Consensus Algorithm for Economic Scheduling of Microgrids Based on Improved Dynamic Event-Triggered Mechanism

doi:10.12096/j.2096-4528.pgt.24039

Abstract

Abstract:

Objectives To address the instability issues in traditional consensus algorithms for microgrid optimization scheduling, caused by frequent information exchanges among distributed units and changes in system topology.　An improved dynamic event-triggered mechanism consensus algorithm is proposed. Methods The threshold function in the triggering condition of the improved dynamic event-triggering mechanism is modified to enable dynamic adjustment based on the states of distributed units. This ensures that information exchange occurs only when the triggering condition is satisfied. Results The proposed algorithm ensures system stability during changes in microgrid topology and plug-and-play operation of distributed units. It achieves state convergence and consistency among distributed units while significantly reducing communication frequency. Conclusions The application of the improved dynamic event-triggered mechanism consensus algorithm in microgrid economic scheduling enhances system stability and reliability, significantly saves communication resources, and meets the economic requirements of microgrids, making it highly practical.

Key words: distributed energy, microgrid, economic scheduling, consensus algorithm, dynamic event-triggered mechanism, communication frequency, topology change, communication resources, economic requirements

CLC Number:

TK 01

Changle OUYANG, Chun ZHANG, Wentai WU, Weicong LUAN, Xiangzi XIA. Consensus Algorithm for Economic Scheduling of Microgrids Based on Improved Dynamic Event-Triggered Mechanism[J]. Power Generation Technology, 2025, 46(6): 1240-1250.

Figures/Tables 17

Fig. 1 System network topology diagram

Fig. 2 IEEE-14 bus system configuration diagram

Tab. 1 Parameters of distributed units

节点	α_i	β_i	γ_i	P_max/kW	P_min/kW	P₀/kW
G1	300	2.250	0.040	60	30	35
G2	210	4.200	0.031	50	20	30
G3	290	3.250	0.038	50	30	40
G4	320	4.050	0.035	50	20	20
L5	0	8.250	0.036	30	10	10
L6	0	7.200	0.033	20	5	5
L7	0	7.800	0.038	30	10	10
L8	0	8.050	0.030	35	15	20
L9	0	8.450	0.039	35	15	20
L10	0	8.450	0.040	40	15	15
L11	0	9.000	0.043	40	15	20
L12	0	7.050	0.035	25	0	20
L13	0	8.150	0.039	35	20	25
B14	0	0.000	0.175	25	25	5

Fig. 3 Incremental costs of distributed units under proposed algorithm

Fig. 4 Comparison of incremental costs and system imbalance power of distributed units

Fig. 5 Triggering moments

Tab. 2 Performance comparison between traditional consensus algorithm and proposed algorithm

算法	增量成本是否收敛到最优	不平衡功率是否收敛为0 kW	收敛时间/s	总触发次数/次
传统一致性算法	是	是	3.2	28 000
本文算法	是	是	2.5	433

Fig. 6 Structure diagram of IEEE-14 bus system after topology change

Fig. 7 Incremental costs of distributed units aftertopology change

Fig. 8 Incremental costs and system imbalance power of distributed units before and after system topology change

Fig. 9 Triggering moments of distributed units after system topology change

Tab. 3 Performance comparison before and after system topology change

拓扑结构	增量成本是否收敛到最优	不平衡功率是否收敛为0kW	收敛时间/s	总触发次数/次
拓扑变化前	是	是	2.5	433
拓扑变化后	是	是	5.2	618

Fig. 10 Incremental costs and system imbalance power of distributed units during plug-and-play operation

Fig. 11 Triggering moments of distributed units during plug-and-play operation

Fig. 12 Incremental costs, imbalance power, and triggering moments of distributed units under traditional static event-triggered consensus algorithm

Fig. 13 Incremental costs, imbalance power, and triggering moments of distributed units under traditional dynamic event-triggered consensus algorithm

Tab.4 Performance comparison between proposed algorithm and traditional algorithms

算法	增量成本是否收敛到最优	不平衡功率是否收敛为0 kW	收敛时间/s	总触发次数/次
本文算法	是	是	2.5	2 057
传统静态事件触发一致性算法	否	否	不收敛	2 776
传统动态事件触发一致性算法	是	是	3.5	4 205

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