Abstract
Aiming at the carbon peaking and carbon neutrality goals, the water resource and energy management of internet data center involves many aspects, including the interaction among internet data center, power grid, water network and heat consumer. As an important demand response resource for the power grid, the interaction between both can improve the flexibility of the power grid, and ensure the power supply reliability and operation economy of internet data center. Existing references assume that regional integrated energy system can obtain all data information and scheduling permissions of internet data center, ignore the autonomous decision-making ability of both, resulting in its application has some constraints. The operation pattern switchover of internet data center and data allocation among multiple internet data center s are ignored, it is not possible to fully play the flexibility characteristics of internet data center. The study of interaction between internet data center and power grid simplifies the power grid as an ideal power source, ignoring its operational constraints. Internet data center configured with renewable energy, electric energy storage device and water-cooling system is taken as one of the research objects in this paper, and regional integrated energy system formed by the coupling of distribution network and water network as the other. The water-energy coupling and cooperative operation between above two are studied for solving the above problem. The proposed method has been validated in the demonstration project of the “East Data and West Computing” big data industrial park in Qingyang, Gansu, China. The results of example show that water-energy demand response and waste heat reuse can help to reduce the operational cost of internet data center, improve scheduling flexibility of regional integrated energy system, and enhance the capacity of renewable energy utilization. The operational cost of internet data center and regional integrated energy system is sensitive to emergency capacity of electric energy storage device, cost coefficient of the power demand response of electric energy storage device and water demand response of reservoir, data load flexible scheduling strategy, which is consistent with the operational status of the demonstration project.
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