioritise deploying solutions at scale that are right here, right now.
The new feasibility studies
Digitalisation for operational efficiency and GHG emission reduction at container ports
Led by: Professor Dongping Song, University of Liverpool
Ports are concentrated areas producing air pollutants and CO2 emissions. This project aims to apply digital technologies to:
- predict import containers’ out-terminals
- optimise yard operations
- improve efficiency and reduce emissions.
DEMSIS: Digital energy management services in supermarket buildings via cloud-based solutions
Led by: Professor Nilay Shah, Imperial College London
A cloud-based energy management framework that uses real-time model predictive control to optimise the combined economic and environmental performance of:
- heating, ventilation and air conditioning (HVAC)
- refrigeration systems in commercial buildings.
Cloud-based solar forecasting for improved grid management
Led by: Professor Yupeng Wu, University of Nottingham
This project will develop a comprehensive digital platform for forecasting meteorological parameters. This will improve the prediction of solar power generation, significantly reducing power mismatch caused by forecast errors.
Data for digital decarbonisation (3D): a FAIR approach to energy demand data in buildings
Led by: Dr Steven Firth, Loughborough University
This project aims to unlock the vast potential of building energy datasets using:
- open research techniques
- findable, accessible, interoperable and reusable (FAIR) data guidelines to support novel solutions for a net zero economy.
Digital twin with data-driven predictive control: unlocking flexibility of industrial plants for supporting a net zero electricity system
Led by: Dr Yue Zhou, Cardiff University
This project is to develop digital twins with data-driven predictive control functionality to tap the flexibility potential of industrial plants for supporting the decarbonisation of both industrial and electricity sectors.
Blockchain-enabled cloud-edge coordination for demand side management
Led by: Dr Fei Teng, Imperial College London
This project will investigate the application of blockchain and cloud-edge computing technologies in demand response management to offload the computation and communication burden while providing a trustworthy and privacy-preserving environment.
Electric fleets with on-site renewable energy sources (EFORES): data-driven dynamic dispatching and charging under uncertainties
Led by: Dr Xuewu Dai, Northumbria University
This project investigates optimal dispatching and charging management of electric fleets to improve the efficient use and self-consumption of highly variable onsite renewable energy sources.
Top image: Credit: UKRI, Brad Wakefield