CTS2M: concurrent task scheduling and storage management for residential energy consumers under dynamic energy pricing
CTS2M: concurrent task scheduling and storage management for residential energy consumers under dynamic energy pricing
复制标题
CTS2M：动态能源定价下住宅能源消费者的并发任务调度和存储管理
DOI：
10.1049/iet-cps.2017.0050
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Massoud Pedram
中科院分区：
文献类型：
--
作者：
Ji Li;X. Lin;Shahin Nazarian;Massoud Pedram
动态能源定价政策在智能电网中引入实时反映电力消耗的定价，以激励能源消费者更谨慎地安排用电应用程序（任务），以最大限度地减少电费。随着光伏（PV）发电设施和可控储能系统的可用性，这已成为一个特别有趣的问题。本研究解决了具有光伏和存储系统的住宅能源消费者的并发任务调度和存储管理问题，以最大限度地减少电费。假设一般类型的动态定价场景，其中能源价格既依赖于使用时间又依赖于功率。任务可以支持立即挂起和稍后恢复操作。提出了基于协商的迭代方法。在每次迭代中，所有任务都在固定的存储充电/放电方案下进行拆分和重新调度，然后根据最新的任务调度导出存储控制方案。引入拥塞的概念来逐步调整每个任务的调度，而动态规划则用于寻找最优调度。有效地实现了近乎最优的存储控制算法。实验结果表明，与各种基线方法相比，该算法可以实现高达 60.95% 的总能源成本降低。
Dynamic energy pricing policy introduces real-time power-consumption-reflective pricing in the smart grid in order to incentivise energy consumers to schedule electricity-consuming applications (tasks) more prudently to minimise electric bills. This has become a particularly interesting problem with the availability of photovoltaic (PV) power generation facilities and controllable energy storage systems. This study addresses the problem of concurrent task scheduling and storage management for residential energy consumers with PV and storage systems, in order to minimise the electric bill. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and power dependent. Tasks are allowed to support suspend-now and resume-later operations. A negotiation-based iterative approach has been proposed. In each iteration, all tasks are ripped-up and rescheduled under a fixed storage charging/discharging scheme, and then the storage control scheme is derived based on the latest task scheduling. The concept of congestion is introduced to gradually adjust the schedule of each task, whereas dynamic programming is used to find the optimal schedule. A near-optimal storage control algorithm is effectively implemented. Experimental results demonstrate that the proposed algorithm can achieve up to 60.95% in the total energy cost reduction compared with various baseline methods.