Adaptive Kernighan-Lin Algorithm for Efficient Power Grid Partitioning in Distributed Security-Constrained Unit Commitment

With the expansion of power system scale and the integration of virtual power plants (VPPs), the computational speed of power system optimization problems struggles to meet the growing demand. For the Security-Constrained Unit Commitment (SCUC) problem, a distributed solution method based on the Ker...

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Veröffentlicht in:2025 IEEE 8th International Electrical and Energy Conference (CIEEC) S. 2888 - 2894
Hauptverfasser: Zhang, Wei, Dai, Zhen, Song, Xueqing, He, Boyu, Jin, Xiaolong, Xie, Xinglang, Han, Jian, Liu, Yi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 16.05.2025
Schlagworte:
Accuracy
Computational efficiency
Convex functions
Distributed optimization
Optimization
Optimization models
Partitioning algorithms
power grid partitioning
Power grids
Scalability
Security-Constrained Unit Commitment
Virtual power plants
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Zusammenfassung:With the expansion of power system scale and the integration of virtual power plants (VPPs), the computational speed of power system optimization problems struggles to meet the growing demand. For the Security-Constrained Unit Commitment (SCUC) problem, a distributed solution method based on the Kernighan-Lin graph partitioning algorithm is proposed. Firstly, to better apply the graph partitioning algorithm and improve the data processing efficiency of the SCUC optimization model, this paper constructs a graph-based SCUC model using a graph database. On this basis, the Adaptive KL algorithm is applied to partition the power grid into different subregions. Finally, the Alternating Direction Method of Multipliers (ADMM) is employed for solving the problem. By decomposing the large-scale SCUC problem into multiple subproblems for different regions, the proposed method effectively addresses the challenges associated with problem size and computational complexity. Case studies demonstrate the accuracy and effectiveness of the proposed method. Compared with centralized solution methods, the proposed approach improves the computational efficiency of large-scale power systems while ensuring computational accuracy.
DOI:10.1109/CIEEC64805.2025.11116538