TY - JOUR
T1 - Real-Time Load Elasticity Tracking and Pricing for Electric Vehicle Charging
AU - Soltani, Nasim Yahya
AU - Kim, Seung Jun
AU - Giannakis, Georgios B.
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach.
AB - While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach.
KW - Conditional random field (CRF)
KW - online convex optimization
KW - real-time pricing
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85028233775&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028233775&partnerID=8YFLogxK
U2 - 10.1109/TSG.2014.2363837
DO - 10.1109/TSG.2014.2363837
M3 - Article
AN - SCOPUS:85028233775
SN - 1949-3053
VL - 6
SP - 1303
EP - 1313
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 3
M1 - 6948246
ER -