Renewable Energy Sources Power FOrecasting and SyNchronisation for Smart GriD NEtworks MaNagemenT. Renewable energy sources (RES) play a major role to the EU’s aspiration to transform to a climate-neutral economy. Their integration into the power grid is pivotal to the green transition and to the decarbonisation of the energy sector. However, as the most commonly used RES (solar, wind and hydropower) are also weather-dependent, their power generation capacity varies according to the local microclimatic conditions. This power production variability makes RES difficult to integrate into the power grid and to provide seamless, stable and secure amounts of power. On the other hand, power demand also affects the power grid operation, since there must always be a supply/demand balance in the power grid. Grid power imbalances can cause frequency fluctuations and other unwanted transient phenomena, which can compromise grid stability and operation. For that matter, advanced grid monitoring techniques have been developed, employing phasor measurement units (PMUs) to measure the electrical signals in a precise and synchronised way, based on a reliable timing reference. Yet, currently, no Galileo-based applications on PMU timing exist. In the above framework, RESPONDENT comes to address the challenges of RES power generation forecasting, demand forecasting and smart power grid monitoring and supply/demand balancing. An AI/ML RES power generation forecasting algorithm is proposed, exploiting both Copernicus EO and site-specific weather data, along with renewable energy power conversion models. Furthermore, an AI/ML – multiphysics model for power demand of certain communities is also developed. Lastly, RESPONDENT will build a Galileo-enabled PMU and develop a monitoring module, in order to test and verify the advantages offered from the Galileo timing and synchronization services in smart grid monitoring, power balancing and overall operation.

The current global climate crisis and ongoing shift towards autonomous transport necessitate sustainable and passenger-centric public transport solutions. The goal of OptiPEx is to enhance of sense of comfort and safety of the passengers as well as the security and ease of travelling by co-creating ethical and passenger-aware public transport services together with specific target user groups, such as wheelchair users, passengers with large objects, fragile passengers with limited mobility, tourists and students. To achieve this, OptiPex is built on three fundamental development pillars: measurement, analytics and interaction. OptiPEx partners will develop perception technologies to measure passenger behaviour and situations. Trustworthy analytics is essential in recognising real-time passenger experiences and situations, enabling interaction with services and vehicles. Moreover, OptiPEx partners will develop adaptive and interactive vehicle technologies and digital services in collaboration with target groups and suitable for various public transport modes. These services will optimise the onboard experience, promoting safety, inclusiveness and trust. Ultimately improved passenger satisfaction will drive the adoption of automated public transport technologies and improve the sustainability of mobility services via contribution to the modal shift. A consortium consisting of 3 research organisations, 6 industry members and 2 SMEs will validate the developed solutions together with target groups and other stakeholders in three living labs. The consortium has leading expertise in human behaviour, user-centric design, vehicle technologies, modelling and data analysis, and adaptive and interactive services. The successful adoption of OptiPEx results will be facilitated by efficient dissemination, communication, and exploitation activities in collaboration with the Connected, Cooperative and Automated Mobility (CCAM) partnership.

DELTA proposes a DR management platform that distributes parts of the Aggregator’s intelligence into lower layers of a novel architecture, based on VPP principles, in order to establish a more easily manageable & computationally efficient DR solution, ultimately aiming to introduce scalability & adaptiveness into the Aggregator’s DR toolkits; the DELTA engine will be able to adopt & integrate multiple strategies & policies provided from its energy market stakeholders, making it authentically modular & future-proof. DELTA will also deliver a fully autonomous architectural design, enabling end-users to escape the hassle of responding to complex price/incentive-based signals, while facilitating active, aware & engaged prosumers, based on innovative award schemes, a social collaboration platform & enhanced DR visualisation. Provision of full-scale market & grid services will be made possible by delivering explicit & implicit-based DR elasticity services, while pushing current market regulatory limitations so that they can be surpassed, and satisfying potential grid constraints related to flexibility activation through Multi-Factor Forecasting and Deep Reinforcement Learning Profiling. Furthermore, DELTA will propose & implement novel multi-agent based, self-learning energy matchmaking algorithms to enable aggregation, segmentation & coordination of several diverse supply & demand clusters, designed end-to-end using well-known, open protocols (i.e. OpenADR), for increasing interoperability. DELTA will set the milestone for data security in future DR applications by not only implementing novel block-chain methods & authentication mechanisms, but also by making use of Smart Contracts which would further secure & facilitate Aggregators-to-Prosumers transactions. Two pilots in UK & Cyprus will realise the DELTA concept, covering a wide variety of residential/tertiary loads (>11GWh), RES generation (>14GWh) & energy storage systems (>9MWh) (average annual measurements).

Smart factories are characterized by increasing automation and increasing customization. In these dynamic environments flexible and adaptive work organization is crucial both for productivity and work satisfaction. Factory2Fit project will support this development by developing adaptation solutions with which people with different skills, capabilities and preferences can be engaged, motivated and productive members of the work community in manufacturing industries. The core idea in Factory2Fit project is that the worker is an expert of his/her own work and thus (s)he shall have an active role in designing his/her work. The proposed adaptive automation solutions are based on a dynamic user model that includes physical and cognitive abilities. The worker him/herself gets feedback of his performance and skills, which supports continuous learning and competence development. Virtual factory models will be used as engaging platforms for participatory design of work practices, knowledge sharing and training, involving all the relevant stakeholders in contributing the organizational development. Contextual guidance and knowledge sharing is supported by augmented reality based tools. The adaptation solutions will be developed within three industrial pilots in actual manufacturing environments. The solutions will be generalized and disseminated widely to the manufacturing industry. Adaptive automation solutions to be developed in Factory2Fit will support fluent human-automation cooperation and will have impacts in work satisfaction, less occupational health issues, less stress, better ergonomics, better quality, less errors and better productivity. Adaptive automation supports current and forthcoming workers to develop their competences towards knowledge workers of smart factories with fulfilling work careers. This will further improve the competitiveness of European manufacturing industry and support the principle of responsible manufacturing industry.