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Publications (10 of 18) Show all publications
Efkarpidis, N., Imoscopi, S., Bratukhin, A., Brännvall, R., Franzl, G., Leopold, T., . . . Sauter, T. (2024). Proactive Scheduling of Mixed Energy Resources at Different Grid Levels. IEEE Transactions on Sustainable Energy, 15, 952
Open this publication in new window or tab >>Proactive Scheduling of Mixed Energy Resources at Different Grid Levels
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2024 (English)In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, p. 952-Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Keywords
Controllers; Costs; Electric power distribution; Electric substations; Energy management systems; Energy resources; Level control; Model predictive control; Predictive control systems; Bi-level energy management framework; Cost saving; Deterministics; Distributed Energy Resources; Level controllers; Low-level controllers; Management frameworks; Model-predictive control; Predictive models; RBC; Robust; Stakeholder; Transformer; Uncertainty; Upper level controller; Energy management
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:ri:diva-67990 (URN)10.1109/TSTE.2023.3320055 (DOI)2-s2.0-85173370167 (Scopus ID)
Note

The transnational project SONDER has received funding inthe framework of the joint programming initiative ERA-NetSmart Energy Systems’ focus initiative Integrated, RegionalEnergy Systems, with support from the European Union’sHorizon 2020 research and innovation programme under grantagreement No 775970. 

Available from: 2023-11-24 Created: 2023-11-24 Last updated: 2024-06-10Bibliographically approved
Fredriksson, S., Eleftheriadis, L., Brännvall, R., Bäckman, N. & Gustafsson, J. (2023). ANIARA: Experimental Investigation of Micro Edge Data Centers with Battery Support on Power-Constrained Grids. In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems: . Paper presented at e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems. June 2023 (pp. 72-78). Association for Computing Machinery
Open this publication in new window or tab >>ANIARA: Experimental Investigation of Micro Edge Data Centers with Battery Support on Power-Constrained Grids
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2023 (English)In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 72-78Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
Thermodynamics, Edge DC, power infrastructure, Power flow dynamics
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-65655 (URN)10.1145/3599733.3600252 (DOI)
Conference
e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems. June 2023
Available from: 2023-07-05 Created: 2023-07-05 Last updated: 2023-07-05Bibliographically approved
John, W., Balador, A., Taghia, J., Johnsson, A., Sjöberg, J., Marsh, I., . . . Dowling, J. (2023). ANIARA Project - Automation of Network Edge Infrastructure and Applications with Artificial Intelligence. ACM SIGAda Ada Letters, 42(2), 92-95
Open this publication in new window or tab >>ANIARA Project - Automation of Network Edge Infrastructure and Applications with Artificial Intelligence
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2023 (English)In: ACM SIGAda Ada Letters, Vol. 42, no 2, p. 92-95Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-66258 (URN)10.1145/3591335.3591347 (DOI)
Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-11Bibliographically approved
Brännvall, R., Stark, T., Gustafsson, J., Eriksson, M. & Summers, J. (2023). Cost Optimization for the Edge-Cloud Continuum by Energy-Aware Workload Placement. In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems: . Paper presented at e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems. June 2023 (pp. 79-84). Association for Computing Machinery
Open this publication in new window or tab >>Cost Optimization for the Edge-Cloud Continuum by Energy-Aware Workload Placement
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2023 (English)In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 79-84Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
cost optimization, sustainability, data center, edge, energy efficiency
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-65654 (URN)10.1145/3599733.3600253 (DOI)
Conference
e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems. June 2023
Available from: 2023-07-05 Created: 2023-07-05 Last updated: 2023-07-05Bibliographically approved
Taddeo, P., Romaní, J., Summers, J., Gustafsson, J., Martorell, I. & Salom, J. (2023). Experimental and numerical analysis of the thermal behaviour of a single-phase immersion-cooled data centre. Applied Thermal Engineering, 234, Article ID 121260.
Open this publication in new window or tab >>Experimental and numerical analysis of the thermal behaviour of a single-phase immersion-cooled data centre
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2023 (English)In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 234, article id 121260Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Data centre, Energy model, Immersion cooling, Simulation, Single-phase cooling
National Category
Energy Engineering
Identifiers
urn:nbn:se:ri:diva-65978 (URN)10.1016/j.applthermaleng.2023.121260 (DOI)2-s2.0-85166949943 (Scopus ID)
Note

This work has received funding from the European Union H2020 Framework Programme under Grant Agreement no. 857801 (WEDISTRICT). IREC authors would like to thank Generalitat de Catalunya for the project grant given to their research group (2021 SGR 01403). Ingrid Martorell would like to thank Generalitat de Catalunya for the project grant given to her research group (2021 SGR 01370).

