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EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking (2404.01849v1)
Published 2 Apr 2024 in cs.SE and cs.AI
Abstract: As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces the EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.
- H. Pandzic, B. Franc, S. Stipetic, F. Pandžić, M. Mesar, M. Miletić, and S. Jovanovic, “Electric vehicle charging infrastructure in croatia – first-hand experiences and recommendations for future development,” Journal of Energy - Energija, vol. 71, pp. 16–23, 06 2023.
- H. S. Das, M. M. Rahman, S. Li, and C. W. Tan, “Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review,” Ren. and Sustainable Energy Reviews, 3 2020.
- I. Sengor, O. Erdinc, B. Yener, A. Tascikaraoglu, and J. P. Catalao, “Optimal energy management of EV parking lots under peak load reduction based DR programs considering uncertainty,” IEEE Trans. on Sustainable Energy, vol. 10, no. 3, pp. 1034–1043, 7 2019.
- C. Diaz-Londono, G. Fambri, P. Maffezzoni, and G. Gruosso, “Enhanced ev charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types,” eTransportation, vol. 20, 2024.
- A. M. Koufakis, E. S. Rigas, N. Bassiliades, and S. D. Ramchurn, “Offline and Online Electric Vehicle Charging Scheduling with V2V Energy Transfer,” IEEE Trans. on Intelligent Transportation Systems, vol. 21, no. 5, pp. 2128–2138, 5 2020.
- P. Meenakumar, M. Aunedi, and G. Strbac, “Optimal Business Case for Provision of Grid Services through EVs with V2G Capabilities,” Int. Conf. on Ecological Vehicles and Renewable Energies, 9 2020.
- Y. Yang, H. G. Yeh, and R. Nguyen, “A Robust Model Predictive Control-Based Scheduling Approach for Electric Vehicle Charging With Photovoltaic Systems,” IEEE Systems Journal, vol. 17, no. 1, 3 2023.
- D. Qiu, Y. Wang, W. Hua, and G. Strbac, “Reinforcement learning for electric vehicle applications in power systems:a critical review,” Renewable and Sustainable Energy Reviews, vol. 173, p. 113052, 2023.
- X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, “Deep reinforcement learning: A survey,” IEEE Trans. on Neural Networks and Learning Systems, pp. 1–15, 2022.
- G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv:1606.01540, 2016.
- S. Saxena, “Vehicle-to-grid simulator, version 00,” 11 2013. [Online]. Available: https://www.osti.gov/biblio/1437011
- E. S. Rigas, S. Karapostolakis, N. Bassiliades, and S. D. Ramchurn, “EVLibSim: A tool for the simulation of electric vehicles’ charging stations using the EVLib library,” Simulation Modelling Practice and Theory, vol. 87, pp. 99–119, Sep. 2018.
- E. Balogun, E. Buechler, S. Bhela, S. Onori, and R. Rajagopal, “EV-EcoSim: A Grid-Aware Co-Simulation Platform for the Design and Optimization of Electric Vehicle Charging Infrastructure,” IEEE Trans. on Smart Grid, pp. 1–1, 2023.
- T. Morstyn, K. A. Collett, A. Vijay, M. Deakin, S. Wheeler, S. M. Bhagavathy, F. Fele, and M. D. McCulloch, “Open: An open-source platform for developing smart local energy system applications,” Applied Energy, vol. 275, p. 115397, 2020.
- Z. J. Lee, S. Sharma, D. Johansson, and S. H. Low, “ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research,” IEEE Trans. on Smart Grid, vol. 12, no. 6, Nov. 2021.
- C. Yeh, V. Li, R. Datta, J. Arroyo, N. Christianson, C. Zhang, Y. Chen, M. Hosseini, A. Golmohammadi, Y. Shi, Y. Yue, and A. Wierman, “SustainGym: Reinforcement learning environments for sustainable energy systems,” in NeurIPS Datasets and Benchmarks Track, New Orleans, LA, USA, 12 2023.
- G. Karatzinis, C. Korkas, M. Terzopoulos, C. Tsaknakis, A. Stefanopoulou, I. Michailidis, and E. Kosmatopoulos, “Chargym: An EV Charging Station Model for Controller Benchmarking,” in Artificial Intelligence Applications and Innovations. Springer International Publishing, 2022, pp. 241–252.
- E. Marcel, M. Marius, S. Alfio, and R. Christian, “Accurate ev charging profiles for simulation studies,” Horizon 2020, EC grant agreement no 101056934, Lappeenranta/Lahti, Finland, D8.1, 2023.
- C. F. Lee, K. Bjurek, V. Hagman, Y. Li, and C. Zou, “Vehicle-to-grid optimization considering battery aging,” 22nd IFAC World Congress, vol. 56, no. 2, pp. 6624–6629, 2023.
- J. Schmalstieg, S. Käbitz, M. Ecker, and D. U. Sauer, “A holistic aging model for li(nimnco)o2 based 18650 lithium-ion batteries,” Journal of Power Sources, vol. 257, pp. 325–334, 2014.
- Rijksdienst voor Ondernemend Nederland (RVO), “Statistics electric vehicles and charging in the netherlands: September 2023.”
- “Elaadnl open datasets for electric mobility research,” https://platform.elaad.io/analyses/ElaadNL_opendata.php.
- “Entso-e transparency platform,” https://transparency.entsoe.eu/.
- “Pecan street data portal,” https://www.pecanstreet.org/dataport/.
- S. Pfenninger and I. Staffell, “Long-term patterns of european pv output using 30 years of validated hourly reanalysis and satellite data,” Energy, vol. 114, pp. 1251–1265, 2016.
- C. Diaz, F. Ruiz, and D. Patino, “Smart charge of an electric vehicles station: A model predictive control approach,” in 2018 IEEE Conference on Control Technology and Applications (CCTA), 2018, pp. 54–59.
- A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, “Stable-baselines3: Reliable reinforcement learning implementations,” Journal of ML Research, vol. 22, no. 268, pp. 1–8, 2021.
