Real-Time Load Elasticity Tracking and Pricing for Electric Vehicle Charging

Nasim Yahya Soltani, Seung Jun Kim, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6948246
Pages (from-to)1303-1313
Number of pages11
JournalIEEE Transactions on Smart Grid
Volume6
Issue number3
DOIs
StatePublished - May 1 2015

Keywords

  • Conditional random field (CRF)
  • online convex optimization
  • real-time pricing
  • smart grid

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