Flexibility Management of Data Centers to Provide Energy Services in the Smart Grid

In this paper, we address the problem of Data Centers (DCs) energy efficiency considering their integration into the electrical and thermal grids by emphasizing the role of the DC Digital Twin model in DC flexibility management. Due to their high digitization and controllable energy systems, the DCs can act as flexible assets, being able to dynamically adapt their energy profiles and valuable energy services. We present a flexibility management solution that is using a Digital Twin model of DC systems to determine action plans for shifting energy load. DC monitored data is acquired by integration with existing DC infrastructure management (DCIM) while energy predictions are computed for DC energy demand, energy flexibility, and heat generation. The flexibility optimization plans for DC operation are determined and enforced after DC manager validation via DCIM integration. Five energy services are identified as suitable to be provided by the DC with the help of described flexibility management solution: energy trading for increasing profit, grid congestion management by decreasing DC energy demand, scheduling by increasing DC energy demand to consume as much as possible the renewable available in the local grid, power factor compensation and sell heat on demand.

[1]  Yiran Chen,et al.  A Survey of Accelerator Architectures for Deep Neural Networks , 2020 .

[2]  Zong Woo Geem,et al.  Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers , 2020, Sustainability.

[3]  Paolo Arcaini,et al.  Modeling and Analyzing MAPE-K Feedback Loops for Self-Adaptation , 2015, 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems.

[4]  Arockia Ranjini A,et al.  Virtual Machine Consolidation Framework for Energy and Performance Efficient Cloud Data Centers , 2019, 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN).

[5]  Jukka Manner,et al.  Utilizing data center waste heat in district heating – Impacts on energy efficiency and prospects for low-temperature district heating networks , 2017 .

[6]  Birgitte Bak-Jensen,et al.  Opportunities and challenges of demand response in active distribution networks , 2018 .

[7]  Lava Bhargava,et al.  Dynamic Voltage Frequency Scaling in Multi-core Systems using Adaptive Regression Model , 2020, 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).

[8]  Luca Castellazzi,et al.  Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency , 2017 .

[9]  Ariel Oleksiak,et al.  Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model , 2019, Energies.

[10]  Florin Pop,et al.  A System of Systems approach for data centers optimization and integration into smart energy grids , 2017, Future Gener. Comput. Syst..

[11]  Marcel Antal,et al.  Transforming Data Centers in Active Thermal Energy Players in Nearby Neighborhoods , 2018 .

[12]  Atif Manzoor,et al.  Minimizing Thermal Stress for Data Center Servers through Thermal-Aware Relocation , 2014, TheScientificWorldJournal.

[13]  Morten Boje Blarke,et al.  Thermal battery with CO2 compression heat pump: Techno-economic optimization of a high-efficiency Smart Grid option for buildings , 2012 .

[14]  N. Jones.  How to stop data centres from gobbling up the world’s electricity , 2018, Nature.

[15]  Sanna Syri,et al.  Future views on waste heat utilization - case of data centers in Northern Europe , 2016 .

[16]  Qinghui Tang,et al.  Sensor-Based Fast Thermal Evaluation Model For Energy Efficient High-Performance Datacenters , 2006, 2006 Fourth International Conference on Intelligent Sensing and Information Processing.

[17]  Graeme Maidment,et al.  Using data centres for combined heating and cooling: An investigation for London , 2016 .