Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation

Yi LIU; Xin WU

doi:10.11959/j.issn.1000-0801.2024029

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Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation

Research and Development | 更新时间：2024-06-05

- Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation
- Telecommunications Science Vol. 40, Issue 2, Pages: 124-140(2024)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2024029
  CLC： TN929.5
- Published Online：2024-02，
  
  Published：20 February 2024
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Yi LIU, Xin WU. Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation[J]. Telecommunications science, 2024, 40(2): 124-140.
DOI：

Yi LIU, Xin WU. Cloud edge end network resource allocation for thermostatically controlled load aggregation regulation[J]. Telecommunications science, 2024, 40(2): 124-140. DOI： 10.11959/j.issn.1000-0801.2024029.

摘要

温控负荷是以空调和电热水器等为一类控制温度调节的柔性负荷，作为一类重要的需求侧资源，对负荷集群进行灵活的聚合调控可以充分调动清洁能源消纳能力，保障电网供需平衡。由于温控负荷常见于商业写字楼及居民区内，可对其采用较为稳定的控制与传输方式，故引入高效的分层分级传输网络，实现负荷与电网之间的数据传输与信息交互，灵活、实时、精准地利用负荷集群的调节潜力。首先提出了一种“中心云-边缘云-区域控制器-温控负荷”的云边端协同通信组网架构。接着，针对端-边部分，考虑不同聚合控制任务的需求，利用改进的聚类算法对任务进行分类，以减小传输开销。针对边-云协同部分，构建了考虑时延、能耗和误码率的传输开销效用函数，设计了基于稳定匹配和注水算法的子信道资源分配算法，并利用二进制粒子群算法解决了任务上传决策问题。最后，通过仿真验证了本文所提模型的有效性，并进行了对比实验。

Abstract

Thermostatically controlled load is a flexible load that controls temperature regulation

such as air conditioning and electric water heaters.As a crucial demand side resource

flexible aggregation and regulation of load clusters can fully mobilize clean energy consumption capacity and ensure the balance between supply and demand of the power grid.Due to the common occurrence of thermostatically controlled loads in commercial office buildings and residential areas

a relatively stable control and transmission method can be adopted.Therefore

an efficient hierarchical transmission network is introduced to achieve data transmission and information interaction between loads and the power grid

and to flexibly

real-time

and accurately utilize the adjustable potential of load clusters.Firstly

an information interaction architecture of load IoT which structured “central cloud-edge cloud-regional load controller-thermostatically controlled load”was proposed.Then

for the “end edge”part

considering the requirements of different aggregation control tasks

an improved clustering algorithm was used to classify the tasks and reduce transmission overhead.For the “end-side” part

an improved clustering algorithm was used to optimize the transmission distance.For the edge-cloud collaboration part

a subchannel resource allocation algorithm was designed based on stable matching and water injection algorithms.The binary particle swarm optimization algorithm was used to solve the task upload decision problem.Finally

the effectiveness of the proposed model and algorithm is verified through simulation

and comparative experiments are also conducted.

关键词

Keywords

references

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