Abstract
The ever-expanding power system is developed into an interconnected pattern of power grids. Zone partitioning is an essential technique for the operation and management of such an interconnected power system. Owing to the transmission capacity limitation, transmission congestion may occur with a regional influence on power system. If transmission congestion is considered when the system is decomposed into several regions, the power consumption structure can be optimized and power system planning can be more reasonable. At the same time, power resources can be properly allocated and system safety can be improved. In this paper, we propose a power system zone partitioning method where the potential congested branches are identified and the spectral clustering algorithm is improved. We transform the zone partitioning problem into a graph segmentation problem by constructing an undirected weighted graph of power system where the similarities between buses are measured by the power transfer distribution factor (PTDF) corresponding to the potential congested branches. Zone partitioning results show that the locational marginal price (LMP) in the same zone is similar, which can represent regional price signals and provide regional auxiliary decisions.
Highlights
Recent interest in solving problems of power system operation and planning has focused on parallel or decentralized computing
This paper proposes a power system zone partitioning method using an improved spectral clustering algorithm, which can avoid the defects of traditional clustering algorithms like k-means [14] that are sensitive to initial values and easy to fall into local optimization
Since the difference between locational marginal price (LMP) is proportional to the power transfer distribution factor (PTDF), PTDF can reveal the impact of transmission congestion on power system indirectly
Summary
Recent interest in solving problems of power system operation and planning has focused on parallel or decentralized computing. Transmission congestion is an essential factor that leads to narrow zone partitioning results, which cannot reveal the system operation rules with an impact on regional decisionmaking [6]. Spectral clustering is usually used to reveal the internal connectivity structure of such a topology network with the eigenvalues and eigenvectors of an associated matrix [12] It plays a vital role in image processing especially for image segmentation by taking local information such as edge weights to globalize them [13]. We identify the potential congested branches before power system zone partitioning for a more reasonable zone partitioning result that can represent regional economic characteristics to support system operation decision-making.
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