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Pricing EV Charging Service with Demand Charge

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Pricing electric vehicle (EV) charging services is difficult when the electricity tariff includes both time-of-use energy cost and demand charge based on peak power draw. In this paper, we propose a pricing scheme that assigns a sessionspecific energy price to each charging session at the end of the billing period. The session price precisely captures the costs of energy, demand charge, and infrastructure congestion for which that session is responsible in that month while optimizing the trade-off between inexpensive time-of-use pricing and peak power draw. While our pricing scheme is calculated offline at the end of the billing period, we propose an online scheduling algorithm based on model predictive control to determine charging rates for each EV in real-time. We provide theoretical justification for our proposal and support it with simulations using real data collected from charging facilities at Caltech and JPL. Our simulation results suggest that the online algorithm can approximate the offline optimal reasonably well, e.g., the cost paid by the operator in the online setting is higher than the offline optimal cost by 9.2% and 6.5% at Caltech and JPL respectively. In the case of JPL, congestion rents are enough to cover this increase in costs, while at Caltech, this results in a negligible average loss of $18 per month.

Author(s):

Zachary J. Lee    
Caltech
United States

John Z. F. Pang    
Caltech
United States

Steven H. Low    
Caltech
United States

 

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