The transition to the smart grid era is associated with the creation of a meshed network of data contributors that necessitates for the transformation of the traditional top-down business model, where power system optimization relied on centralized decisions based on data silos preserved by stakeholders, to a more horizontal one in which optimization decisions are based on interconnected data assets and collective intelligence. Consequently, the need for “end-to-end” coordination between the electricity stakeholders, not only in business terms but also in exchanging information is becoming a necessity to enable the enhancement of electricity networks’ stability and resilience, while satisfying individual business process optimization targets of all stakeholders involved in the value chain. SYNERGY introduces a novel reference big data architecture and platform that leverages data, primary or secondarily related to the electricity domain, coming from diverse sources (APIs, historical data, statistics, sensors/ IoT, weather, energy markets and various other open data sources) to help electricity stakeholders to simultaneously enhance their data reach, improve their internal intelligence on electricity-related optimization functions, while getting involved in novel data (intelligence) sharing/trading models, in order to shift individual decision-making at a collective intelligence level.

To this end SYNERGY will develop a highly effective a Big Energy Data Platform and AI Analytics Marketplace, accompanied by big data-enabled applications for the totality of electricity value chain stakeholders (altogether integrated in the SYNERGY Big Data-driven EaaS Framework). SYNERGY will be validated in 5 large-scale demonstrators, in Greece, Spain, Austria, Finland and Croatia involving diverse actors and data sources, heterogeneous energy assets, varied voltage levels and network conditions and spanning different climatic, demographic and cultural characteristics.

For more information, visit the website of the project.

"This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No 872734"