Abstract:High-frequency acquisition services in distribution networks have stringent requirements for real-time response capability. There is a contradiction of performance limitations under the current resource allocation method. Traditional resource allocation methods tend to optimize only one of the communication or sensing parties in isolation, making it difficult to make full use of resources and inefficient data transmission. This seriously affects the real-time monitoring and control operation of the power grid and fault response capability. Recently, some methods have been proposed to collaboratively optimize multidimensional resources, but most of them lack the consideration of association mechanism and collection frequency requirements. Aiming at these problems, this paper proposed a high-frequency acquisition and low-latency interaction method for distribution network data based on sensing-communication integrated, which can reduce the average weighted sum of end-to-end delay and operating costs of the sensing-communication integrated network. First, the network architecture of high-frequency data collection and low-latency interactive for distribution networks based on sensing-communication integrated was designed. On this basis, models for acquisition devices scheduling, sensing data compression and transmission, edge data processing, as well as end-to-end delay and network operation cost were constructed. The cost of network operation was suppressed while reducing end-to- end latency by jointly optimizing sensing-communication variables such as device scheduling, data compression ratio, bandwidth allocation, and power control. Subsequently, in order to maintain the operation level of the distribution service carried by each acquisition device, the device must complete a certain number of data acquisition, compression and transmission within a specified time slot. A long-term terminal scheduling frequency constraint was introduced to safeguard this requirement. Meanwhile, the long-term optimization problem was decoupled into a single time slot deterministic optimization problem by Lyapunov theory. Then, for the mixed nonlinear integer programming problem containing multiple variables, a sensing- communication integrated intelligent resource allocation algorithm for high-frequency acquisition scheduling frequency sensing was proposed in two stages. In the first stage, an improved DQN algorithm was used to realize the device scheduling and data compression ratio selection strategy. A penalty term was introduced into the loss function calculation of traditional DQN to examine whether the long-term terminal scheduling frequency constraints are satisfied or not, which improves the algorithm’s ability to perceive the scheduling frequency of each device. In the second stage, the ADMM algorithm was used to optimize the transmission power and bandwidth of each device in a distributed way, which can greatly improve the convergence of the algorithm. The proposed algorithm ensures that the high-frequency acquisition and low-latency interaction requirements of distribution network services are well satisfied. Finally, the effectiveness and reasonableness of the proposed algorithm was verified by simulation examples. Simulation results show that the proposed method can effectively reduce the average weighted sum of end-to-end delay and operating costs of the sensing-communication integrated network, which are reduced by 12.41% and 18.07%, compared with the comparison algorithm. Meanwhile, the proposed algorithm also fully considers the characteristics and demands of high-frequency acquisition services in the distribution network, which makes it much more aware of the device scheduling frequency and significantly enhances the learning speed. The authors’ team will further explore the deep integration of global perception of the power grid and integrated sensing and communication (ISAC).
王睿秋雨, 张洪硕, 张孙烜, 廖海君, 周振宇. 基于通感协同的配电网数据高频采集与低时延互动方法[J]. 电工技术学报, 2026, 41(3): 924-937.
Wang Ruiqiuyu, Zhang Hongshuo, Zhang Sunxuan, Liao Haijun, Zhou Zhenyu. A High-Frequency Acquisition and Low-Latency Interaction Method for Distribution Network Data Based on Sensing-Communication Integrated. Transactions of China Electrotechnical Society, 2026, 41(3): 924-937.
