Tina Stark
Forsknings-och utvecklingsingenjör
Contact Tina
Smart integration of electricity grid, micro grids and Data center
01 June 2022, 12:58
With machine learning and Artificial Intelligence, it is possible to reduce the operation cost of a data center microgrid and with a variable electricity pricing the use of renewable electricity could be encouraged.
The increasing growth of the ICT-sector when introducing IoT and 5G has made data center a part of the industrial ecology. Data center is a power demanding industry and is using about 1-2% of the global electricity production, where this project aimed to study how the electricity pricing could affect the data center operation. To do that a data center at RISE ICE is studied containing a microgrid with a controller for the solar panels and battery storage, a chiller with free cooling possibility and a cold thermal storage. This was done by first creating a model of the data center microgrid which is the base for operation optimization and self-optimization by using “reinforcement learning”, as a final step the model is used for evaluation of different power and energy tariffs.
The data center microgrid was modelled in MATLAB and Simulink with a resolution of 1 hour. The microgrid operation was optimized by using linear programing, grid search, brute force, and system identification. To analyze the pricing of electricity different tariffs were applied, simple tariff, time tariff, power tariff and time and power tariff.
The results shows that tariffs are a good way to affect the operation of the data center to make use of both grid and self-produced renewable power, by this a microgrid would influence the power grid and increase its utilization. With a variable electricity price, the use of batteries could be encouraged. A combination of a variable energy fee and power fee is good in order to make operation optimization feasible and still reduce peak power load. The optimization shows that it is possible to reduce the data center operation cost and by that recoup some of the investments without reducing the battery lifetime. A self-optimization algorithm can succeed in finding the optimal control plan for reducing the operational costs within a specified time horizon.
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