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Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of its rising energy consumption. In this paper, a Lyapunov optimization-based online algorithm is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated ADMM algorithm is developed with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show the effectiveness of the proposed method and its advantages over the existing methods., 27 pages, 10 figures

As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Meanwhile, thanks to the flexibility of EV charging, vehicle-to-grid (V2G) interaction has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity, large number, and private information. Hence, it is the aggregator that interacts with the grid on behalf of EVs by characterizing their aggregate flexibility. In this paper, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We prove that the charging time delays for EVs always meet the requirement even if they are not considered explicitly. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method., 12 pages, 13 figures

SiC x thin films with silicon quantum dots(QDs) were prepared by plasma enhanced chemical vapor deposition (PECVD) and thermal annealing. Transmission electron microscopy (TEM) observation shows that a large number of silicon QDs growing in SiC x films. Memory structure with double barrier which consist of SiC x thin films embedded in silicon QDs were prepared. TEM observations shows that the above process successfully prepared Si 3 N 4 /SiC x /Si-QDs/SiC x /SiO 2 double-barrier memory structure. The programming mechanism of double-barrier silicon quantum dot memory was analyzed theoretically in consideration of coulomb blockade effects and quantum confinement effect of silicon quantum QDs. The threshold voltage shift model of double-barrier structure was established. Simulation of that mode shows that threshold voltage shift of double barrier memory is larger than a single barrier memory and double-barrier memory programming faster too. C - V characteristic tests of storage structure shows that double-barrier structure has good storage carrier storage effect, getting about 10 V's the storage window when the scan voltage is ±12 V.