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.
