1 research outputs found
Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties
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
increased 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.Comment: 11 pages, 9 figures, Proceedings of VEHITS 2016, ISBN:
978-989-758-185-