“多站融合”背景下边缘数据中心运行优化研究

随着泛在电力物联网的发展,传统通信技术与计算平台已逐渐无法适应庞大的数据传输与计算规模,5G通信技术与边缘计算将成为支撑泛在电力物联网建设的中坚力量。“多站融合”模式的提出为边缘数据中心的运营提供了新的方向,多站一体化运行方法有待深入研究。针对边缘数据中心的数据迁移方法以及“多站融合”模式下各功能站协同优化问题,文章设计了涵盖数据中心、储能电站等功能站的融合站运行架构,构建了数据中心负荷模型、储能电站数学模型,并基于混合整数非线性规划方法,建立了以最小化年总成本为目标的融合站协同优化与数据迁移调度模型。通过模型的优化求解,确定了最佳数据迁移调度策略与储能电站最优运行方案。利用案例分别对数据迁移策略与储能电站的作用进行仿真分析,通过计算,采用数据迁移策略后,典型日运行成本降低了2.38%,年总成本降低了1.36%;发挥储能电站削峰填谷作用后,典型日运行成本进一步降低了8.34%,年总成本进一步降低了4.67%。结果表明,在满足时延约束情况下将数据负荷按一定策略进行分配可以实现各边缘数据中心互补调度,达到降低运行成本的效果,结合分时电价政策,利用储能电站参与日常电力调度,可以进一步将峰时负荷转移至谷时,从而显著降低运行成本。

Research on the Operation Optimization of Edge Data Center under the Background of "Multi-Station Integration "

With the development of ubiquitous power Internet of things (IoT), traditional communication technology and computing platform have been unable to adapt to the huge scale of data transmission and computing. 5G communication technology and edge computing will become the backbone force supporting the construction of the ubiquitous power Internet of things. The "multi-station integration" mode provides a new direction for the operation of edge data center, and the operation method of multi-station integration needs to be further studied. In order to study the data migration method of edge data center and the collaborative optimization problem of each function station under the mode of "multi-station integration", this paper designed a fusion station operation architecture covering data center and energy storage power station, constructed the load model of data center and the mathematical model of energy storage station, and based on the mixed integer nonlinear programming method, established the collaborative optimization and data migration scheduling model of integrate station with the goal of minimizing the total annual cost. Through solving the optimization model, the optimal data migration scheduling strategy and the optimal operation scheme of energy storage power station were determined. The simulation analysis of data migration strategy and the role of energy storage power station was carried out by case study. The calculation results showed that, the typical daily operation cost was reduced by 2.38% and the annual total cost was reduced by 1.36% after the implementation of the data migration strategy. After the energy storage power station was used to cut peak and fill valley, the typical daily operation cost was further reduced by 8.34% and the annual total cost was further reduced by 4.67%. The analysis results showed that the operation cost could be reduced when the data load was allocated according to a certain strategy under the condition of meeting the delay constraint. Combined with the time-of-use electric rate structure, the energy storage power station participating in the power dispatching could further transfer the peak load to the valley load, thus significantly reducing the operation cost.