The general objective of the project is the development of an integrated planning tool for multi-energy systems on a European scale. To reach the COP21 goals concerning a stepwise reduction of energy-related greenhouse gas (GHG) emissions in a cost effective way, the decarbonisation of multiple energy sectors is necessary. Therefore, the model considers the coupling of different energy sectors (electricity, heat, mobility and gas) and calculates the cost-optimal energy mix for the future European energy system (e. g. up to 2050) that is compliant with the climate goals. Besides generation and storage systems, also transmission and distribution grids are considered in the planning and operation stage in an integrated way. These modeling requirements lead to both a large mixed-integer (non-linear) optimization problem and new solution methods that will be developed within the course of the project. This will be achieved by solving both mathematical and computational challenges in the field of energy system modeling. Thereby, novel mathematical formulations of energy system modeling problems will be proposed, e. g. by combining diverse mathematical decomposition methods. The goal is to strive towards a system, where a multiplicity of models for single energy system aspects all synergistically contribute to the optimal planning of such a complex system. The project will provide a new energy system planning tool for different stakeholders of the energy system, which promotes optimal development and operation of the system. This includes European system planners, regulators and national authorities as well as technology companies, grid operators and utilities. To ensure the applicability of the developed tools, an advisory board will review the intermediate and final results of the project. Finally, two case studies with European scope will be performed to show the adequacy and relevance of the developed modelling framework.

Planning and scheduling (P&S) is a core area of AI. Its aim is to build systems that assist humans in planning, organising and optimising courses of action to achieve complex objectives. Despite the pressing need for decision-support systems for P&S applications in industry and public services, current approaches do not satisfy essential properties of trustworthy AI, such as transparency, explainability, robustness, safety and scalability. TUPLES is a 3 year project aiming to obtain scalable, yet transparent, robust and safe algorithmic solutions for P&S. The cornerstones of our scientific contributions will be (1) combining symbolic P&S methods with data-driven methods to benefit from the scalability and modelling power of the latter, while gaining the transparency, robustness, and safety of the former and (2) developing rigorous explanations and verification approaches for ensuring the transparency, robustness, and safety of a sequence of interacting machine learned decisions. Both of these challenges are at the forefront of AI research. We will demonstrate and evaluate our novel and rigorous methods in a laboratory environment, on a range of use-cases in manufacturing, aircraft operations, sport management, waste collection, and energy management. Our results also include practical guidelines derived from the lessons learnt in this process, and open-source software tools and test environments enabling the human-centered development and assessment of trustworthy P&S systems. Expected outcomes include increased productivity, decreased environmental footprint and the empowerment of workers in the above sectors. These could translate into huge economic, environmental and social impacts if trustworthiness ends up driving mass adoption of P&S. The TUPLES consortium includes world-leading researchers in several fields of AI (P&S, constraints, machine learning, explanations), humanities and social sciences (psychology, law, ethics), and experts of their applications.

The overall objective of the Upgrade DH project is to improve the performance of inefficient district heating networks in Europe by supporting selected demonstration cases for upgrading, which can be replicated in Europe. The Upgrade DH project supports the upgrading and retrofitting process of DH systems in different climate regions of Europe, covering various countries: Bosnia-Herzegovina, Denmark, Croatia, Germany, Italy, Lithuania, Poland, and The Netherlands. In each of the target countries, the upgrading process will be initiated at concrete DH systems of the so called Upgrade DH demonstration cases (demo cases). The gained knowledge and experiences will be further replicated to other European countries and DH systems (replication cases) in order to leverage the impact. Core activities of the Upgrade DH project include the collection of the best upgrading measures and tools, the support of the upgrading process for selected district heating networks, the organisation of capacity building measures about DH upgrading, financing and business models, as well as the development of national and regional action plans. In addition, an image raising campaign for modern DH networks will be carried out in the Upgrade DH project. The outcome will be the initiation of district heating upgrading process in the above mentioned target countries and beyond.

RETROFEED main objective is to enable the use of an increasingly variable, bio-based and circular feedstock in process industries through the retrofitting of core equipment and the implementation of an advanced monitoring and control system, and providing support to the plant operators by means of a DSS covering the production chain. This approach will be demonstrated in five resource and energy intensive sectors (ceramic, cement, aluminium, steel, and agrochemical) with the potential to reach in average an increase of 22% in resource efficiency and 19% in energy efficiency, with a consequent reduction in costs and GHG emissions of 9.3 M€ and 135 kton CO2 respectively. Furthermore, the project aims to develop a methodology to support retrofitting in resource and energy intensive industries that will be complemented by a decision support system able to perform a diagnosis of the impact in the process of different retrofitting solutions so plant managers and operators can decide on the most suitable retrofitting action for their companies. This decision support system will remain operative after the modification of the process so it can ease the operation of the process under the increased variability of feedstock by analysing a set of key indicators defined within RETROFEED over the production chain for this purpose. RETROFEED consortium comprises strong industrial participation; 9 large companies, including 5 of them as final users, and 4 SMEs as technology providers, working with experienced RTOs and supporting entities. The private investment associated to RETROFEED is over 7M€ along the execution of the project. Lastly, RETROFEED is expected not only to enable technological advances in the technologies and demonstrators involved but will also contribute to the development of new standards, regulations, training programmes, and adaptation and certification of industrial processes thus facilitating the replication of the project results within the EU industry.