TY - GEN
T1 - Coordinating EV charging demand with wind supply in a bi-level energy dispatch framework
AU - Huang, Qilong
AU - Jia, Qing Shan
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2016 American Automatic Control Council (AACC).
PY - 2016/7/28
Y1 - 2016/7/28
N2 - Electric vehicles (EVs) and wind power are becoming the key to achieving the green energy target. The coordination between EV charging demand and wind supply can reduce the greenhouse gas emission and the driving cost. The main challenges of this coordination are the large number of EVs and the uncertainties in the wind supply and EV charging demand. Therefore, facing to these two challenges, we propose a bi-level optimization method to coordinate the EV charging load with the uncertain wind supply. We make the following contributions. First, a bi-level energy dispatch framework is proposed for this coordination problem. In order to handle the uncertainties in the EV moving and wind power, this problem is formulated as a bi-level Markov Decision Process (MDP) to determine the optimal charging policy of the EVs. Second, by utilizing the aggregation relationship between upper-level and lower-level, a bi-level simulation-based policy improvement (SBPI) method is developed to solve this problem for a large number of EVs. The effectiveness of the proposed bi-level MDP model and bi-level SBPI is validated through numerical result.
AB - Electric vehicles (EVs) and wind power are becoming the key to achieving the green energy target. The coordination between EV charging demand and wind supply can reduce the greenhouse gas emission and the driving cost. The main challenges of this coordination are the large number of EVs and the uncertainties in the wind supply and EV charging demand. Therefore, facing to these two challenges, we propose a bi-level optimization method to coordinate the EV charging load with the uncertain wind supply. We make the following contributions. First, a bi-level energy dispatch framework is proposed for this coordination problem. In order to handle the uncertainties in the EV moving and wind power, this problem is formulated as a bi-level Markov Decision Process (MDP) to determine the optimal charging policy of the EVs. Second, by utilizing the aggregation relationship between upper-level and lower-level, a bi-level simulation-based policy improvement (SBPI) method is developed to solve this problem for a large number of EVs. The effectiveness of the proposed bi-level MDP model and bi-level SBPI is validated through numerical result.
KW - Bi-level optimization
KW - Discrete time dynamic system
KW - Electrical vehicles
KW - Wind power
UR - https://www.scopus.com/pages/publications/84992116237
U2 - 10.1109/ACC.2016.7526649
DO - 10.1109/ACC.2016.7526649
M3 - 会议稿件
AN - SCOPUS:84992116237
T3 - Proceedings of the American Control Conference
SP - 6233
EP - 6238
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -