摘要
随着分布式光伏集群系统中越来越多的电力终端发起实时业务需求和电力计算请求,具有传统运行和调度方式的数据采集与计算架构性能受限。为了解决该问题,文章采用移动性边缘云(moving edge cloud,mEC)优化大规模光伏集群终端设备的资源调度。mEC按顺序收集光伏终端设备任务,首先处理紧急任务并返回结果,对于常规监测等非紧急任务,mEC可以收集请求后离开,直到再次到达该设备后返回计算结果。这种请求接收和结果返回的异步性有效解耦严格的延迟要求和资源需求之间的关系。文章将边缘云的移动路线以及任务优先级建模为复杂的混合整数非线性规划问题,并采用迭代更新算法求解优化问题,确定计算资源分配策略。仿真结果表明,该方案能显著降低能耗,节约系统成本。
With more and more power terminals in distributed photovoltaic cluster systems initiating real-time business demands and power computation requests,the performance of data collection and computation architectures with traditional operation and scheduling methods is limited.To solve the problem,this paper adopts moving edge cloud(mEC)to optimise the resource scheduling of terminal devices in large-scale photovoltaic clusters.The mEC collects PV terminal device tasks sequentially,processes urgent tasks first and returns the results.For non-urgent tasks such as normal monitoring,the mEC can collect the request and leave,returning the computation results until it reaches the device again.The asynchronous nature of request reception and result return effectively decouples the relationship between stringent latency requirements and resource demands.We model the movement routes and task priorities of edge server groups as a complex mixed-integer nonlinear programming problem.An iterative update algorithm is used to solve the problem and determine the resource allocation strategy.Simulation results show that the scheme can significantly reduce energy consumption and save system cost.
作者
马梦轩
张冠群
何文昭
宋昕
雍晓峰
李智磊
MA Mengxuan;ZHANG Guanqun;HE Wenzhao;SONG Xin;YONG Xiaofeng;LI Zhilei(State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 750000,Ningxia Hui Autonomous Region,China;Yinchuan Power Supply Company,State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 750000,Ningxia Hui Autonomous Region,China)
出处
《电力信息与通信技术》
2025年第9期61-66,共6页
Electric Power Information and Communication Technology
基金
国网宁夏电力有限公司科技项目“面向整县分布式光伏集群接入的通信组网技术研究”(5229YC220009)。
关键词
异步解耦
移动性边缘云
轨迹优化
能源利用率
asynchronous decoupling
moving edge cloud
trajectory optimization
energy efficiency
