The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase. 

The optimal utilisation of distribution grids requires the proactive management of volatilities caused by mixed energy resources installed into different grid levels, such as buildings, energy communities (ECs) and substations. In this context, proactive control based on predictions for energy demand and generation is applied. The mitigation of conflicts between the stakeholders' objectives is the main challenge for the control of centralized and distributed energy resources. In this paper, a bi-level approach is proposed for the control of stationary battery energy storage systems (SBES) supporting the local distribution system operator (DSO) at the transformer level, as well as distributed energy resources (DERs) operated by end customers, i.e., EC-members. Model predictive control (MPC)- based and hybrid approaches merging rule- and MPC-based control schemes are evaluated. Simulation studies based on a typical European low voltage (LV) feeder topology yield the performance assessment in terms of technical and economic criteria. The results show an advantage of hybrid approaches with respect to the DSO's cost savings from peak shaving. From the EC's perspective, both hybrid and MPC-based schemes can achieve effective cost savings from proactive energy management.

As the demand for data privacy and low latency grows, edge computation carried out at edge data center nodes is believed to become increasingly important for future telecom applications. Providers must consider various factors, including power consumption, thermal dynamics, and the ability to maintain high-quality service, in addition to deployment and service orchestration. This paper presents a detailed description of two different prototype edge data centers designed to investigate the power performance and thermal dynamics of edge nodes under various applied services. The prototypes were developed and tested at the RISE ICE Datacenter research facility. We present the results of power flow experiments in which input current from the grid was limited while the computational load was maintained using the energy stored in batteries. We further discuss implications for placing edge data center nodes in locations with temporal power constraints and opportunities for participation in support services at the grid level.

Emerging use-cases like smart manufacturing and smart cities pose challenges in terms of latency, which cannot be satisfied by traditional centralized infrastructure. Edge networks, which bring computational capacity closer to the users/clients, are a promising solution for supporting these critical low latency services. Different from traditional centralized networks, the edge is distributed by nature and is usually equipped with limited compute capacity. This creates a complex network to handle, subject to failures of different natures, that requires novel solutions to work in practice. To reduce complexity, edge application technology enablers, advanced infrastructure and application orchestration techniques need to be in place where AI and ML are key players.

This article investigates the problem of where to place the computation workload in an edge-cloud network topology considering the trade-off between the location-specific cost of computation and data communication. For this purpose, a Monte Carlo simulation model is defined that accounts for different workload types, their distribution across time and location, as well as correlation structure. Results confirm and quantify the intuition that optimization can be achieved by distributing a part of cloud computation to make efficient use of resources in an edge data center network, with operational energy savings of 4–6% and up to 50% reduction in its claim for cloud capacity.

Server power densities are foreseen to increase, and conventional air-cooling systems will struggle to cope with thermal demand. Single-phase immersion systems are a promising alternative to operate very intensive workload such as high-performance computing, cryptocurrencies mining or research activities. However, few companies deal with this kind of system and there is a lack of energy models that can reproduce an accurate analysis of the system behaviour. This study addresses the experimentation, data collection, and model validation of a single-phase immersion cooling system where 54 open compute project servers, each with a peak power of 400 Watts that are submerged and operated in a dielectric coolant. Results show the evolution of the thermal profile of the system under static and dynamic workloads, and it provides a correlation of server energy use under various system temperatures. The energy model is presented, validated against real data, and exploited to investigate the system response to different cooling conditions. In conclusion, the study demonstrates the validation of the energy model and supports the basis for further investigation. © 2023 The Authors

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs.

In this paper, we explore the use of Reinforcement Learning (RL) to improve the control of cooling equipment in Data Centers (DCs). DCs are inherently complex systems, and thus challenging to model from first principles. Machine learning offers a way to address this by instead training a model to capture the thermal dynamics of a DC. In RL, an agent learns to control a system through trial-and-error. However, for systems such as DCs, an interactive trial-and-error approach is not possible, and instead, a high-fidelity model is needed. In this paper, we develop a DC model using Computational Fluid Dynamics (CFD) based on the Lattice Boltzmann Method (LBM) Bhatnagar-Gross-Krook (BGK) algorithm. The model features transient boundary conditions for simulating the DC room, heat-generating servers, and Computer Room Air Handlers (CRAHs) as well as rejection components outside the server room such as heat exchangers, compressors, and dry coolers. This model is used to train an RL agent to control the cooling equipment. Evaluations show that the RL agent can outperform traditional controllers and also can adapt to changes in the environment, such as equipment breaking down. 

This report investigates the problem of where to place computation workload in an edge-cloud network topology considering the trade-off between the location specific cost of computation and data communication.

ANIARA (https://www.celticnext.eu/project-ai-net) attempts to enhance edge architectures for smart manufacturing and cities. AI automation, orchestrated lightweight containers, and efficient power usage are key components of this three-year project. Edge infrastructure, virtualization, and containerization in future telecom systems enable new and more demanding use cases for telecom operators and industrial verticals. Increased service flexibility adds complexity that must be addressed with novel management and orchestration systems. To address this, ANIARA will provide en-ablers and solutions for services in the domains of smart cities and manufacturing deployed and operated at the network edge(s). © 2021 Owner/Author.