Dynamic Pricing and Energy Management Strategy for EV Charging Stations under Uncertainties

Chao Luo, Yih-Fang Huang and Vijay Gupta

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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 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.

Date: 2018-01
New Economics Papers: this item is included in nep-cmp, nep-com, nep-ene and nep-reg
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Citations: View citations in EconPapers (1)

Published in In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2016)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1801.02783

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