Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden.

Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden. 

Low latency requirements are expected to increase with 5G telecommunications driving data and compute to EDGE data centers located in cities near to end users. This article presents a testbed for such data centers that has been built at RISE ICE Datacenter in northern Sweden in order to perform full stack experiments on load balancing, cooling, micro-grid interactions and the use of renewable energy sources. This system is described with details on both hardware components and software implementations used for data collection and control. A use case for off-grid operation is presented to demonstrate how the test lab can be used for experiments on edge data center design, control and autonomous operation. © 2020 Author.

As our world becomes increasingly digitalized, data centers as operational bases for these technologies lead to a consequent increased release of excess heat into the surrounding environment. This paper studies the challenges and opportunities of industrial symbiosis between data centers’ excess heat and greenhouse farming, specifically utilizing the north of Sweden as a case study region. The region was selected in a bid to tackle the urgent urban issue of self-sufficiency in local food production. A synergetic approach towards engaging stakeholders from different sectors is presented through a mix of qualitative and quantitative methods to facilitate resilient data-center-enabled food production. The paper delivers on possible future solutions on implementing resource efficiency in subarctic regions. © 2022 by the authors. 

The design of intelligent algorithms used for device monitoring and control can be costly and is an investment that must be protected against reverse engineering by competitors. An algorithm can be safeguarded by running remotely from the cloud instead of locally on the equipment hardware. However, such a setup requires that sensitive data is sent from the device to the cloud. Fully Homomorphic Encryption (FHE) is an emerging technology that offers a solution to this problem since it enables computation on encrypted data. A cloud service using FHE can protect its proprietary algorithms while simultaneously offering customer data confidentiality. The computational overhead for the technology is, however, still very high. This work reports on a practical investigation of using FHE for data center remote control problems: What applications are feasible today? And at what cost?

Datacentres are becoming a sizable part of the energy system and are one of the biggest consumers of the energy grid. The so-called “Green Datacentre” is capable of not only consuming but also producing power, thus becoming an important kind of prosumers in the electric grid. Green datacentres consist of a microgrid with a backup uninterrupted power supply and renewable generation, e.g., using photovoltaic panels. As such, datacentres could realistically be important participants in demand/response applications. However, this requires reconsidering their currently rigid control and automation systems and the use of simulation models for online estimation of the control actions impact. This paper presents such a microgrid simulation model modelled after a real edge datacentre. A case study consumption scenario is presented for the purpose of validating the developed microgrid model against data traces collected from the green edge datacentre. Both simulation and real-time validation tests are performed to validate the accuracy of the datacentre model. Then the model is connected to the automation environment to be used for the online impact estimation and virtual commissioning purposes.

Lågtempererad restvärme är i dag en underutnyttjad resurs i samhället trots stor tillgång. I den här förstudien har växthus studerats, dvs system med stort behov av värmeenergi under delar av året. Projektet studerade teoretiskt ett växthuskoncept som använder lågvärdiga (30 – 40 °C) externa och interna värmekällor för uppvärmning, samt nyttjandet av frikyla (daggpunkt ca 25–10 °C) via termiskt lager för avfuktning. Syftet med förstudien är att skapa förutsättningar för hållbar och konkurrenskraftig växthusodling i Sverige genom synergier mellan olika branscher och tekniker. Som möjliggörare föreslås ett nytt koncept, som utvecklas för växthusodling, där energi- och odlingseffektivitet samspelar. Genom att utvärdera konceptet avser förstudien besvara frågeställningar om hur energieffektiv teknik dels kan minska energibehovet för växthus, dels använda sig av lågtempererade värmekällor, i kombination med värmeåtervinning, samtidigt som klimatet i växthuset styrs så odlingskapaciteten och CO2-gödsling förbättras. Resultatet visar att potentialen är mycket årstidsberoende och mycket beroende av hur stor värmekällan från växtbelysning är. Värmeåtervinning av växthusets interna värme kan minska värmeenergibehovet med ca 85% och växthuset kan använda lågtempererad värmekälla på ca 30–40 °C. Ytterligare resultat är att produktionen även kan ökas med ca 30–50% (beroende på årstid) med effektiv CO2-gödning och bättre klimat för växterna.

