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基于云边协同的微电网自动控制系统
引用本文:江伟,马光,涂卫平,彭超逸,陈州.基于云边协同的微电网自动控制系统[J].计算机测量与控制,2023,31(12):117-122.
作者姓名:江伟  马光  涂卫平  彭超逸  陈州
作者单位:中国南方电网有限责任公司,,,,南京南瑞继保电气有限公司
基金项目:南方电网公司科技项目(000000KK52200035)
摘    要:微电网及分布式能源的数量、规模快速发展,当前系统无法满足海量相关数据采集、存储、分析计算需求;为适应多微电网控制的高并发、高可靠性的处理要求,提出了基于云边协同的微电网自动控制系统;基于Docker容器技术优化配置云边计算资源;设计了基于云边协同的微电网自动控制系统总体架构,对云边协同功能及主要数据流进行了详细说明;进一步分析了其中微电网控制及能量管理等核心功能及其数据处理需求,提出了云边协同架构下的计算分析任务调度策略,基于带宽资源对迁移任务数据流进行调度,兼顾任务时限要求和服务器的负荷均衡;最后通过运行实例对提出的微电网自动控制系统功能及优越性进行有效性验证,所提系统通过架构、功能和任务调度策略满足了多微电网的控制要求。

关 键 词:云边协同  微电网  Docker  控制  任务调度
收稿时间:2023/2/6 0:00:00
修稿时间:2023/3/6 0:00:00

Microgrid Automatic Control System Based on Cloud Edge Collaboration
Abstract:With the rapid development of the number and scale of microgrid and distributed energy, the current system can not satisfy the requirements of massive related data acquisition, storage, analysis and calculation. In order to meet the processing requirements of high concurrency and high reliability of multi-microgrid control, this paper proposes a microgrid automatic control system based on cloud edge collaboration. Cloud edge computing resources is optimized based on Docker container technology. The structure of microgrid automatic control system is designed based on cloud edge collaboration. And the cloud edge collaboration function and main data flow are described in detail. The core functions such as microgrid control and energy management and their data processing requirements are further analyzed. A computational analysis task scheduling strategy under cloud edge collaborative architecture is proposed. The migration task data flow is scheduled based on bandwidth resources, taking into account the task time limit requirements and server load balance. Finally, the validity of the function and superiority of the proposed microgrid automatic control system is verified by an operational example. The proposed system meets the control requirements of multi-micro-grid through the cloud edge collaboration with proposed structure, function and task scheduling strategy.
Keywords:cloud edge collaboration  microgrid  Docker  control  task scheduling
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