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

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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.
Funding Information
  • National Science Foundation (NSF)-Computing and Communication Foundations (CCF) (1423316, CyberSEES 1442686)
  • NSF (1202135)
  • Institute of Renewable Energy and the Environment (RL-0010-13)

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