Risk-Averse Joint Capacity Evaluation of PV Generation and Electric Vehicle Charging Stations in Distribution Networks

被引：0
|
作者：
Chen, Huimiao [1 ]
Hu, Zechun [1 ]
Jia, Yinghao [2 ]
Shen, Zuo-Jun Max [3 ,4 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
[2] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China
[3] Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA
[4] Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA
来源：
2017 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE EUROPE (ISGT-EUROPE) | 2017年
基金：
中国国家自然科学基金;
关键词：
Capacity evaluation; PV generation; EVs; distributionally robust chance constrained programming; WC-CVaR; SIMULTANEOUS ALLOCATION; RENEWABLE ENERGY; PEVS; DG;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Increasing penetration of distribution generation (DG) and electric vehicles (EVs) calls for an effective way to estimate the achievable capacity connected to the distribution systems, but the exogenous uncertainties of DG outputs and EV charging loads make it challengeable. This study provides a joint capacity evaluation method with a risk threshold setting function for photovoltaic (PV) generation and EV charging stations (EVCSs). The method is mathematically formulated as a distributionally robust joint chance constrained programming model. And the worst-case conditional value at risk (WC-CVaR) approximation and an iterative algorithm based on semidefinite program (SDP) are used to solve the model. Finally, the method test is carried out numerically on IEEE 33-bus radial distribution system.
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收藏
页数：6
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