Periodic Reporting for period 1 - TESTBED2 (Testing and Evaluating Sophisticated information and communication Technologies for enaBling scalablE smart griD Deployment)
Berichtszeitraum: 2020-02-01 bis 2023-07-31
The European Commission (EC) has set a roadmap for reducing greenhouse gas emissions by at least 80% by 2050 compared with 1990 levels. This ambitious goal cannot be achieved without the deployment of smart grids, representing an electricity network that can intelligently integrate generators, consumers and energy storage in order to efficiently deliver electricity. There is a clear consensus that smart grids can provide many innovative services – to date the EC has devoted €360,413 million to support 527 projects on developing smart grid services, including demand response, virtual power plant etc. Decision- making plays a vital role in these services. But the computational complexity of decision-makings could grow explosively with the size of smart grid infrastructure, the number of devices/users, or the amount of data; If this scalability issue was underestimated, smart grid services can end up with poor performance or limited function, making these services impractical to meet the needs of real-life or industrial-scale deployment. European Union, along with US and China, have insisted on the urgent need for scalable smart grid solutions. Hence, there is an urgent need to solve the research problem: to what extent the performance and function of smart grids can be maintained without having significant increase of the computational complexity when its scale is changed in terms of smart grid infrastructure size or the number of devices/users?
The objectives of TESTBED2 can be split into network- and research-objectives.
The network ones include:
1) To improve the expertise of seconded researchers by attaining training and staff exchange activities. The purpose is to train 30 Early Stage Researchers (ESRs), whilst supporting the career development of 31 Experienced Researchers (ERs) by taking advantage of the integrated training and staff exchange scheme offered by the project network.
2) To encourage the knowledge exchange of best practices in the design and implementation of smart grid and artificial intelligence (AI) technologies between academic and industrial sectors. The innovation capability of enterprises in Europe will be promoted through understanding the complete cycle from initial creative ideas to the final products or services and comparing European approaches to those of the US and China.
3) To strengthen Europe-US-China research partnerships through the mobility of ESRs and ERs. Both US and China have been the global leading players in the industries of energy and Information and Communication Technologies (ICT), and have the world's largest energy consumers. This project will therefore act as a timely Science-Bridge promoting systematic, long-term, and sustainable
collaborations between EU and US/China.
The research objectives include:
4) To develop and test novel tools of decentralised optimisation and modular designs for enabling scalable smart grid services. New insights will be gained and new methods of improving scalability of smart grid services will be established. These include the development of new decentralised optimisation algorithms and modular design techniques.
5) To develop novel (AI-centred) algorithms and numerical tools to explore smart grid related data for improving macroeconomic models in order to ensure long-term scalability of smart grid services. Universal information models will be proposed for organising large volume of data from various sectors (including power, data, transport and heating) and energy vectors. Laboratory tests will be performed to evaluate the proposed information model and AI-centred data analytics tools. New traffic models will then be developed for modelling typical smart grid applications and infrastructures.
6) To build joint experimental testbeds using our laboratories for bridging the gap between theoretical and practical developments. Aforesaid tools, algorithms, and models will be tested and validated with all practical factors considered.
The objectives of TESTBED2 can be split into network- and research-objectives.
The network ones include:
1) To improve the expertise of seconded researchers by attaining training and staff exchange activities. The purpose is to train 30 Early Stage Researchers (ESRs), whilst supporting the career development of 31 Experienced Researchers (ERs) by taking advantage of the integrated training and staff exchange scheme offered by the project network.
2) To encourage the knowledge exchange of best practices in the design and implementation of smart grid and artificial intelligence (AI) technologies between academic and industrial sectors. The innovation capability of enterprises in Europe will be promoted through understanding the complete cycle from initial creative ideas to the final products or services and comparing European approaches to those of the US and China.
3) To strengthen Europe-US-China research partnerships through the mobility of ESRs and ERs. Both US and China have been the global leading players in the industries of energy and Information and Communication Technologies (ICT), and have the world's largest energy consumers. This project will therefore act as a timely Science-Bridge promoting systematic, long-term, and sustainable
collaborations between EU and US/China.
