Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage
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References listed on IDEAS
- 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.
- 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.
- Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
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Cited by:
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- Weihua Wu & Jieyun Wei & Eun-Young Nam & Yifan Zhang & Dongphil Chun, 2024. "Data Drive—Charging Behavior of Electric Vehicle Users with Variable Roles," Sustainability, MDPI, vol. 16(11), pages 1-18, June.
- 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.
- 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.
- Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
- 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.
- 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).
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-CMP-2018-01-22 (Computational Economics)
- NEP-ENE-2018-01-22 (Energy Economics)
- NEP-TRE-2018-01-22 (Transport Economics)
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