The main objective of the EnerGAware project is to achieve a 15-30% energy consumption and emissions reduction in a social housing pilot and increase the social tenants’ understanding and engagement in energy efficiency. The EnerGAware project will develop and test, in publically owned social housing, a serious game that will be linked to the actual energy consumption (smart meter data) of the game user’s home and embedded in social media and networking tools. The solution fits within all three ICT areas suggested in the topic EE-11 scope: gaming, social networking and personalised data driven applications. The EnerGAware solution will provide an innovative IT ecosystem in which users can design their own virtual home and Avatar and learn about the potential energy savings from installing energy-efficiency measures and changing user behaviour, whilst maintaining the comfort of their Avatar. The user will need to learn to balance the energy consumption, comfort and financial cost of their actions. Energy savings achieved both virtually in the game, calculated by building performance simulation, and in reality, in the users’ actual homes, measured through smart meter data, will enable progression in the serious game. The social media features will provide users a platform to share data of their achievements, compete with each other, give energy advice, as well as, join together to form virtual energy communities. The EnerGAware solution will be developed and deployed with the ‘cleanweb’ philosophy in mind: “Capital light, Quick to market and Quick to scale”, therefore the EnerGAware project will aim to go beyond just testing in a social housing pilot, but will seek commercial exploitation of the solution at the end of the project, through our industrial partners, in particular EDF Energy, a global energy provider, with 38 million European energy customers.

SheaRIOS is a solution for the Wind Turbine Blade (WTB) inspection industry that enables easier, faster and more accurate inspection utilising robotics and shearography, a high-quality method that is applied outside of the laboratory for the first time. A deployment platform will ascend on the wind turbine tower and deploy a work climber on the base of the blade. The climber will move on the blade by means of air-suction and carry out inspection with a shearography kit on a cantilever. The deployment platform will also act as the power and data link. Operational modeling is done by EDF, the end-users that drive this Innovation Action. Preliminary testing and validation of the market-readiness of SheaRIOS robotic application will take place at their site, both on-shore (EDF R&D) and off-shore (EDF Renewables). Three competitive small and mid-scale technology companies from three European countries will contribute so Europe will (1) integrate more wind power, (2) reduce operational costs, (3) keep the technology lead, and (4) remain a major export. As per Wind Europe, these are the targets for enabling wind to become the backbone of our electricity generation system. Based on our analysis, the non-destructive testing service provider would save 1,055€ per wind turbine inspection and payback of SheaRIOS investment will be achieved after 152 inspections, or the first 2 years. The wind farm operator will save more than 1 full day per wind turbine inspection, because of the reduced inspection time, which directly translates to less revenue lost due to idle wind turbines. Finally, the cumulative savings for a period of the first 5 years will translate to €92.74m, assuming SheaRIOS will be successful in averting just 20% of the unforeseen WTB failures and contributing to increased health and safety for the rope access workers that are involved in hundreds of accidents each year. [1] Wind Europe, “Making transition work”, September 2016

The proposed project intends to translate research to practice through rigorous and efficient methodologies and tools to assess seismic safety of NPP. It also has the aim to innovate current practice by supporting simulation results with experimental data and experience feedback in the framework of Bayesian approaches and machine learning. The research will develop methods to improve the predictability of (non linear, best-estimate) beyond design analyses (design extension earthquakes). The refined seismic PSA provides meaningful support in the decision making process and could be useful for real time expertise of plant safety in case of temporary unavailability of safety relevant equipment or structures. It is also proposed to develop efficient tools to identify major contributors to risk such that efforts to increase safety and resistance are focused on relevant equipment. The outcome will thus increase the reliability of the analyses and in turn increase confidence in the probabilistic and deterministic safety assessment results. The results of this project will then help nuclear operators in their periodic safety reviews and to respond to the high-level EU-wide safety objectives of the amended EURATOM nuclear safety directive (stress tests). The considered accident scenarios will provide input for updating severe accident management guidelines (SAMG).

Probabilistic Safety Assessment (PSA) procedures allow to better understand and estimate the likelihood of the most causes prone to initiate nuclear accidents and to identify the most critical elements of the systems. However, despite of the remarkable reliability of current procedures, the 2011 Fukushima Daiichi accident highlighted a number of challenging issues with respect to their application and validity of their results. From this nuclear disaster the upgrading of the current methodological framework appeared to be necessary in areas such as cascading/conjunct events characterization, fragility analyses and uncertainties treatment. New developments in those areas would even enable the extension of their use in accident management. Based on recent theoretical progresses, the NARSIS project aims at making significant scientific updates of some elements required for the PSA, focusing on external natural events (earthquake, tsunami, flooding, high speed winds...). These improvements mainly concern: • Natural hazards characterization, considering concomitant external (simultaneous-yet-independent or cascading) events, and the correlation in intra-event intensity parameters; • Fragility and functionality assessment of main critical NPPs' elements, accounting for conjunct effects (including ageing effects) and interdependencies under single or multiple external aggressions; • Risk integration combined with uncertainty characterization and quantification, to allow efficient risks comparison and account for all possible interactions and cascade effects; • Better processing/integration of expert-based information within PSA, through modern uncertainty theories both to represent in flexible manner experts’ judgments and to aggregate them to be used in a comprehensive manner. The proposed improvements will be tested and validated on simplified and real NPP case studies. Demonstration supporting tools for operational & severe accident management will be also provided.

The growing share of variable renewable energy necessitates flexibility in the electricity system, which flexible energy generation, demand side participation and energy storage systems can provide. SIMBLOCK will develop innovative demand response (DR) services for smaller residential and commercial customers, implement and test these services in three pilot sites and transfer successful DR models to customers of Project partners in further European countries. The pilot sites are blocks of highly energy efficient buildings with a diverse range of renewable and cogeneration supply systems and requisite ICT infrastructure that allows direct testing of DR strategies. SIMBLOCK’s main objectives are to specify the technical characteristics of the demand flexibility that will enable dynamic DR; to study the optimal use of the DR capability in the context of market tariffs and RES supply fluctuations; and to develop and implement market access and business models for DR models offered by blocks of buildings with a focus on shifting power to heat applications and optimization of the available energy vectors in buildings. Actions toward achieving these objectives include: quantifying the reliability of bundled flexibility of smaller buildings via pilot site monitoring schemes; combining innovative automated modelling and optimization services with big data analytics to deliver the best real time DR actions, including motivational user interfaces and activation programs; and developing new DR services that take into account the role of pricing, cost effectiveness, data policies, regulations, and market barriers to attain the critical mass needed to effectively access electricity markets. SIMBLOCK’s approach supports the Work Program by maximizing the contribution of buildings and occupants and combining decentralized energy management technology at the blocks of building scale to enable DR, thereby illustrating the benefits achievable (e.g. efficiency, user engagement, cost).