This project targets building a proposal for a H2020 FETOPEN- Research and Innovation Actions (FETOPEN1) call of the work programme 2016-2017. It consist in two main actions consisting, on the one hand, to complete and validate the consortium, based on pre-existing informal contacts, and, on the other hand, to write the proposal. The core network is based on two French partners, CNRS (including two laboratories, IEMN and IJL) and CEA-INES (LITEN/DTS) as well as a German partner, Fraunhofer Institute for Solar Energy Systems. The targeted FETOPEN project will aim demonstrating the feasibility of a high efficiency solar cell which figure of merit in €/W should be ultimately in line with the crystalline silicon one. We will use an inorganic/inorganic tandem cell concept using earth abundant and nontoxic raw materials. Fabrication processes will be matched and up scalable to mass production. Today, such a tandem cell concept appears among the best solutions exhibiting cells with 30% and more efficiency. The approach is based on the use of a crystalline silicon bottom cell, in homo- and hetero-junction structure, since these technologies are already well established and display optimised efficiency values as well as stability and cost performance. The technological breakthrough is more particularly located in the development of a thin film technology for top cell using earth abundant materials. The Zn(Sn,Ge,Si)N2 material line is proposed since its bandgap energy is in perfect matching with silicon one for a tandem cell structure. It is a most challenging and exploratory material line, only a few American groups started to work on it. The second challenge will be to combine such a nitride cell with an usual silicon cell in a monolithic (2-terminal) tandem cell. Research will be devoted to the development of theoretical and experimental background on the different materials required for the top nitride cell, the design and fabrication of such a cell, the design of an optimum structure of tandem cell, including the mandatory tunnel junction, the fabrication of demonstrators and their characterization. The envisaged consortium will be composed of European partners with matched expertise allowing this multidisciplinary (material growth, modelling, cell design, cell fabrication and characterization among the main topics) problematic to be answered. Such a success will pave the way to develop a new high efficiency solar cell concept meeting cost and lifetime demands

Solar resource is the optimum renewable energy source to build a climate-neutral energy economy in Europe. However, it is under-exploited for producing heat and electricity, and even more for solar fuels. Today, the large needs for heat and electricity are covered by large-scale power plants delivering an energy vector or another, but not both. CONVERGY is a modular use of concentrated solar beams to produce electricity or heat when needed, considering real-time energy demand and their “selling price” for a given location, largely smoothing the main drawback of the renewable energy sources: its intermittency. It is also a single infrastructure for several energy vectors, a path towards energy production cost reduction and so a support for penetration of renewable energies in EU. The main objectives of CONVERGY are to: - Unlock the current technological bottleneck by developing concentrated photovoltaic receiver modules reaching efficiencies >43% at cell level and >41.9% at receiver level. - Demonstrate and validate at lab-scale (TRL4) the modular use of a single field of heliostats for both heat and electricity production. - Align modular production potential with solar resource to reach levelized cost of electricity of 0.08 €/kWh and roadmap where to implement solution. Main impacts will be to build a knowledge platform on modular use of solar resource that will contribute to save up to 600 tons carbon emission in 2030 and up to 20 Mt in 2050. Also, the project will provide a backbone for the use of energy in future scenarios, support the emergence of new business models where EU actors all along the value chain will be reinforced. The European scientific network from this proposal brings together 5 European partners including 1 University / 2 RTO for technology development, 2 industrials for demonstration and for technology integration and operation, and 1 non-European partner, worldwide leader of concentrated solar use, and ready to transfer knowledge towards partnership. It will provide the support to strengthen the network collaboration towards the setup, the submission and the acceptance of the European project Convergy to be submitted at the next H2020-LC-SC3-RES-1 call.

Heat pumps constitute an effective solution to mitigate the energy consumption and environmental impact of buildings. They have a high potential to use renewable energies for converting power to heat. However, the actual on-field performance of heat pumps does not always rise to expectations. High heat losses occur, and energy efficiency is reduced due to unsuitable system layout but also mal parametrization of heat pump controls and undetected operation deficits. The current practice is to set the control parameters by the installer once and for all, however, the over-time variability, e.g. of occupancy behaviour, building’s age, weather conditions, requires an adaptive heat pump control and supervision. The subject of the AI4HP project is therefore the development of novel artificial intelligence (AI) methods based on incremental learning with artificial neural networks (ANN) for adaptive heat pump control and monitoring. AI methods provide a “self-learning” capability allowing to create and adapt automatically models and predictions just from measurement data. The resulting low-effort/low-cost adaptive AI methods will improve the operational performance of heat pumps by a) automatically adapting controller settings to varying boundary conditions and b) detecting heat pump and system mal-functioning. This leads to the development of a new generation of “smart heat pumps”, which integrate new functionalities and interactions with a changing environment in order to provide the best energy-efficiency and comfort for the user, make maintenance operations easier and avoid performance degradation by fault detection. The project addresses both dual service heat pumps and heat pumps serving domestic hot water only, which make up a substantial part of the French market. ANNs can improve heat pump operation by system modelling and predicting future developments based on measurement data. They can capture the complexity of the heat pump system – e.g., there is not a one-to-one relationship between a specific fault and a single variable. However, in most cases ANNs and other machine learning methods lead to large errors when confronted with significantly different or new data. Another problem is the continuous generation of measurement data. The memory size and computing power are limited and prevent a retraining using the complete data set. However, if the system is trained only on the new data, catastrophic forgetting or interference occurs. Consequently, for real-time use in leading edge technologies the ANN system must continuously learn new knowledge without forgetting previous knowledge, which requires more logic than the existing methods alone. In this project, the consortium consisting of experts in the fields of ANN research, energy research, heat pump manufacturing and energy supply will develop adaptive ANNs based on methods of incremental learning, which are suitable for real-time use in heat pump operation with continuous measurement data acquisition. The adaptive AI pipeline is being developed for the three use cases adaptive heating curve control, adaptive control based on load forecasts and fault detection and diagnosis (CEA List, LPNC, Fraunhofer ISE), implemented in a heat pump controller (Stiebel Eltron) and validated in laboratory tests and a pilot system (EDF, Stiebel Eltron). Using the advanced AI methods, we expect up to 20% energy savings and CO2-emissions reduction for domestic hot water and space heating without comfort violation. If the project aims can be successfully validated in the pilot demonstrations, the industrial partners plan to initiate a project development in a short to middle term after the end of the project to include the AI routines in their portfolio, either by an implementation directly in the heat pump controller or as a service in their heat pump cloud platform.