[1] Dong Yanjie, Hossain M J, Cheng Julian, et al.Cross-layer scheduling and beamforming in smart-grid powered cellular networks with heterogeneous energy coordination[J]. IEEE Transactions on Communica-tions, 2020, 68(5): 2711-2725. [2] 肖娟霞, 李勇, 韩宇, 等. 计及台风时空特性和灵活性资源协同优化的配电网弹性提升策略[J]. 电工技术学报, 2024, 39(23): 7430-7446. Xiao Juanxia, Li Yong, Han Yu, et al.Resilience enhancement strategy for distribution networks considering the spatiotemporal characteristics of typhoon and the collaborative optimization of flexible resources[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7430-7446. [3] Jiang Yazhou.Data-driven fault location of electric power distribution systems with distributed generation[J]. IEEE Transactions on Smart Grid, 2020, 11(1): 129-137. [4] 董雷, 李扬, 陈盛, 等. 考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度[J]. 电工技术学报, 2024, 39(9): 2635-2651. Dong Lei, Li Yang, Chen Sheng, et al.Multi-microgrid cooperative game optimization scheduling considering multiple uncertainties and coupled electricity-carbon transactions[J]. Transactions of China Electro-technical Society, 2024, 39(9): 2635-2651. [5] Guo Yu, Ni Xuming, Li Lifeng.Design and application of automation system with the distribution network system in power communication[C]//2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi’an, China, 2022: 384-388. [6] 李宗晟, 张璐, 张志刚, 等. 考虑柔性资源多维价值标签的交直流配电网灵活调度[J]. 电工技术学报, 2024, 39(9): 2621-2634. Li Zongsheng, Zhang Lu, Zhang Zhigang, et al.A flexible scheduling method of AC/DC hybrid distribu-tion network considering the multi-dimensional value tags of flexible resources[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2621-2634. [7] 吴舒坦, 王琦, 于昌平, 等. 计及时空灵活性资源协同的配电信息物理系统规划方法[J]. 电力系统自动化, 2025, 49(15): 74-88. Wu Shutan, Wang Qi, Yu Changping, et al.Cyber-physical distribution system planning considering spatial-temporal flexible resource collaboration[J]. Automation of Electric Power Systems, 2025, 49(15): 74-88. [8] Luo Ling, Liu Zhenyu, Chen Zhiyong, et al.Age of information-based scheduling for wireless D2D systems with a deep learning approach[J]. IEEE Trans-actions on Green Communications and Networking, 2022, 6(3): 1875-1888. [9] 李云鸷, 刘吉臻, 胡阳. 计及低碳响应的综合能源系统多时间尺度源-荷互动优化调度[J]. 太阳能学报, 2024, 45(11): 84-98. Li Yunzhi, Liu Jizhen, Hu Yang.Multi-time scale source-load interactive optimal scheduling of integ-rated energy system considering low-carbon demand response[J]. Acta Energiae Solaris Sinica, 2024, 45(11): 84-98. [10] 吴润泽, 马伊嘉, 郭昊博, 等. 面向海量分布式新能源业务流的通存资源无交互协同预留算法[J]. 电力系统自动化, 2024, 48(23): 112-120. Wu Runze, Ma Yijia, Guo Haobo, et al.Interaction-free cooperative reservation algorithm of communication-cache resources for massively distributed renewable energy service flow[J]. Automation of Electric Power Systems, 2024, 48(23): 112-120. [11] 李鹏, 钟瀚明, 马红伟, 等. 基于深度强化学习的有源配电网多时间尺度源荷储协同优化调控[J]. 电工技术学报, 2025, 40(5): 1487-1502. Li Peng, Zhong Hanming, Ma Hongwei, et al.Multi-timescale optimal dispatch of source-load-storage coordination in active distribution network based on deep reinforcement learning[J]. Transactions of China Electrotechnical Society, 2025, 40(5): 1487-1502. [12] 汤中卫, 刘昊东, 龚榆淋, 等. 适配配网分级调控的通感算资源协同互动方法[J/OL]. 华北电力大学学报(自然科学版), 2024: 1-11[2025-04-07]. https://kns. cnki.net/kcms/detail/13.1212.TM.20241025.1457.002. html. Tang Zhongwei, Liu Haodong, Gong Yulin, et al. A collaborative interaction method of communication, sensing and computational resources adapted for distribution network hierarchical regulation[J/OL]. Journal of North China Electric Power University (Natural Science Edition), 2024: 1-11[2025-04-07]. https://kns.cnki.net/kcms/detail/13.1212.TM.20241025.1457.002.html. [13] 金仙美, 王佳妮, 赵力强, 等. 面向6G通感算融合的多粒度资源分配算法[J]. 