Communication Network Resource Allocation for Electric Vehicle Aggregation and Control

SU Huiling; ZHANG Tingquan; LIU Ying; LU Xiaoquan; DU Kejun; ZHANG Chi

doi:10.19725/j.cnki.1007-2322.2024.0193

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Communication Network Resource Allocation for Electric Vehicle Aggregation and Control

更新时间：2026-10-19

- Communication Network Resource Allocation for Electric Vehicle Aggregation and Control
- Vol. 43, Issue 5, Pages: 1053-1064(2026)
- 作者机构：
  
  国网江苏省电力有限公司营销服务中心,江苏省,南京市,210019
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0193
  CLC： 5
- Published：2026
- 稿件说明：
移动端阅览
SU Huiling, ZHANG Tingquan, LIU Ying, et al. Communication Network Resource Allocation for Electric Vehicle Aggregation and Control[J]. 2026, 43(5): 1053-1064.
DOI：

SU Huiling, ZHANG Tingquan, LIU Ying, et al. Communication Network Resource Allocation for Electric Vehicle Aggregation and Control[J]. 2026, 43(5): 1053-1064. DOI： 10.19725/j.cnki.1007-2322.2024.0193.

摘要

随着清洁能源规模化接入电网，对电动汽车进行聚合控制，已成为维持电力供需平衡的关键手段。电动汽车聚合过程产生大量信息传输业务，其严格的实时与准确交互需求，对现有网络通信性能构成了严峻挑战。因此，该文提出面向电动汽车聚合控制过程中信息交互的“中心云平台–边缘服务器–电动汽车用户”通信网络架构，重点分析各层业务处理功能与信息交互需求的差异性，分别建立面向需求响应过程中各层的信息传输时效性与信息传输准确性的效用函数模型，基于效用函数构建信道及功率资源分配模型。基于蜣螂优化算法、双边匹配以及改进注水算法，实现信道和功率分配。为了扩大种群迭代的随机性，在蜣螂优化算法中引入正弦策略，结合双边匹配和改进注水算法，使模型能够快速收敛至最大效用值，实现车网互动过程中信道及功率资源的分配。通过对比算法的仿真验证表明，该算法在电动汽车分层聚合控制场景下能有效提升传输业务的整体信息传输效用。

Abstract

In the context of large-scale clean energy access to the grid

the aggregated regulation of electric vehicles is an important way to ensure the balance of electricity supply and demand. The aggregation process of electric vehicles generates a large number of information transmission services

and its strict real-time and accurate interaction requirements pose a severe challenge to the existing network communication performance. Therefore

this paper proposes a communication network architecture of "central cloud platform-edge server-EV user" for information exchange in the process of EV aggregation and regulation

focuses on analyzing the differences in business processing functions and information exchange requirements at each layer

and establishes utility function models for the timeliness and accuracy of information transmission at each layer in the process of demand response. A channel and power resource allocation model is constructed based on the utility function. Channel and power allocation are realized using the dung beetle optimization algorithm

bilateral matching

and improved water-filling algorithm. To expand the randomness of population iteration

a sinusoidal strategy is introduced into the dung beetle optimization algorithm

combined with bilateral matching and an improved water injection algorithm

enabling the model to quickly converge to the maximum utility value and realize the allocation of channel and power resources during the vehicle network interaction process. The simulation results indicate that the algorithm is capable of effectively improving the overall information transmission efficiency of transmission services in the hierarchical aggregation control scenario for electric vehicles.

关键词

Keywords

references

WU X, YOU L, WU R, et al . Management and control of load clusters for ancillary services using internet of electric loads based on cloud-edge-end distributed computing[J ] . IEEE Internet of Things Journal, 2022, 9(19): 18267−18279.

BAI Ying, LIU Xiaofeng, CHEN Xueying. Dynamics analysis on decision-making of load aggregator for day-ahead demand response market bidding[J]. Automation of Electric Power Systems, 2022, 46(24): 95−103(in Chinese).

YAO Lijuan, CAI Ruitian, QIAN Jiang, et al . Aggregation and control model construction of hybrid load system for supply and demand balance in low-carbon zone[J ] . Power System Technology, 2023, 47(8): 3153−3163(in Chinese).

CHEN Hongyu, ZHANG Ye, GUAN Yan, et al . Research on clearing optimization model of power spot market incorporating generation, network, load and storage benefit improvement[J ] . Electrical Measurement Instrumentation, 2022, 59(5): 50−57(in Chinese).

KONG Yueping, YANG Shihai, DUAN Meimei, et al . Optimal decision-making for electric vehicle aggregator based on a hybrid action reinforcement learning algorithm[J ] . Computer Engineering, 2024, 50(10): 418−428(in Chinese).

ZHAO Wenwei, LIN Bing, LU Yu, et al . Energy sche duling strategy for photovoltaic storage charging station for vehicle charging reservation[J ] . Computer Engineering, 2023, 49(12): 262−273+281(in Chinese).

WU X, JIANG X, YAO L, et al . Heterogeneous aggregation and control modeling for electric vehicles with random charging behaviors[J ] . IEEE Transactions on Sustainable Energy, 2023, 14(1): 525−536.

GONG Xin, SU Yu, ZHANG Xiaofeng, et al . Optimal charging scheduling for electric vehicle aggregator considering incentive demand response[J ] . Modern Electric Power, 2019, 36(6): 16−22(in Chinese).

