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Details of Grant 

EPSRC Reference: EP/T021985/1
Title: Sustainable urban power supply through intelligent control and enhanced restoration of AC/DC networks
Principal Investigator: Liang, Professor J
Other Investigators:
Cipcigan, Professor LM Ming, Dr W Rana, Professor O
Ranjan, Professor R
Researcher Co-Investigators:
Project Partners:
Exergy LTD Newcastle City Council NR Electric UK Limited
Plexus Innovation Ltd Scottish Power Smarter Grid Solutions
Turbopowersystems Zero Carbon Futures
Department: Sch of Engineering
Organisation: Cardiff University
Scheme: Standard Research
Starts: 01 January 2020 Ends: 31 December 2022 Value (£): 604,562
EPSRC Research Topic Classifications:
Energy Storage Sustainable Energy Networks
EPSRC Industrial Sector Classifications:
Energy Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
26 Nov 2019 EPSRC-NSFC Call in Sustainable Power Supply Announced
Summary on Grant Application Form
China remains the world's largest electric car market, and the UK is leading deployment of electric vehicles (EVs) to meet the new net-zero 2050 emission target. Both nations will face challenges in connecting EVs in urban areas due to limited land space, constraints on carrying additional power over traditional transmission lines and challenges in providing reliable power to critical load centres. This proposal identifies areas of common technical challenges and lays out a joint programme to analyse the issues and assess possible solutions.

Urban areas are the significant location of critical loads such as hospitals, airports, public transport network and data centres. Fully exploiting the potential transfer capacity and resilience of the urban electricity network with a minimum capital investment is important to citizens and governments as 60% of Chinese population and 83% of UK population live in urban areas. To release the capacity of existing AC lines and to increase the reliability, a combined AC/DC configuration is proposed, and contribution of power electronic materials and converters are considered. Coordinated control of EVs, hybrid AC/DC networks and dispersed generation are investigated to optimise transfer capacity and enhance fault-tolerant operation with the support of Internet-of-Things (IoT) tools to enable an efficient decision-making.

Two specific aspects will be investigated: the ability of IoT based data-driven modelling method to enable response services by coordinating dispersed resources in an urban power network and the headroom provided by power converters to accommodate this service. The contribution of IoT in providing useful data that enable the efficient management of urban power network is an emerging paradigm for the realisation of smart cities. As an essential part of daily life, optimal utilisation and reliability of electric energy becomes paramount. However, blackouts affecting the security and stability of the power system is an important issue. EVs storage capacity and optimal scheduling through power converters will be explored and quantified to provide grid support services in the event of an emergency situation. Protection schemes that can achieve fast and reliable identification and isolation with the aids of IoT, EVs and power converters are analysed in detail and re-engineering solutions proposed.

Technical challenges from widespread use of dispersed resources connected to urban energy networks will be studied. Data-driven modelling will be applied to urban power systems to characterise the capacity of EVs and distributed generations that will allow two-way communication that transforms conventional networks into more secure networks. Traditional network topologies with the inclusion of power converters will be reassessed to eliminate potentially wasteful energy conversion stages and support flexibility services. Coordinated control of converters and distributed resources with spatial-temporary coupling and edge-cloud collaboration will be developed to make cost-effective, sustainable, resilient and fault-tolerant urban power system operation.

The key outputs will be the data-driven modelling and analysis methods that can assess the spatial-temporary relation between distributed resources and urban electricity network (useful to system operators and equipment vendors); engineering solutions to map the capability of vehicle to grid services; optimal scheduling using power converters in the event of an emergency situation (useful to system operators, equipment vendors and EV owners); and verification through real-time simulation and scaled laboratory test systems.

The main work programme will be conducted through international partnership with 1 China research institute, 2 China universities and 2 UK universities. Researchers involved in the project will benefit from the unique international collaboration and training.

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