An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators

被引:29
|
作者
Lu, Xi [1 ]
Chan, Ka Wing [1 ]
Xia, Shiwei [2 ]
Shahidehpour, Mohammad [3 ]
Ng, Wing Ho [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China
[2] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China
[3] IIT, ECE Dept, Chicago, IL 60616 USA
基金
中国国家自然科学基金;
关键词
Uncertainty; Real-time systems; Electric vehicles; Portfolios; Generators; Energy storage; Companies; Distribution company; electric vehicle aggregator; renewable energy; uncertainty; distributionally robust optimization; ADAPTIVE ROBUST OPTIMIZATION; UNIT COMMITMENT; ENERGY-STORAGE; UNCERTAINTY; FRAMEWORK; SYSTEM; COORDINATION; STRATEGY; DISPATCH; UTILITY;
D O I
10.1109/TSG.2020.3037053
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO's deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.
引用
收藏
页码:1507 / 1518
页数:12
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