Enhancement Method for Distributed Photovoltaic Hosting Capacity of Distribution Network Based on Chance-constrained Programming

被引：0
|
作者：
Wang Q. [1 ,2 ]
Yao L. [1 ,2 ]
Sheng W. [3 ]
Xu Y. [1 ,2 ]
Liu K. [3 ]
Cheng F. [1 ,2 ]
机构：
[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan
[2] Hubei Engineering and Technology Research Center for AC, DC Intelligent Distribution Network, Wuhan University, Wuhan
[3] China Electric Power Research Institute, Beijing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2023年 / 47卷 / 18期
关键词：
chance-constrained programming; co-evolution algorithm; distributed photovoltaic; distribution network; hosting capacity;
D O I：
10.7500/AEPS20230416003
中图分类号：
学科分类号：
摘要：
The dense integration of distributed photovoltaic to distribution networks is one of the structural forms of future distribution networks, and the enhancement of distributed photovoltaic hosting capacity of distribution networks is one of the key technical issues for the planning and operation of future distribution networks. Aiming at enhancing the distributed photovoltaic hosting capacity of distribution networks, an enhancement method for the distributed photovoltaic hosting capacity of distribution networks based on the chance-constrained programming is proposed from the perspective of cooperative optimization among reactive power control of the distributed photovoltaic inverter on the source side, and the minimum additional reactive power compensation and network reconstruction on the grid side, as well as comprehensively considering the uncertainties of both the source side and the load side. A multi-objective chance-constrained programming model is established with the objective of maximizing the distributed photovoltaic hosting capacity and minimizing the total investment and operation costs. The co-evolution algorithm based on Latin hypercube sampling based Monte Carlo simulation (LHS-MC) embedded with an improved initial population generation strategy is used to search the optimal solution, in order to improve both the search performance and solution efficiency. Finally, the effectiveness of the proposed method is verified by simulations. © 2023 Automation of Electric Power Systems Press. All rights reserved.
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页码：132 / 141
页数：9
相关论文
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