The EURECA project tackles the lack of knowledge and awareness of how to identify and procure environmentally sound and greener data centres. The work will encompass solutions for pre-commercial procurement (PCP) and procurement of innovative solutions (PPI). This will be achieved by consolidating recognised and emerging benchmark criteria into an easy-to-use tool that can be deployed by non-experts. EURECA will recommend an improvement roadmap indicating the procurement options(s) to reduce energy consumption, make efficiencies and minimise the environmental footprint. Key criteria will be presenting the Cost-Benefit analysis, covering the life cycle of the datacentre and the environmental impact. The project will strengthen business cases by presenting training and advisory resources on how to establish the options with both technical and commercially neutral information, without prejudice. These options include, as appropriate, to perform detailed studies on investing in existing staff, refitting facilities, consolidation actions, new builds, or outsourcing – or specific combinations or subsets of these. The resources include RFI, ITT or RFP templates, technical & environmental data directories and a case study catalogue, structured along the procurement workflow. The ultimate goal is to enable procurement teams to choose environmentally sound buying options whilst producing true and robust cost-benefit visibility to enable successful triggering of tenders. To ensure efficient use of the project’s developments, we will deliver a coherent set of targeted and efficient training components, developed throughout the project that supports the use of the EURECA tool and its resources. The consortium’s existing comprehensive liaisons to European and international standards committees and industry groups will ensure the EURECA programme is a “living” resource that is sustainable, interactive and able to reflect the latest developments. .

Data Centres (DC) are among the largest yet increasing energy consumers, due to the rising digitization of human activities. RES integration and energy efficiency improvements have the potential to reduce significantly DC carbon footprint, while increasing at the same time the auto-consumed energy, thus improving DC security and resiliency of supply against climate change. However, very few solutions, despite validated in lab, have been successfully deployed on operational DCs, mostly due to technological fragmentation, excessive CAPEX and lack of appropriate business models. CATALYST will address these challenges through turning existing/new DCs into flexible multi-energy hubs, which can sustain investments in RES and energy efficiency by offering mutualized flexibility services to the smart energy grids (both electricity and heat grids). By leveraging on the outcomes of FP7 GEYSER and DOLPHIN projects, CATALYST will adapt, scale up, validate and deploy an innovative, adaptable technological and business framework aimed at i) exploiting available DC non-IT legacy assets (onsite RES/backup generation, UPS/batteries, cooling system thermal inertia, heat pump for waste heat reuse) to deliver simultaneous energy flexibility services to multi-energy coupled electricity/heat/IT load marketplaces ii) deploying Cross-DC cross-infrastructures (e.g. heat vs IT) IT workload orchestration, by combining heat-demand driven HPC geographical workload balancing, with traceable ICT-load migration between federated DCs to match IT demands with time-varying on-site RES (“follow the energy approach”) iii) providing marketplace-as-a-service tools to nurture novel ESCO2.0 business models. The adaptation and replication potential of CATALYST will be demonstrated through carrying out four different real life trials spanning through the full spectrum of DCs types (fully distributed DCs, HPC, co-location, legacy) and architectures (from large centralized versus decentralized micro-DCs).

Artificial-Intelligence-Augmented Cooling System for Small Data Centres “ECO-Qube”; is a holistic management system which aims to enhance energy efficiency and cooling performance by orchestrating both hardware and software components in edge computing applications. ECO-Qube is a data driven approach which utilizes valuable unused data from active data centre components. Created big data is being used by an artificial intelligence augmented system which detects cooling and energy requirements instantaneously. ECO-Qube differentiates from conventional cooling systems which keep operating temperatures within a strict interval and do not evaluate measurable cooling performance. Unmeasured cooling performance leads underperformed airflow, thermal disequilibrium, and high energy consumption. On the contrary, ECO-Qube offers a zonal heat management system which benefits from Computational Fluid Dynamics (CFD) simulations to adapt cooling system for the best airflow and cooling performance with minimum energy consumption. Moreover, ECO-Qube realizes smart workload orchestration to keep the CPUs at their most energy efficient state and maintain the thermal equilibrium to reduce overheating risk. Sustainability is another priority for ECO-Qube’s smart energy management system (EMS), which is designed to track the energy demand and operate the energy supply in cooperation with building/district’s EMS. This synergy maximizes the energy supplied from renewable energy sources and minimizes the energy supplied from sources with big carbon footprint. ECO-Qube solution will be assessed in three different pilots from different climatic conditions to validate energy efficiency under different external variables.