DOI:10.35833/MPCE.2020.000886 |
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Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks |
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Page view: 112
Net amount: 580 |
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Author:
Han Wu1,2,Yue Yuan1,Junpeng Zhu1,Kejun Qian3,Yundai Xu1
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Author Affiliation:
1.College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;2.Nanjing Institute of Technology, Nanjing 211167, China;3.Suzhou Power Supply Branch of State Grid Jiangsu Electric Power Company, Suzhou 215004, China
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Foundation: |
This work was supported in part by the National Key Research and Development Program of China (No. 2016YFB0900100) and in part by the National Natural Science Foundation of China (No. 51807051). |
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Abstract: |
Successful distributed photovoltaic (PV) planning now requires a hosting capacity assessment process that accounts for an appropriate model of PV output and its uncertainty. This paper explores how the PV hosting capacity of distribution networks can be increased by means of spatial correlation among distributed PV outputs. To achieve this, a novel PV hosting capacity assessment method is proposed to account for arbitrary geographically dispersed distributed PVs. In this method, the empirical relation between the spatial correlation coefficient and distance is fitted by historical data in one place and then applied to model the joint probability distribution of PV outputs at a neighboring location. To derive the PV hosting capacity at candidate locations, a stochastic PV hosting capacity assessment model that aims to maximize the PV hosting capacity under thermal and voltage constraints is proposed. Benders decomposition algorithm is also employed to reduce the computational cost associated with the numerous sampling scenarios. Finally, a rural 59-bus distribution network in Suzhou, China, is used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology and the significant benefits obtained by increasing geographical distance. |
Keywords: |
Copula ; mixed-integer cone programming ; PV capacity assessment ; spatial correlation ; stochastic program |
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Received:December 21, 2020
Online Time:2021/08/04 |
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