The WinGrid (Wind farm - Grid interactions: exploration and development) consortium aims to train the next generation of researchers on future power system integration issues associated with large-scale deployment of wind generation, focussing on the modelling and control aspects of wind turbine design, and the system stability issues and supervisory structures required for robust implementation. The volume of wind installations is growing rapidly, giving rise to various concerns about future power system stability. More sophisticated modelling capability is required to fully assess the growing complexity as we advance towards a 100% RES resilient power system, while new wind generation technologies are emerging which may radically impact how the future system evolves, against a background of more stringent grid code requirements and emerging system service markets. Highly-skilled researchers, capable of solving such problems, are scarce and in high demand by industry. WinGrid comprises an expert group of 10 academics from 8 beneficiary organisations including 7 leading universities and one large company DNV GL across 6 countries. It also has 8 internationally renowned industrial partners (e.g. ABB) ranging from wind turbine developer, transmission system operator, power system analysts and renewable energy consultants from 6 countries. Combined together we provide wide-ranging expertise in power electronics converters, control theory, system stability analysis, power system operation and electricity markets. The ESRs will enjoy a highly integrated, multi-disciplinary training environment, including access to specialist software and hardware-in-the-loop test environments, enriched through secondments with the network of industrial partners. WinGrid will enable critical learning across all training aspects, in order to ensure that comprehensive, robust and implementable solutions are obtained and validated to face the grid integration challenges of the future.

The Smart4RES project aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.