Currently, most of the existing data centers use chilled air to remove the heat produced by the servers. However, liquids have generally better heat dissipation capabilities than air, thus liquid cooling systems are expected to become a standard choice in future data centers. Designing and managing these cooling units benefit from having control-oriented models that can accurately describe the thermal status of both the coolant and the heat sources. This manuscript derives a control-oriented model of liquid immersion cooling systems, i.e., systems where servers are immersed in a dielectric fluid having good heat transfer properties. More specifically, we derive a general lumped-parameters gray box dynamical model that mimics energy and mass transfer phenomena that occur between the main components of the system. The proposed model is validated against experimental data gathered during the operation of a proof-of-concept immersion cooling unit, showing good approximation capabilities. 

Liquid cooling systems have better heat dissipation capabilities than air based ones, and are expected to become a standard choice in future data centers, due to the ever increasing power density and heat rejection needs of the compute infrastructure. A convenient side-effect of implementing liquid cooling is that it facilitates the efficient recovery of the heat waste. However designing and managing these heat recovery infrastructures benefit from having control-oriented models that can accurately describe how different operating conditions of the to-be-cooled heat sources will affect the thermal status of the coolant. The aim of this manuscript is to derive control-oriented models of liquid immersion cooling systems, i.e., systems where the compute infrastructure is immersed in a vessel filled with dielectric fluid. More specifically we derive, starting from physical interpretations, a general lumped-parameters gray box dynamical model that has-as inputs-the electrical consumption of the heat sources and the working point of the heat recovery system, and has-as outputs-the temperature distribution of the coolant in the most relevant points of the system. Beyond proposing this modelling methodology we also validate the generalization capabilities of the obtainable models. In specific, we test the achievable statistical performances in a field case, plus compare with the ones of classical black box system identification strategies. We thus report that in the considered field case our gray box model reached a fit index of 91.08% when simulating test sets, while the best black box model we have been able to identify reached (on the same test sets) fit indexes of only 72.56%.

This paper examines the possibility of increasing Northern Sweden's degree of self-sufficiency in food supply, at the 65th latitude, by using a data center as a heating source for greenhouse production. A dynamic building energy simulation software was used to compute both the hourly exhaust air output from a 1 MW data center for one year and the corresponding heating demand for two different greenhouse sizes, 2000 m2 and 10 000 m2, and two different production scenarios. Partial year production, 1 Mars – 15 October, without grow lights and full-year production with grow lights. The study showed that 5.5–30.5% of the electrical input to a 1 MW data center could be recovered. The 2000 m2 greenhouse could operate almost entirely, 89.7–97.9%, on excess heat while only 50.0–61.5% of the 10 000 m2 greenhouse heating demand could be met for full- and partial-year production, respectively. Furthermore, it is concluded that the 10 000 m2 greenhouse with full year production was the most prominent case and would cost-effectively yield 7.6% of northern Sweden's vegetable self-sufficiency.

This paper examines the opportunities to reuse excess heat from direct free air-cooled data centres without incorporating heat pumps to upgrade the heat. The operation of a data centre in northern Sweden, Luleå, was simulated for a year. It was established that heat losses through the thermal envelope and from the humidification of the cooling airflow influenced the momentary energy reuse factor, iERF, with up to 7%. However, for the annual energy reuse factor, ERF, the heat losses could be neglected since they annually contributed to an error of less than 1%. It was shown that the ideal heat reuse temperature in Luleå was 13, 17, and 18 °C with an exhaust temperature of 30, 40 and 50 °C. The resulting ERF was 0.50, 0.59 and 0.66, meaning that a higher exhaust temperature resulted in potentially higher heat reuse. It could also be seen that raising the exhaust temperature lowered the power usage effectiveness, PUE, due to more efficient cooling. Using heat reuse applications with different heat reuse temperatures closer to the monthly average instead of an ideal heat reuse temperature for the whole year improved the ERF further. The improvement was 11–31% where a lower exhaust temperature meant a higher relative improvement.