WANG Jing, XING Haijun, WANG Huaxin, et al . Optimal scheduling of integrated energy system considering integration of electric vehicles and load aggregators[J ] . Journal of Shanghai Jiaotong University, 2023, 57(7): 814−823(in Chinese).

VAGROPOULOS S L, BAKIRTZIS A G. Optimal bidding strategy for electric vehicle aggregators in electricity markets[J]. IEEE Transactions on Power Systems, 2013, 28(4): 4031−4041.

LIU Lin, QI Bing, LI Bin, et al . Requirements and developing trends of electric power communication network for new services in electric internet of things[J ] . Power System Technology, 2020, 44(8): 3114−3128(in Chinese).

DONG Chaowu, XIAO Zhihong, XIN Peizhe, et al . Evaluation method of multiple service and multi-mode communication adaptability of new distribution networks[J ] . Electric Power Construction, 2024, 45(1): 1−12(in Chinese).

CHEN Xiaomin, WANG Gang, WANG Longjun. Ordered charging strategy of electric vehicle based on vehicle networking framework[J]. Modern Electric Power, 2018, 35(4): 1−7(in Chinese).

LIU Hong, YAN Jun, GE Shaoyun, et al . Dynamic response of electric vehicle and fast charging stations considering multi-vehicle interaction[J ] . Proceedings of the CSEE, 2020, 40(20): 6455−6467(in Chinese).

LI Q, FENG Z, LI W, et al . Joint spatial and temporal spectrum sharing for demand response management in cognitive radio enabled smart grid[J ] . IEEE Transactions on Smart Grid, 2014, 5(4): 1993−2001.

ZHENG L, LU N, CAI L. Reliable wireless communication networks for demand response control[J]. IEEE Transactions on Smart Grid, 2013, 4(1): 133−140.

KONG P Y. Radio resource allocation scheme for reliable demand response management using D2D communications in smart grid[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2417−2426.

ZHANG Wei, WANG Dan. Real-time demand response scheduling strategy for electric vehicles based on cloud edge collaboration[J]. Power System Technology, 2022, 46(4): 1447−1456(in Chinese).

LIU Jianing, SU Zhuo, WANG Ke, et al . Elastic configuration method of cloud-edge collaborative resources for distribution internet of things automation business[J ] . Electric Power Construction, 2023, 44(8): 118−127(in Chinese).

WANG Haizhou. Research on resource allocation algorithm oriented to differentiated service requirements of the UAV swarm[D]. Beijing: Beijing University of Posts and Telecommunications, 2021(in Chinese).

ZHAO Wentao, WANG Shaowei. Joint subchannel and power allocation in multiuser OFDM systems with minimal rate constraints[J]. International Journal of Communication Systems, 2014, 27(1): 1−12.

ZHOU Jiang, CHEN Yang, YU Lingyun. Dynamic bandwidth allocation algorithm for passive optical networks based on utility function[J]. Computer and Modernization, 2023(4): 106−110(in Chinese).

LIU Zeyuan, LIU Junyong, LIU Jichun, et al . Profit allocation method of coalition including multi-energy based on Nash-Harsanyi game theory[J ] . Electrical Measurement Instrumentation, 2025, 62(1): 51−60(in Chinese).

JI Jianling, LIU Hongli. Application of FH-OFDM technology in low voltage power line communication[J]. Electric Power Construction, 2011, 32(08): 1−4(in Chinese).

XU Wenqi. Research on carrier communication network of power distribution system based on OFDM Technology[D]. Beijing: North China Electric Power University, 2018(in Chinese).

WANG Qing, JING Zhen, LI Congcong, et al . A novel algorithm of physical layer resource allocation for broadband power line carrier communication[J ] . Electrical Measurement Instrumentation, 2021, 58(7): 124−129(in Chinese).

CHEN M, YANG Z, SAAD W, et al . A joint learning and communications framework for federated learning over wireless networks[J ] . IEEE Transactions on Wireless Communication, 2021, 20(1): 269−283.

SU Su, WANG Jianxiang, WANG Lei, et al . Guidance strategy for electric vehicle charging based on dynamic huff model and bilateral matching[J ] . Automation of Electric Power Systems, 2024, 48(07): 181−189(in Chine se).

LOU Gewei, ZHENG Yonghuang, CHEN Jun, et al . Improved dung beetle optimization algorithm by hybrid multi-strategy[J ] . Computer Engineering and Applications, 2024, 60(24): 97−109(in Chinese).

XUE J K, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2022, 79(7): 7305−7336.

Views

241

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Analysis of Latency Characteristics of Wind Farm Communication Network Based on Time-Sensitive Networking

An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities

Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics

Comprehensive Evaluation of Power Quality Based on LSM-GA and Improved VFS

A Two-stage Consensus-empowered Measurement Data Sharing Approach for Distribution Networks Based on Lightweight Fuzzy Petri Nets

Related Author

Huisheng GAO

Wensi YU

Dongsheng HAN

HU Ping

HAN Jinglin

CHEN Zhiyong

LI Xiang

LIAN Haoran

Related Institution

Department of Electronic and Communication Engineering, North China Electric Power University

国网河北省电力有限公司

国网河北省电力有限公司经济技术研究院

华北电力大学电气与电子工程学院, 北京市昌平区

国网河北省电力有限公司雄安新区供电公司, 河北省雄安新区

AI问答

⁰