Source-load Scheduling Strategy Based on Super-node Collaborative Consensus Algorithm

NAVIGATE

Table of Contents

STATISTICS

Viewed31

Downloads24

Source-load Scheduling Strategy Based on Super-node Collaborative Consensus Algorithm

[HTML] PDF下载 (24)

[1]LI Qing,LI Hong,DONG Haiying,et al.Source-load Scheduling Strategy Based on Super-node Collaborative Consensus Algorithm[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):107-113.[doi:10.13705/j.issn.1671-6833.2025.05.003]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 107-113 Column: Public date: 2025-08-10

Title:: Source-load Scheduling Strategy Based on Super-node Collaborative Consensus Algorithm

Author(s):: LI Qing; LI Hong; DONG Haiying; WANG Hao; School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Keywords:: distributed; consistency algorithm; frequency modulation control; supernodes; optimize scheduling; correction factor

CLC:: TM73

DOI:: 10.13705/j.issn.1671-6833.2025.05.003

Abstract:: To solve the issues of high computational and communication costs, as well as the difficulty in achieving fully distributed control within multi-distributed power generation optimization scheduling, a source load collaborative scheduling method based on the supernode collaborative consistency algorithm was proposed in this study, which combined frequency modulation control to enhance the consistency algorithm. Firstly, the original distributed power network structure′s topology was reconstructed based on the single-hop sampling method. Super nodes were chosen for solving of the decision variables, while the local set of ordinary nodes within the partition only communicated with these supernodes. Secondly, an adaptive global correction coefficient frequency modulation control method for improving the discrete consistency algorithm was introduced to better adapt to real-time scheduling and fully distributed control of active distribution networks. This method was utilized to address the system′s minimum operating cost optimization scheduling model. Finally, the effectiveness of the proposed scheduling method in dealing with topology switching and source load mutation scenarios was verified through MATLAB simulation. The simulation results showed that rapid convergence of consistency incremental cost could be achieved by the suggested scheduling algorithm, effectively controlling each DG′s incremental cost below 8.95 yuan when the system underwent sudden changes. Without sudden changes in the system, 197 iterations were needed by the algorithm to achieve scheduling allocation, offering an effective solution for the scheduling department.