Simulation tools for thermal management of datacenters help to improve layout of new builds or analyse thermalproblems in existing data centers. The development of LBMon remote GPUs as an approach for such simulations is discussedmaking use of VirtualGL and prioritised multi-threadedimplementations of an existing LBM code. The simulation isconfigured to model an existing and highly monitored test datacenter. Steady-state root mean square averages of measured andsimulated temperatures are compared showing good agreement.The full capability of this simulation approach is demonstratedwhen comparing rack temperatures against a time varyingworkload, which employs time-dependent boundary conditions.

Since Sweden joined the EU in 1995, importing food became easier and cheaper, leading to certain parts of the country, such as Norrbotten, becoming highly dependent on imports. This dependency, along with the inherent environmental impact of imports, could be significantly reduced by local farming. The environmental emissions originating from animal farming could be lowered even further by substituting the highly polluting soybean feed with, e.g., insect feed. This study examines the farming of mealworms, utilizing excess heat from a data center, part of a growing industry in Norrbotten county, as a means of alternative feedstock for animal production and a case study for industrial symbiosis. This industrial symbiosis project is in line with the EU’s incentive to use other sources of protein and thus lower the EU’s reliance on the import of foreign protein. Three different feeding approaches are tested, in a room heated with data center excess heat of 30 °C and at room temperature of about 20 °C. After the adult mealworms were harvested, a sample was taken to analyze their nutritional values. The results show that protein, lipid, and fiber content is 19,1 g, 12,6 g, and 2,7 g per 100 g, respectively. All amino acids except tryptophan were detected. This project concludes that it is possible to reach full-grown mealworms in about 8 weeks, which is about half the time stated in the literature. 

Arctiq-DC is a InterReg North funded project with a total budget of about €1´430´000 where 9 partners from Sweden and Finland are collaborating: Oulu University, Oulun Data Center, Aurora Data Center, SFTec, Xarepo, Hushållningssällskapet, Älvsbyns municipality, Hydro 66 and RISE Research Institutes of Sweden as coordinator. The project duration is almost three years and consist of six main activities where the fourth is about cooling and heat reuse from data center. This report describes the trails that were made for evaluating data center excess heat as heat source for biomass drying.

Aiming at addressing environmental challenges, large data centers, such as Facebook, Google, and Yahoo, are increasing share of green power in their daily energy consumption. Such trends drive research into new directions, e.g., sustainable data centers. The research often relies on expressive models that provide sufficient details, however, practical to re-use and expand. There is a lack of available data center models that capture internal operating states of the facility from the CPU to the cooling tower. It is a challenge to develop a model that allows to describe complete data center of any scale including its connection to the grid. This paper proposes such a model building on the existing work. The challenge was to put the pieces of data center together and model behavior of each element so that interdependencies between components and parameters and operating states are captured correctly and in sufficient details. The proposed model was used in the project 'Data center microgrid integration' and proven to be adequate and important to support such study.

Data centers are important players in the energy infrastructure. Aiming at addressing environmental challenges, large data centers such as Facebook, Google, Yahoo, etc., are increasing share of green power in their daily energy consumption. Such trends drive research into new directions, e.g. sustainable data centers. The research often relies on expressive models that provides sufficient details however practical to re-use and expand. There is a lack of available data center models that capture dynamics of the facility from the CPU to the cooling tower. It is a challenge to develop a model that allows to describe complete data center of any scale including its connection to the grid. This paper proposes such a model building on existing work. The challenge was to put the pieces of data center together and describe dynamics of each element so that interdependencies between components and parameters are captured correctly and in sufficient details. The proposed model was used in the project “Data center microgrid integration” and proven to be adequate and important to support such study.