考虑新能源黑启动电源时空互补特性的分区恢复统一优化决策

doi:10.19595/j.cnki.1000-6753.tces.260732

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Abstract

In recent years, the penetration of renewable energy has been gradually increasing. The inherent characteristics of renewable energy, together with continuous advancements in control technologies, enable renewable-based black-start sources to enhance the emergency resilience of power systems. However, existing optimization methods for partition restoration have not fully considered the operational characteristics of renewable-based black-start sources and have difficulty in effectively exploiting the synergistic support benefits among renewable energy sites. Therefore, to address the shortage of traditional black-start sources, this paper proposes a partition restoration method considering the spatio-temporal complementarity of renewable-based black-start sources.
First, a black start capability assessment framework considering the spatio-temporal complementarity of renewable energy sites is established, based on which renewable energy sites are classified into three recovery roles: independent support, collaborative support, and external-start-dependent. Then, differentiated upper output constraints are applied to different recovery roles to determine the adjustable power ranges of renewable energy sites. Subsequently, the complementarity among renewable energy sites is quantified according to the changes in volatility before and after incorporating available output, thereby characterizing the interconnection benefits of renewable-based black-start sources. This objective guides renewable-based black-start sources with high complementarity to be assigned to the same partition, forming a combined source that provides coordinated output and enables rapid interconnection during the restoration process. Based on this, a multi-objective mixed-integer linear programming model is constructed with the objectives of maximizing the generation capability of thermal power units, minimizing critical load losses, and maximizing the interconnection benefits of renewable-based black-start sources. The proposed model integrates the formation and partitioning of renewable-based black-start source groups, the generation of partition boundaries, and the optimization of component restoration sequences. Finally, the effectiveness of the proposed method is verified in the modified IEEE 39-bus system and IEEE 118-bus system.
The results demonstrate that the proposed method can provide safe and efficient restoration schemes for power systems with a high proportion of renewable energy. The following conclusions can be drawn from the simulation analysis: (1) The proposed black start capability assessment method, which considers the spatio-temporal complementarity, can effectively exploit the black start potential of renewable energy sites. This increases the initial startup power available to the system and accelerates power supply restoration. (2) By utilizing the spatio-temporal complementarity to construct renewable-based black-start source groups, multiple power sources can achieve coordinated power output. This effectively reduces fluctuations in available renewable power within each partition, thereby lowering operational risks during the restoration process. (3) By jointly optimizing the formation and partitioning of renewable-based black-start source groups, partition boundaries, and component restoration sequences, the proposed model obtains an optimal restoration scheme. This enables better utilization of the black start capability of the system and satisfies the requirements for rapid decision-making during the restoration process.

Key words： Black start partition restoration renewable-based black-start sources spatio-temporal complementarity

Received: 14 May 2026

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TM732

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	Guo Menghao
	Wang Yingshan
	Gu Xueping
	Li Shaoyan

Cite this article:

Guo Menghao,Wang Yingshan,Gu Xueping等. Unified Optimization Decision for Partition Restoration Considering Spatio-temporal Complementarity of Renewable-based Black-start Sources[J]. Transactions of China Electrotechnical Society, 0, (): 260732-.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.260732 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/260732

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