TY - JOUR
T1 - An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators
AU - Lu, Xi
AU - Chan, Ka Wing
AU - Xia, Shiwei
AU - Shahidehpour, Mohammad
AU - Ng, Wing Ho
N1 - Funding Information:
Manuscript received April 30, 2020; revised September 9, 2020; accepted November 3, 2020. Date of publication November 10, 2020; date of current version February 26, 2021. This work was supported in part by the Hong Kong Polytechnic University under Research Studentship RUH5, in part by the National Natural Science Foundation of China under Grant 52077075; in part by the Jiangsu Basic Research Project under Grant BK20180284; and in part by the Fundamental Research Funds for the Central Universities under Grant 2019MS007. Paper no. TSG-00665-2020. (Corresponding authors: Ka Wing Chan; Shiwei Xia.) Xi Lu, Ka Wing Chan, and Wing Ho Ng are with the Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong SAR (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2020 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Distribution company
KW - distributionally robust optimization
KW - electric vehicle aggregator
KW - renewable energy
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85096844918&partnerID=8YFLogxK
U2 - 10.1109/TSG.2020.3037053
DO - 10.1109/TSG.2020.3037053
M3 - Journal article
AN - SCOPUS:85096844918
SN - 1949-3053
VL - 12
SP - 1507
EP - 1518
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 2
M1 - 51
ER -
