Authors:
Chao Luo
;
Yih-Fang Huang
and
Vijay Gupta
Affiliation:
University of Notre Dame, United States
Keyword(s):
Electric Vehicle, Charging Station, Dynamic Pricing, Energy Management, Dynamic Programming, Renewable Energy Integration.
Related
Ontology
Subjects/Areas/Topics:
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Power Management
;
Sensor Networks
;
Systems Modeling and Simulation
;
Wireless Information Networks
Abstract:
This paper presents a dynamic pricing and energy management framework for electric vehicle (EV) charging
service providers. To set the charging prices, the service providers faces three uncertainties: the volatility
of wholesale electricity price, intermittent renewable energy generation, and spatial-temporal EV charging
demand. The main objective of our work here is to help charging service providers to improve their total
profits while enhancing customer satisfaction and maintaining power grid stability, taking into account those
uncertainties. We employ a linear regression model to estimate the EV charging demand at each charging
station, and introduce a quantitative measure for customer satisfaction. Both the greedy algorithm and the
dynamic programming (DP) algorithm are employed to derive the optimal charging prices and determine how
much electricity to be purchased from the wholesale market in each planning horizon. Simulation results show
that DP algorithm achieves an increas
ed profit (up to 9%) compared to the greedy algorithm (the benchmark
algorithm) under certain scenarios. Additionally, we observe that the integration of a low-cost energy storage
into the system can not only improve the profit, but also smooth out the charging price fluctuation, protecting
the end customers from the volatile wholesale market.
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