Today, there are still several R&D barriers, and user-acceptance-related challenges that hinder the smooth integration and proliferation of multiple Renewable Energy Technologies (RETs) in buildings. In response to that, RE-COGNITION proposes a holistic, end-to-end RETs Integration Framework towards energy positive buildings with a focus on small and medium-sized buildings in Europe. Through the envisaged Automated Cognitive Energy Management Engine (ACEME), RE will be utilized more efficiently paired with appropriate storage technologies and innovative energy systems to meet the electricity and heating/cooling demand of the buildings. The framework is designed to enable the integration of multiple, heterogeneous, energy generating systems covering the spectrum of available building-scale RES (solar (PV, thermal/ cooling), wind, bio-energy (renewable biofuel through micro-CHP) and geothermal) and demonstrating future-proof extensibility. To this end, the project entails R&D at the level of single technologies and their interconnection with novel energy systems (like heat-pumps harnessing geothermal energy, absorption chillers) leveraging current IoT and smart-grid standardization outcomes. Along with measurable improvements on each technology’s efficiency, performance, desired characteristics and cost-effectiveness, RE-COGNITION ensures optimal integration of RETs in buildings, as well as (inter)operation and matching between building RE supply and energy demand. Its stakeholder-centred approach aligns both the process and its outcomes with the needs and expectations of (end-)users by providing tools that facilitate large-scale deployment of building-scale RETs. For 36 months 15 partners from 7 EU countries will provide technology know-how, lab facilities & 5 validation sites and will work towards meeting EU’s expectations for reduced dependence on fossil fuels and cost-effectiveness compared to conventional energy generation and management solutions in buildings.

To reach the goals of improving the efficiency of CHP systems while simultaneously widening the biomass feedstock base as well as increasing operational flexibility, the project aims to develop a full scale technology demonstrator of a hybrid power plant using biogas as main fuel in lab environment. A combined hybrid heat and power plant combines a micro gas turbine (MGT) and a solid oxide fuel cell (SOFC). The focus of the technology demonstration plant is to prove the functional capability of the plant concept, followed by detailed characterization and optimization of the integration of both subsystems. The main objective is to move the technology beyond the state of the art to TRL 4. Electrical efficiencies of more than 60% and total thermal efficiencies of more than 90% are intended to reach at base load conditions. An operational flexibility ranging from 25% to 100% electric power should be achieved. The emission levels should not exceed 10 ppm NOx and 20 ppm CO (at 15% vol. residual oxygen). The system should allow the use of biogas with methane contents varying from 40-75%, thus covering the biogas qualities from the fermentation of the entire biomass feedstock range. To achieve the objectives the subsystems MGT and SOFC including their subcomponents have to be adjusted and optimized by a multidisciplinary design approach using numerical and experimental measures to ensure a proper balance of plant. In addition an integrated control system has to be developed and implemented to achieve a reliable operation of the coupled subsystems. A detailed analysis of different European markets, economic and technical constraints in terms of biogas production potentials will clarify the regional suitable sizes and attractive performance conditions of the power plant system. To identify cost reduction potentials a thermo-economic analysis will be performed. Here, an internal rate of return (IRR) of the system of higher than 15% should be achieved over a 20 years.

Micro Combined Heat and Power (mCHP) systems are a perfect addition to stabilize the electricity grid in the increased presence of volatile renewable sources. Due to their efficient generation and local use of heat and electricity, their fuel saving- and CO2 reduction potential is tremendous. In spite of great interest of the market and policy-makers, currently available mCHP systems suffer from limited life, high investment and very high maintenance cost, making them too expensive for serious market uptake. MTT solves this problem with the EnerTwin, a mCHP system based on a micro gas turbine. The EnerTwin uses commercial off-the-shelf components resulting in low investment cost. Gas turbines are known for low-maintenance, high power density (small size) and long life. MTT uses automotive turbochargers as key components of the turbine: these are produced in millions and contribute to the low cost and high reliability of the EnerTwin. Gas turbines are inherently insensitive to varying fuel compositions facilitating use of various grades of natural gas. Currently, the EnerTwin is at TLR 7: 19 systems have been deployed in 1st-stage field tests at client locations since mid 2013. Besides the field-trial units, MTT has already sold 500 commercial EnerTwins, which promises an excellent commercial market perspective, while concrete contracts are under negotiation for high volumes for Canadian- and Chinese markets. The main objective of this project is the readiness for commercialisation of the EnerTwin. MTT and its industrial project partners will improve the mCHP to meet future CE and ECO Design requirements. Together with these partners, MTT will work on component and system optimisation for reliability and large-volume manufacturing. Additional field-test units will be deployed to test use cases and validate improvements. By the end of the project MTT expects to close at least 5.000 pre-orders for EnerTwins, resulting in creating over 600 qualified job positions.

Machine learning have revolutionized the way we use computers and is a key technology in the analysis of large data sets. The FUDIPO project will integrate machine learning functions on a wide scale into several critical process industries, showcasing radical improvements in energy and resource efficiency and increasing the competitiveness of European industry. The project will develop three larger site-wide system demonstrators as well as two small-scale technology demonstrators. For this aim, FUDIPO brings together five end-user industries within the pulp and paper, refinery and power production sectors, one automation industry (LE), two research institutes and one university. A direct output is a set of tools for diagnostics, data reconciliation, and decision support, production planning and process optimization including model-based control. The approach is to construct physical process models, which then are continuously adapted using “good data” while “bad data” is used for fault diagnostics. After learning, classification of data can be automated. Further, statistical models are built from measurements with several new types of sensors combined with standard process sensors. Operators and process engineers are interacting with the system to both learn and to improve the system performance. There are three new sensors included (TOM, FOM and RF) and new functionality of one (NIR). The platform will have an open platform as the base functionality, as well as more advanced functions as add-ons. The base platform can be linked to major automation platforms and data bases. The model library also is used to evaluate impact of process modifications. By using well proven simulation models with new components and connect to the process optimization system developed we can get a good picture of the actual operations of the modified plant, and hereby get concurrent engineering – process design together with development of process automation.