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Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage

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  • Chao Luo
  • Yih-Fang Huang
  • Vijay Gupta

Abstract

This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties --- charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. We propose a new metric to assess the impact on power grid without solving complete power flow equations. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms --- stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) --- are applied to derive the pricing and electricity procurement policy. A Pareto front of the multiobjective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.

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  • Chao Luo & Yih-Fang Huang & Vijay Gupta, 2018. "Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage," Papers 1801.02128, arXiv.org.
    • Handle: RePEc:arx:papers:1801.02128
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    References listed on IDEAS

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    1. Yi, Zonggen & Bauer, Peter H., 2016. "Optimization models for placement of an energy-aware electric vehicle charging infrastructure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 227-244.
    2. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
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    Cited by:

    1. Chao-Tsung Ma, 2019. "System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review," Energies, MDPI, vol. 12(21), pages 1-22, November.
    2. Konstantina Valogianni & Wolfgang Ketter & John Collins & Dmitry Zhdanov, 2020. "Sustainable Electric Vehicle Charging using Adaptive Pricing," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1550-1572, June.
    3. Santarromana, Rudolph & Mendonça, Joana & Dias, André Martins, 2020. "The effectiveness of decarbonizing the passenger transport sector through monetary incentives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 442-462.
    4. Fox-Penner, Peter & Gorman, Will & Hatch, Jennifer, 2018. "Long-term U.S transportation electricity use considering the effect of autonomous-vehicles: Estimates & policy observations," Energy Policy, Elsevier, vol. 122(C), pages 203-213.
    5. Miguel Campaña & Esteban Inga & Jorge Cárdenas, 2021. "Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities," Energies, MDPI, vol. 14(16), pages 1-16, August.
    6. Urooj Asgher & Muhammad Babar Rasheed & Ameena Saad Al-Sumaiti & Atiq Ur-Rahman & Ihsan Ali & Amer Alzaidi & Abdullah Alamri, 2018. "Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources," Energies, MDPI, vol. 11(12), pages 1-26, December.
    7. Liu, Shuohan & Cao, Yue & Ni, Qiang & Xu, Lexi & Zhu, Yongdong & Zhang, Xin, 2023. "Towards reservation-based E-mobility service via hybrid of V2V and G2V charging modes," Energy, Elsevier, vol. 268(C).
    8. Ruisheng Wang & Zhong Chen & Qiang Xing & Ziqi Zhang & Tian Zhang, 2022. "A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station," Sustainability, MDPI, vol. 14(3), pages 1-14, February.
    9. Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
    10. Hui Guo & Dandan Gong & Lijun Zhang & Wenke Mo & Feng Ding & Fei Wang, 2022. "Time-Decoupling Layered Optimization for Energy and Transportation Systems under Dynamic Hydrogen Pricing," Energies, MDPI, vol. 15(15), pages 1-18, July.
    11. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    12. Nala Alahmari & Rashid Mehmood & Ahmed Alzahrani & Tan Yigitcanlar & Juan M. Corchado, 2023. "Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters," Sustainability, MDPI, vol. 15(22), pages 1-44, November.
    13. Gong, Lili & Cao, Wu & Liu, Kangli & Yu, Yue & Zhao, Jianfeng, 2020. "Demand responsive charging strategy of electric vehicles to mitigate the volatility of renewable energy sources," Renewable Energy, Elsevier, vol. 156(C), pages 665-676.
    14. Rui Ye & Xueliang Huang & Ziqi Zhang & Zhong Chen & Ran Duan, 2018. "A High-Efficiency Charging Service System for Plug-in Electric Vehicles Considering the Capacity Constraint of the Distribution Network," Energies, MDPI, vol. 11(4), pages 1-20, April.
    15. Lee, Sangyoon & Choi, Dae-Hyun, 2021. "Dynamic pricing and energy management for profit maximization in multiple smart electric vehicle charging stations: A privacy-preserving deep reinforcement learning approach," Applied Energy, Elsevier, vol. 304(C).
    16. Cui, Jingshi & Wu, Jiaman & Wu, Chenye & Moura, Scott, 2023. "Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies," Applied Energy, Elsevier, vol. 352(C).
    17. Leonardo Nogueira Fontoura da Silva & Marcelo Bruno Capeletti & Alzenira da Rosa Abaide & Luciano Lopes Pfitscher, 2024. "A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior," Energies, MDPI, vol. 17(7), pages 1-27, April.

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