Available from: 2023-08-23 Created: 2023-08-23 Last updated: 2023-08-23Bibliographically approved
Brännvall, R., Gustafsson, J. & Sandin, F. (2023). Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers. Energies, 16(5), Article ID 2255.
Open this publication in new window or tab >>Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers
2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 5, article id 2255Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
edge data center, heat management, heat reuse, modular machine learning, transferable machine learning, recurrent neural network, transfer learning, meta-learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-64222 (URN)10.3390/en16052255 (DOI)
Note

Funding: Vinnova through the Celtic Next project AI-NET Aniara with project-ID C2019/3-2

Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-08-28Bibliographically approved
Heimerson, A., Sjölund, J., Brännvall, R., Gustafsson, J. & Eker, J. (2022). Adaptive Control of Data Center Cooling using Deep Reinforcement Learning. In: Proceedings - 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022: . Paper presented at 3rd IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022, 19 September 2022 through 23 September 2022. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Adaptive Control of Data Center Cooling using Deep Reinforcement Learning
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2022 (English)In: Proceedings - 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
Adaptive Control, CFD Modeling, Data-center Cooling, Reinforcement Learning, Adaptive control systems, Computational fluid dynamics, Controllers, Cooling, Deep learning, Learning systems, Computational fluid dynamics modeling, Cooling equipment, Data center cooling, Datacenter, First principles, Machine-learning, Reinforcement learning agent, Reinforcement learnings, Thermal dynamics
National Category
Control Engineering
Identifiers
urn:nbn:se:ri:diva-61598 (URN)10.1109/ACSOSC56246.2022.00018 (DOI)2-s2.0-85143075382 (Scopus ID)9781665471374 (ISBN)
Conference
3rd IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022, 19 September 2022 through 23 September 2022
Note

Funding details: VINNOVA, ITEA3-17002; Funding text 1: This work was supported by Vinnova grant ITEA3-17002 (AutoDC).

Available from: 2022-12-21 Created: 2022-12-21 Last updated: 2023-06-07Bibliographically approved
Brännvall, R., Stark, T., Gustafsson, J., Eriksson, M. & Summers, J. (2022). Cost Optimization by Energy Aware Workload Placement for the Edge Cloud Continuum.
Open this publication in new window or tab >>Cost Optimization by Energy Aware Workload Placement for the Edge Cloud Continuum
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2022 (English)Report (Other academic)
Abstract [en]

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.

National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-64293 (URN)
Available from: 2023-04-17 Created: 2023-04-17 Last updated: 2023-06-07Bibliographically approved
Marsh, I., Paladi, N., Abrahamsson, H., Gustafsson, J., Sjöberg, J., Johnsson, A., . . . Amiribesheli, M. (2021). Evolving 5G: ANIARA, an edge-cloud perspective. In: Proceedings of the 18th ACM International Conference on Computing Frontiers 2021, CF 2021: . Paper presented at 18th ACM International Conference on Computing Frontiers 2021, CF 2021, 11 May 2021 through 13 May 2021 (pp. 206-207). Association for Computing Machinery, Inc
Open this publication in new window or tab >>Evolving 5G: ANIARA, an edge-cloud perspective
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2021 (English)In: Proceedings of the 18th ACM International Conference on Computing Frontiers 2021, CF 2021, Association for Computing Machinery, Inc , 2021, p. 206-207Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2021
Keywords
AI, container tech, edge comp, energy metering, orchestration, Containers, Manufacture, EDGE architectures, Edge clouds, Efficient power, Network edges, Service flexibility, Smart manufacturing, Telecom operators, Telecom systems, 5G mobile communication systems
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-53472 (URN)10.1145/3457388.3458622 (DOI)2-s2.0-85106011241 (Scopus ID)9781450384049 (ISBN)
Conference
18th ACM International Conference on Computing Frontiers 2021, CF 2021, 11 May 2021 through 13 May 2021
Available from: 2021-06-14 Created: 2021-06-14 Last updated: 2023-05-22Bibliographically approved
Marsh, I., Paladi, N., Abrahamsson, H., Gustafsson, J., Sjöberg, J., Johnsson, A., . . . Amiribesheli, M. (2021). Evolving 5G: ANIARA, an edge-cloud perspective. In: CF '21: Proceedings of the 18th ACM International Conference on Computing FrontiersMay 2021: . Paper presented at CF '21: 18th ACM International Conference on Computing FrontiersMay 2021 (pp. 206-207). Association for Computing Machinery
Open this publication in new window or tab >>Evolving 5G: ANIARA, an edge-cloud perspective
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2021 (English)In: CF '21: Proceedings of the 18th ACM International Conference on Computing FrontiersMay 2021, Association for Computing Machinery , 2021, p. 206-207Conference paper, Published paper (Refereed)
Abstract [en]

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).

Place, publisher, year, edition, pages
Association for Computing Machinery, 2021
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-58315 (URN)10.1145/3457388.3458622 (DOI)
Conference
CF '21: 18th ACM International Conference on Computing FrontiersMay 2021
Available from: 2022-01-24 Created: 2022-01-24 Last updated: 2023-05-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9759-5594

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