Risk Assessment of Distribution Networks Integrating Large-Scale Distributed Photovoltaics

Risk impacts on the operation of electric power distribution networks, when large amounts of distributed photovoltaics (PVs) are included, are gradually increasing to become a critical consideration for distribution networks' operations. In this paper, a risk assessment method is proposed which...

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Veröffentlicht in:IEEE access 2019, Vol.7, p.59653-59664
Hauptverfasser: Wang, Lei, Yuan, Minyu, Zhang, Fan, Wang, Xuli, Dai, Lei, Zhao, Feng
Format: Artikel
Sprache:eng
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Zusammenfassung:Risk impacts on the operation of electric power distribution networks, when large amounts of distributed photovoltaics (PVs) are included, are gradually increasing to become a critical consideration for distribution networks' operations. In this paper, a risk assessment method is proposed which considers large-scale distributed PV feeding into distribution networks. Cluster partitioning is used to group large-scale distributed PV, to more accurately calculate the operational risk indicators for distribution networks with large-scale distributed PV compared to traditional risk assessment methods. A copula function is used to describe the correlation between the PV output and the load, in order to more accurately calculate power flow information compared to the traditional independent model of output and load. Node voltages over-limits and branch power flows outside boundaries are selected as the risk indicators, and a comprehensive risk indicator is introduced to more comprehensively and meaningfully evaluate the risk of distribution networks' operation. Also, the effects on the operational risk of the distribution network of load fluctuations, and, PV quantity, capacity, and location are analyzed and discussed. Finally, the IEEE34-bus system was simulated, and the results for different scenarios were calculated and analyzed. The simulation results demonstrate the effectiveness of the proposed method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2912804