A Distributed Resilience Enhancement Strategy for Multi-microgrids Based on System of Systems Architecture

doi:10.11930/j.issn.1004-9649.202304051

Abstract

Abstract:

To enhance the ability of multi-microgrid systems to cope with extreme scenarios, a distributed resilience enhancement strategy for multi-microgrids based on system of systems(SoS) architecture is proposed. Firstly, the energy exchange process of the multi-microgrid systems is modeled based on the SoS architecture, and the distributed optimization algorithm is used to solve the model, which ensures the privacy of user information. Secondly, the minimum load-shedding problem of the system is solved with both economic optimality and frequency stability as the goals, and the internal needs of the sub-microgrids are specifically met while pursuing the overall operation effect of the system. Finally, the synchronous alternating direction multiplier method based on a dynamic multiplier update strategy is adopted to solve the problem of parameter selection of the distributed algorithm, thus improving the convergence and practicability of the algorithm. Case study verified the effectiveness of the proposed model and algorithm.

Key words: multi-microgrid, system of systems, distributed energy management, resilience

Linxinyan LIN, Junpeng ZHU, Yue YUAN. A Distributed Resilience Enhancement Strategy for Multi-microgrids Based on System of Systems Architecture[J]. Electric Power, 2023, 56(12): 87-99.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202304051

https://www.electricpower.com.cn/EN/Y2023/V56/I12/87

Figures/Tables 17

Fig.1 SoS architecture diagram

Fig.2 Typical system performance evolution curve

Fig.3 The overall process for model solution

Fig.4 Topology of multi-microgrids

Fig.5 Wind-solar-load forecast curves of multi-microgrids

Table 1 Operating cost and power characteristics of multi-microgrid system

类型	参数	m1	m2	m3
柴油发电机	功率区间/kW	0~50	0~60	0~15
	爬坡功率/kW	15	20	15
	成本系数/(元·(kW·h)^–1)	1.5	1.4	1.4
储能	充放电功率区间/kW	0~30	0~30	0~30
	成本系数/(元·(kW·h)^–1)	0.4	0.3	0.3
	初始SOC/(kW·h)	180	180	180
负荷	负荷转移成本系数/(元·(kW·h)^–1)	4	4	4
负荷	切负荷成本系数/(元·(kW·h)^–1)	30	30	30

Fig.6 The rolling optimization process of MPC

Fig.7 Comparison of resilient power supply rates at 20% equipment outage rate

Fig.8 Comparison of resilient power supply rates at 50% equipment outage rate

Fig.9 Comparison of resilient power supply rates at 80% equipment outage rate

Fig.10 Comparison of resilient power supply rates under different equipment outage rates at 12 am

Fig.11 Power exchange comparison of two models

Table 2 Resilience emergent lift rate and total cost

项目		m1	m2	m3
独立运行	韧性指标/(kW·h)	1348.30	2201.60	518.10
独立运行	总成本/元	33893.41	77694.10	13158.60
网络化微网	韧性指标/(kW·h)	1947.80	2842.70	903.50
	韧性涌现提升率/%	36.87	39.43	27.63
	总成本/元	14663.80	38578.10	2587.60
本文方法	韧性指标/(kW·h)	2119.40	3479.20	967.40
	韧性涌现提升率/%	30.87	51.14	17.99
	总成本/元	14714.60	40731.50	2673.30

Fig.12 Comparison of resilient power supply rates under extreme shocks

Fig.13 Comparison of RoCoF under extreme shocks

Fig.14 Convergence process of ADMM with different multiplier update strategies

Table 3 Comparison of distributed optimization and centralized optimization

类型	求解时间/s	总韧性指标/(kW·h)	总成本/元
分布式优化	12.18	6566.15	58119.53
集中式优化	2.85	6569.72	58027.66

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