无线电通信技术, 2023, 49(1): 89-99. Jin Xianmei, Wang Jiani, Zhao Liqiang, et al.Multi-granularity resource allocation algorithm based on network intelligence sensing[J]. Radio Communic-ations Technology, 2023, 49(1): 89-99. [14] 路韬, 张捷, 张永旺, 等. 面向配电网高频采集的通信资源智能编排技术[J]. 南方电网技术, 2025, 19(5): 73-82. Lu Tao, Zhang Jie, Zhang Yongwang, et al.Intelligent scheduling technology of communication resource for high frequency collection in distribution networks[J]. Southern Power System Technology, 2025, 19(5): 73-82. [15] 丰雷, 谢坤宜, 朱亮, 等. 面向电网业务质量保障的5G高可靠低时延通信资源调度方法[J]. 电子与信息学报, 2021, 43(12): 3418-3426. Feng Lei, Xie Kunyi, Zhu Liang, et al.5G ultra-reliable and low latency communication resource scheduling for power business quality assurance[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3418-3426. [16] 詹金珍, 郭达伟, 滑维鑫. 基于公平性的D2D时隙调度算法[J]. 计算机应用, 2017, 37(3): 711-716. Zhan Jinzhen, Guo Dawei, Hua Weixin.Device to device time division scheduling algorithm based on fairness[J]. Journal of Computer Applications, 2017, 37(3): 711-716. [17] Zhang Qixun, Sun Hongzhuo, Lv Yifan, et al.Flexible time resource allocation method for joint communication and sensing enabled CAVs[C]//2023 International Conference on Future Communications and Networks (FCN), Queenstown, New Zealand, 2023: 1-5. [18] 王峰, 刘明林, 文祥宇, 等. 基于联邦生成对抗网络的电力物联网云-边-端协同资源调度方法[J/OL]. 华北电力大学学报(自然科学版), 2024: 1-11[2024-11-02]. https://kns.cnki.net/kcms/detail/13.1212.TM.2024 0322.1453.002.html. Wang Feng, Liu Minglin, Wen Xiangyu, et al. Federated GAN based cloud-edge-end collaborative resource scheduling method for PIoT[J/OL]. Journal of North China Electric Power University (Natural Science Edition), 2024: 1-11[2024-11-02]. https://kns. cnki.net/kcms/detail/13.1212.TM.20240322.1453.002. html. [19] 于子淇, 刘健阳, 陈亚鹏, 等. 面向配电网高频采集的云边端协同业务处理机制[J]. 电工技术学报, 2025, 40(11): 3502-3513. Yu Ziqi, Liu Jianyang, Chen Yapeng, et al.Cloud-edge-end collaborative service processing mechanism for high frequency acquisition in distribution network[J]. Transactions of China Electrotechnical Society, 2025, 40(11): 3502-3513. [20] 李斌, 朱潇, 王俊义. 基于数据压缩的无人机边缘计算卸载优化[J]. 数据采集与处理, 2024, 39(6): 1432-1444. Li Bin, Zhu Xiao, Wang Junyi.Offloading optimiza-tion based on data compression in UAV-ssisted edge computing[J]. Journal of Data Acquisition and Processing, 2024, 39(6): 1432-1444. [21] Xiong Xiong, Zheng Kan, Lei Lei, et al.Resource allocation based on deep reinforcement learning in IoT edge computing[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(6): 1133-1146. [22] 王皓, 艾芊, 吴俊宏, 等. 基于交替方向乘子法的微电网群双层分布式调度方法[J]. 电网技术, 2018, 42(6): 1718-1727. Wang Hao, Ai Qian, Wu Junhong, et al.Bi-level distributed optimization for microgrid clusters based on alternating direction method of multipliers[J]. Power System Technology, 2018, 42(6): 1718-1727. [23] Karakuş O, Kuruoğlu E E, Altınkaya M A.Modelling impulsive noise in indoor powerline communication systems[J]. Signal, Image and Video Processing, 2020, 14(8): 1655-1661. [24] Shi Zhan.Cloud-edge collaborative federated GAN based data processing for IoT-empowered multi-flow integrated energy aggregation dispatch[J]. Computers, Materials & Continua, 2024, 80(1): 973-994. [25] 陈亚鹏, 刘朋矩, 周振宇, 等. 面向业务可靠承载的电力弹性光网络自主协同决策[J]. 电工技术学报, 2023, 38(21): 5821-5831, 5877. Chen Yapeng, Liu Pengju, Zhou Zhenyu, et al.Autonomous collaborative decision-making for power elastic optical network oriented to service reliable bearing[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5821-5831, 5877. [26] Liao Haijun, Fan Jinchao, Ci Haoyu, et al.Electric semantic compression-based 6G wireless sensing and communication integrated resource allocation[J]. IEEE Internet of Things Journal, 2024, 11(24): 39333-39345. [27] Li Biwei, Wang Xianbin, Xin Yan, et al.Value of service maximization in integrated localization and communication system through joint resource allocation[J]. IEEE Transactions on Communications, 2023, 71(8): 4957-4971.