Scientific Methodology

TILOS project, aiming to achieve market uptake of local scale innovative energy storage configurations through the development of optimum integration directions, makes use of a holistic approach that addresses all aspects of the subject investigated.

To this end, TILOS is not limited to the demonstration and operation of the proposed hybrid power station, but extends to also take into account of additional attributes capturing business and social science disciplines. By seeking a deeper understanding of the public perception and acceptance levels through the constant monitoring and feedback-filtering of the local population, TILOS wishes to encourage increased levels of public engagement that will facilitate the implementation of the proposed energy solution.

In this regard, although the focus is placed on the local population of Tilos, the geographical study to be carried out during the project will also engage the local population of Pellworm, adding in this way ethnographic research characteristics that will provide a more collective picture of how the public respond to the idea of active participation. Nevertheless, active participation and public engagement goes beyond this stage to also consider of novel business models and schemes between the private and the public sector, which can accelerate innovation in the field of energy storage and smart grids.

This is actually one of the main challenges that the TILOS project aims to address, i.e. to quantify the social welfare produced by the operation of novel micro grid schemes, to seek its maximization through achieving high levels of public engagement and finally to indicate appropriate models that will allow the local population to harvest it.

This is also directly associated with the experimentation of the different system operation modes, with each of them implying different levels of public engagement and public benefits (as these are perceived by the local population), calling the local population to also contribute to the debate on the local optimum.


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  • This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 646529.

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