References:: [1]LE J, LANG H K, WANG J, et al. The information security issues of distributed economic dispatch for new generation power systems—present situation and forecast [J]. Electronics, 2024, 13(14):2680-2680.
[2]陈婧华,张琳娟,卢丹,等.基于改进粒子群优化算法的分布式电源集群划分方法[J].郑州大学学报(工学版), 2023, 44(5):77-85.
CHEN J H, ZHANG L J, LU D, et al. Distributed power generation cluster partitioning method based on improved particle swarm optimization algorithm [J]. Journal of Zhengzhou University (Engineering Science), 2023, 44 (5): 77-85.
[3]JI L H , XU Z X ,YANG S S , et al. Distributed economic dispatch control in smart grid based on fixed-time dynamic event-triggered algorithm[J]. Electric Power Systems Research, 2024, 236:110933.
[4]WANG Y , XIONG G J , XU S , et al. Large-scale power system multi-area economic dispatch considering valve point effects with comprehensive learning differential evolution[J]. Swarm and Evolutionary Computation, 2024, 89101620-101620.
[5]张富强,白筠妍,张林朋.基于生产甘特图的AGV资源约束调度方法[J].郑州大学学报(工学版),2022,43 (4):23-29.
ZHANG F Q, BAI J Y, ZHANG L P. Scheduling method with AGV resource based on production gantt chart[J]. Journal of Zhengzhou University (Engineering Science), 2022,43(4):23-29.
[6]QUAN Y, XI L. Smart generation system: A decentralized multi-agent control architecture based on improved consensus algorithm for generation command dispatch of sustainable energy systems[J]. Applied Energy, 2024, 365:123209.
[7]米阳,彭建伟,陈博洋,等.基于一致性原理和梯度下降法的微电网完全分布式优化调度[J].电力系统保护与控制, 2022, 50(15):1-10.
MI Y, PENG J W, CHEN B Y, et al. Fully distributed optimal scheduling of microgrids based on consistency principle and gradient descent method[J]. Power System Protection and Control, 2022, 50(15): 1-10.
[8]张步云,王晋宁,梁定康,等.采用一致性算法的自治微电网群分布式储能优化控制策略[J].电网技术, 2020, 44 (5):1705-1713.
ZHANG B Y, WANG J N, LIANG D K, et al. Optimal control strategy for distributed energy storage in autonomous microgrid cluster using consistency algorithm[J]. Power System Technology, 2020, 44(5): 1705-1713.
[9]董璐,边晓燕,周波,等.计及调频备用效益的主动配电网分层分布式协调优化调度[J].电力自动化设备, 2023, 43(1):55-63.
DONG L, BIAN X Y, ZHOU B, et al. Hierarchical distributed coordinated optimization scheduling of active distribution networks considering frequency regulation backup benefits [J]. Electric Power Automation Equipment, 2023, 43(1): 55-63.
[10] CHANG S P, WANG H, LUO X Y, et al. Distributed predefined-time economic dispatch based on event-triggered strategy for microgrids under directed graphs[J]. Neurocomputing, 2024,607:128391-128391.
[11]乐健,周谦,赵联港,等.基于一致性算法的电力分布式经济调度方法综述[J].电力自动化设备, 2020, 40 (3):44-54.
LE J, ZHOU Q, ZHAO L G, et al. A review of power distributed economic dispatch methods based on consistency algorithm[J]. Electric Power Automation Equipment, 2020, 40(3):44- 54.
[12]边晓燕,孙明琦,赵健等.基于一致性算法的源-荷协同分布式优化调控策略[J].中国电机工程学报, 2021, 41(4): 1334-1347,1540.
BIAN X Y, SUN M Q, ZHAO J, et al. Source-load cooperative distributed optimized regulation strategy based on consistency algorithm[J]. Proceedings of the CSEE, 2021, 41(4): 1334-1347,1540.
[13] YIN L F, CAI Z J. Multimodal multi-objective hierarchical distributed consensus method for multimodal multi objective economic dispatch of hierarchical distributed power systems[J]. Energy, 2024, 434:140130.
[14]李龙斌.采用超节点协同的多智能体系统一致性算法[J].信息与控制, 2019, 48(6):694-699,706.
LI L B. Unified Consistency Algorithm for Multi Intelligent Systems Using Hypernode Collaboration [J]. Information and Control, 2019, 48(6): 694-699,706.
[15]赵海祺,卢开诚,董树锋,等.考虑通信状态变化的直流微电网分布式事件驱动优化调度[J].电力系统自动化, 2023, 47(20):33-41.
ZHAO H Q, LU K C, DONG S F, et al. Distributed event-triggered optimal scheduling for DC microgrids considering communication status change[J]. Automation of Electric Power System, 2023, 47(20):33-41.
[16] SHANG L H, SUN M J, PAN C, et al. Distributed optimization scheduling consistency algorithm for smart grid and its application in cost control of power grid[J]. International Journal of Advanced Computer Science and Applications (IJACSA),2024,15(4):34-42.
[17]边晓燕,孙明琦,董璐,等.计及灵活性聚合功率的源荷分布式协调调度[J].电力系统自动化,2021,45 (17):89-98.
BIAN X Y, SUN M Q, DONG L, et al. Distributed source-load coordinated dispatching considering flexible aggregated power [J]. Automation of Electric Power System,2021,45(17):89-98.
[18]陈燕东,李雪萍,等.孤岛微电网多虚拟同步发电机频率无差协调控制策略[J].电力系统保护与控制, 2024, 52(7):12-23.
CHEN Y D, LI X P, et al. Frequency seamless coordinated control strategy for multi virtual synchronous generators in isolated microgrids [J]. Power System Protection and Control, 2024, 52(7): 12-23.

Similar References:

Memo

Last Update: 2025-09-19