Objectives
01.
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.
02.
To encourage the knowledge exchange of best practices in the design and implementation of smart grid 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.
03.
To strengthen 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.
04.
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.
05.
To develop novel 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 tools. New traffic models will then be developed for modelling typical smart grid applications and infrastructures.
06.
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.