网络物理能源系统中调度高峰负荷优化的实时网络/物理相互作用

Daniele De Martini , Guido Benetti , Tullio Facchinetti
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引用次数: 0

摘要

本文旨在通过使用一种受实时计算领域启发的调度技术,降低由许多负载组成的网络物理能源系统(CPES)的功耗;电力负载协调通过避免不必要的负载并发启动,从而限制峰值负载,保证整个系统更高效地运行。我们将电力负载本身表示为 "物理 "组件,将协调它们的计算设备表示为 "网络 "组件,并将网络域和物理域中的操作关系正式推导为每个组件上执行的计划之间的相互作用:实际上,负载的计划--通过结合二维分仓包装和最佳多处理器实时调度算法生成--影响着专门用于激活/停用负载本身的处理任务的时间安排。我们还考虑了不可调度的负载,引入了应对此类负载的策略。数值模拟和实验证实了所提出的峰值负载降低方法的良好性能。在这种情况下使用实时调度,通过限制同时活动的并发负载数量,提供了内在的资源优化,从而直接降低了总体峰值负载;此外,由于算法的计算复杂度有限,它可以扩展到大型系统,克服了普通优化方法的可扩展性问题。数值模拟和实验证实了所提出的峰值负载降低方法的良好性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber–Physical Energy Systems

This paper is about reducing the power consumption of Cyber–Physical Energy Systems (CPESs) composed of many loads through the usage of a scheduling technique inspired by the real-time-computing domain; the electric-load coordination guarantees a more efficient operation of the entire system by avoiding unnecessary concurrent activation of loads and thus limiting the peak load. We represent the power loads themselves as “physical” components and the computing devices that coordinate them as “cyber” components and formally derive the relationship between the operations in cyber and physical domains as the interplay between the schedules enforced on each component: indeed, the schedule of the loads – generated by combining a two-dimensional bin-packing and an optimal multi-processor real-time scheduling algorithm – influences the timing of the processing tasks that are dedicated to the activation/deactivation of loads themselves. We also consider non-schedulable loads by introducing a policy to cope with the presence of such loads. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method. The usage of real-time scheduling in this context provides inherent resource optimisation by limiting the number of concurrent loads that are active at the same time, thus directly reducing the overall peak load; moreover, thanks to the limited computational complexity of the algorithms, it scales to large systems, overcoming the scalability issues of common optimisation methods. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method.

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