Quantifying Topological Flexibility of Active Distribution Networks Based on Community Detection

Huizi Gu and Xiaodong Chu
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Huizi Gu: Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China
Xiaodong Chu: Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China

Energies, 2020, vol. 13, issue 18, 1-21

Abstract: Active distribution networks (ADNs) provide a flexible platform to integrate various distributed generation sources, among which the intermittent renewable sources impose high operating uncertainty. Topological flexibility of ADNs should be exploited to counter the stochastic operating conditions by modifying the topologies of ADNs. Quantifying the topological flexibility is a vital step to utilize it, which is lacking in previous studies. A quantification method is proposed to measure the topological flexibility of ADNs in this paper. First, the community structures of ADNs are detected to achieve spatial partitions of the networks. Second, an improved spectral clustering algorithm is employed to significantly reduce the dimensionality of the partition space, in which the ADNs are further partitioned using the affinity propagation algorithm. Finally, a topological flexibility metric is defined based on the guiding role of sectionalizing and tie switches within and between communities. The proposed topological flexibility quantification method is a superb approach to the utilization of flexibility resources in distribution networks. Case study results of test ADNs demonstrate the effectiveness and efficiency of the proposed quantification method.

Keywords: topological flexibility; active distribution networks; community structure; sectionalizing switch; tie switch; spectral clustering; affinity propagation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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