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1.新能源利用与节能安徽省重点实验室(合肥工业大学),安徽省合肥市 230009
2.合肥综合性国家科学中心能源研究院(安徽省能源实验室),安徽省合肥市 230031
Received:27 June 2022,
Revised:2022-10-22,
Published Online:09 January 2023,
Published:10 March 2023
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方朝,吴红斌,杨晓东等.考虑通信故障的配电网失联分布式电源群优化控制策略[J].电力系统自动化,2023,47(5):44-52.
FANG Zhao,WU Hongbin,YANG Xiaodong,et al.Optimal Control Strategy for Distributed Generator Group Losing Communication in Distribution Network Considering Communication Fault[J].Automation of Electric Power Systems,2023,47(5):44-52.
方朝,吴红斌,杨晓东等.考虑通信故障的配电网失联分布式电源群优化控制策略[J].电力系统自动化,2023,47(5):44-52. DOI: 10.7500/AEPS20220627007.
FANG Zhao,WU Hongbin,YANG Xiaodong,et al.Optimal Control Strategy for Distributed Generator Group Losing Communication in Distribution Network Considering Communication Fault[J].Automation of Electric Power Systems,2023,47(5):44-52. DOI: 10.7500/AEPS20220627007.
高比例新能源并网使得配电网对通信系统的依赖性不断提高。为降低通信设备故障对分布式控制系统运行的影响,提出一种考虑多重通信故障的配电网失联分布式电源(DG)群优化控制方法。首先,建立了分布式通信网络的通信链路矩阵,并基于通信链路搜索得到了典型的故障场景。然后,考虑通信故障场景及源荷的不确定性,以综合风险最小为目标,建立了失联DG群双层优化模型,模型外层优化失联DG多场景控制策略,模型内层优化未失联DG控制策略。最后,采用改进粒子群优化算法求取失联DG群控制节点的最优控制策略。以IEEE 33节点算例系统为例,验证了该控制方法的有效性。结果表明所提方法能够有效降低系统运行风险,提高供电可靠性。
The grid integration of high proportion of renewable energy makes the distribution network increasingly dependent on the communication system. To reduce the influence of communication equipment fault on the operation of the distributed control system
an optimal control method for distributed generator (DG) groups losing communication in the distribution network considering multiple communication faults is proposed. Firstly
the communication link matrix
of the distributed communication network is established
and the typical fault scenarios are obtained based on the communication link search. Secondly
considering the uncertainty of communication fault scenarios
source and load
a double-layer optimization model of DG groups losing communication is established to minimize the comprehensive risk. The outer layer of the model optimizes the multi-scenario control strategy of DG losing communication
and the inner layer of the model optimizes the control strategy of other DGs. Finally
the improved particle swarm optimization algorithm is used to solve the optimal control strategy of the control nodes for the DG group losing communication. The effectiveness of the proposed control method is verified through an IEEE 33-bus case system. The results show that the proposed method can effectively reduce the system operation risk and improve the power supply reliability.
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