The research objectives include:
4) To develop and test novel tools of decentralised optimisation and modular designs for enabling scalable smart grid services. New insights will be gained and new methods of improving scalability of smart grid services will be established. These include the development of new decentralised optimisation algorithms and modular design techniques.
5) To develop novel (AI-centred) algorithms and numerical tools to explore smart grid related data for improving macroeconomic models in order to ensure long-term scalability of smart grid services. Universal information models will be proposed for organising large volume of data from various sectors (including power, data, transport and heating) and energy vectors. Laboratory tests will be performed to evaluate the proposed information model and AI-centred data analytics tools. New traffic models will then be developed for modelling typical smart grid applications and infrastructures.
6) To build joint experimental testbeds using our laboratories for bridging the gap between theoretical and practical developments. Aforesaid tools, algorithms, and models will be tested and validated with all practical factors considered.
• The project has included tens of researchers at different career stages, including PhD students, postdoc researchers, assistant professors, associate professors and professors.
• We successfully organised 1 summer school at Durham in June 2022 with over 30 early-stage researchers (ESRs) joining this event. Attendees include academic staff, PhD students and industrial experts from both TESTBED2 consortium (e.g. UDUR, Heriot-Watt University (HWU), University of Northumbria at Newcastle (UNN), CWI and BEIA) and other research-intensive Universities, e.g. Universities of Manchester, Warwick, Cardiff, Nottingham, and Tempere (Finland). For more details, please see D1.1.
• We successfully held one workshop in September 2022, which attracted many attendees from UK, US and China. For more details, see D6.2.
• We implemented over 20 months cross-sectoral secondment in the project, which helped both academic and non-academic sectors to understand both research and development of services and products.
• We implemented nearly 40 months Europe-US and Europe-China secondment implemented by the project. These have helped promote collaborations as evidenced by joint research publications.
• We developed Blockchain tools for decentralising smart grid services; Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources.
• We designed effective approaches to test and defense the cyber security of modular design of smart grid services.
• We performed comprehensive literature review in the area of AI for smart grid.
• We developed several AI/Machine learning methods and used them to solve practical problems in the areas of energy system optimisation and energy data communications.
• We evaluated the performance of various algorithms using laboratory facilities.
• We successfully organised 1 summer school at Durham in June 2022 with over 30 early-stage researchers (ESRs) joining this event. Attendees include academic staff, PhD students and industrial experts from both TESTBED2 consortium (e.g. UDUR, Heriot-Watt University (HWU), University of Northumbria at Newcastle (UNN), CWI and BEIA) and other research-intensive Universities, e.g. Universities of Manchester, Warwick, Cardiff, Nottingham, and Tempere (Finland). For more details, please see D1.1.
• We successfully held one workshop in September 2022, which attracted many attendees from UK, US and China. For more details, see D6.2.
• We implemented over 20 months cross-sectoral secondment in the project, which helped both academic and non-academic sectors to understand both research and development of services and products.
• We implemented nearly 40 months Europe-US and Europe-China secondment implemented by the project. These have helped promote collaborations as evidenced by joint research publications.
• We developed Blockchain tools for decentralising smart grid services; Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources.
• We designed effective approaches to test and defense the cyber security of modular design of smart grid services.
• We performed comprehensive literature review in the area of AI for smart grid.
• We developed several AI/Machine learning methods and used them to solve practical problems in the areas of energy system optimisation and energy data communications.
• We evaluated the performance of various algorithms using laboratory facilities.
• We trained tens of researchers at different career stages, including PhD students, postdoc researchers, assistant professors, associate professors and professors.
• We helped SME - BEIA to increase turnover from 1.8m to 3.9m.
• We organised summer school and workshop that attracted over 100 ECRs.
• We shared news and updated publications in our website that attracted over 10k clicks and follow-ups.
• We published many scientific papers as reported in the publications tab that attracted thousands of citations so far.
• We helped SME - BEIA to increase turnover from 1.8m to 3.9m.
• We organised summer school and workshop that attracted over 100 ECRs.
• We shared news and updated publications in our website that attracted over 10k clicks and follow-ups.
• We published many scientific papers as reported in the publications tab that attracted thousands of citations so far.