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A Novel Method Based on a Non-Stationary Discrete Markov Chain for Tracking Variations in the Quantity of Reserved Energy and the Number of Electric Vehicles

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Abstract

Since the initial suggestion that electrically propelled vehicles could be used on the grid-side, numerous significant investigations have been conducted to showcase the capabilities of these technologies, which have proven to be highly advantageous. Nevertheless, there are still many uncertainties surrounding the integration of electric vehicles into the power grid, which is why it has been likened to a black box. These uncertainties include the number of electric vehicles that will be connected to the grid at any given time, the amount of energy that will be stored in their batteries during both the daytime and overnight, and the impact that their charging profiles will have on the overall load placed on the power system. In addition, there are several unanswered questions that need to be addressed. This article presents a novel model that effectively addresses these uncertainties. It is based on a non-stationary Markov chain, and it was introduced in this paper. The findings of the model provide fascinating insights into the number of electric vehicles connected to the grid and the amount of energy saved over the course of a day, as demonstrated by a case study. In addition, this article analyzes and evaluates the ability of the model to accurately represent the load modeling of electric vehicle charging.

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REFERENCES

  1. Muratori, M., et al., The Rise of Electric Vehicles—2020 Status and Future Expectations, Progr. Energy, 2021, vol. 3, no. 2, p. 022002.

    Article  ADS  Google Scholar 

  2. Kapustin, N.O. and Grushevenko, D.A., Long-Term Electric Vehicles Outlook and Their Potential Impact on Electric Grid, Energy Policy, 2020, vol. 137, p. 111103.

    Article  Google Scholar 

  3. Statharas, S., et al., Factors Influencing Electric Vehicle Penetration in the EU by 2030: A Model-Based Policy Assessment, Energies, 2019, vol. 12, no. 14, p. 2739.

    Article  Google Scholar 

  4. Martins, H., et al., Assessing Policy Interventions to Stimulate the Transition of Electric Vehicle Technology in the European Union, Socio-Econ. Plan. Sci., 2023, vol. 87, p. 101505.

    Article  Google Scholar 

  5. Han, J., et al., A Three-Phase Bidirectional Grid-Connected AC/DC Converter for V2G Applications, J. Control Sci. Engin., 2020, vol. 2020, p. 1–12.

    Article  ADS  Google Scholar 

  6. Inci, M., Savrun, M.M., and Çelik, Ö., Integrating Electric Vehicles as Virtual Power Plants: A Comprehensive Review on Vehicle-to-Grid (V2G) Concepts, Interface Topologies, Marketing and Future Prospects, J. Energy Storage, 2022, vol. 55, p. 105579.

    Article  Google Scholar 

  7. Upputuri, R.P. and Subudhi, B., A Comprehensive Review and Performance Evaluation of Bidirectional Charger Topologies for V2G/G2V Operations in EV Applications, IEEE Trans. Transport. Electrif., 2023.

  8. Alfaverh, F., Denaı̈, M., and Sun, Y., Optimal Vehicle-to-Grid Control for Supplementary Frequency Regulation Using Deep Reinforcement Learning, Electric Power Systems Res., 2023, vol. 214, p. 108949.

    Article  Google Scholar 

  9. El-Hendawi, M., Wang, Z., and Liu, X., Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation, Energies, 2022, vol. 15, no. 12, p. 4446.

    Article  Google Scholar 

  10. Suh, J., Song, S., and Jang, G., Power Imbalance-Based Droop Control for Vehicle to Grid in Primary Frequency Regulation, IET Gener., Transmiss. Distrib., 2022, vol. 16, no. 17, p. 3374–3383.

    Article  Google Scholar 

  11. Alotaibi, M.A. and Eltamaly, A.M., Upgrading Conventional Power System for Accommodating Electric Vehicle through Demand Side Management and V2G Concepts, Energies, 2022, vol. 15, no. 18, p. 6541.

    Article  Google Scholar 

  12. Kirmani, S., et al., Optimal Allocation of V2G Stations in a Microgrid Environment: Demand Response, in 2023 Int. Conf. on Power, Instrumentation, Energy and Control (PIECON), IEEE, 2023.

  13. Tirunagari, S., Gu, M., and Meegahapola, L. Reaping the Benefits of Smart Electric Vehicle Charging and Vehicle-to-Grid Technologies: Regulatory, Policy and Technical Aspects, IEEE Access, 2022.

  14. Rather, Z.H., et al., Technical Feasibility of EV Infrastructure with Renewable Power Integration: A Case Study at NIT Srinagar, in Intelligent Manufacturing and Energy Sustainability: Proceedings of ICIMES 2022, Springer, 2023, pp. 441–449.

  15. Ravi, S. and Aziz, M., Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives, Energies 2022, vol. 15, p. 589 (Note: MDPI stays neutral with regard to jurisdictional claims in published …).

    Article  Google Scholar 

  16. Mojumder, M.R.H., et al., Electric Vehicle-to-Grid (V2G) Technologies: Impact on the Power Grid and Battery, Sustain., 2022, vol. 14, no. 21, p. 13856.

    Article  Google Scholar 

  17. Sayed, M.A., et al., Electric Vehicle Attack Impact on Power Grid Operation, Int. J. Electrical Power Energy Systems, 2022, vol. 137, p. 107784.

    Article  Google Scholar 

  18. Mastoi, M.S., et al., A Study of Charging-Dispatch Strategies and Vehicle-to-Grid Technologies for Electric Vehicles in Distribution Networks, Energy Rep., 2023, vol. 9, p. 1777–1806.

    Article  Google Scholar 

  19. Kuruvilla, V., Kumar, P.V., and Selvakumar, A.I., Challenges and Impacts of V2g Integration—A Review, in 2022 8th Int. Conf. on Advanced Computing and Communication Systems (ICACCS), IEEE, 2022.

  20. Singh, J. and Tiwari, R., Cost Benefit Analysis for V2G Implementation of Electric Vehicles in Distribution System, IEEE Trans. Industry Appl., 2020, vol. 56, no. 5, pp. 5963–5973.

    Article  Google Scholar 

  21. Fan, J. and Chen, Z., Cost-Benefit Analysis of Optimal Charging Strategy for Electric Vehicle With V2G, in 2019 North American Power Symposium (NAPS), IEEE, 2019.

  22. Hao, X., et al., A V2G-Oriented Reinforcement Learning Framework and Empirical Study for Heterogeneous Electric Vehicle Charging Management, Sust. Cities Soc., 2023, vol. 89, p. 104345.

    Article  Google Scholar 

  23. Oad, A., et al., Green Smart Grid Predictive Analysis to Integrate Sustainable Energy of Emerging V2G in Smart City Technologies, Optik, 2023, vol. 272, p. 170146.

    Article  ADS  Google Scholar 

  24. Ntombela, M. and Musasa, K., A Comprehensive Review for Incorporation of Electric Vehicles and Renewable Energy Distributed Generation to Smart Grid, 2023.

  25. Muqeet, H.A., et al., A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment, Energies, 2023, vol. 16, no. 1, p. 472.

    Article  Google Scholar 

  26. Dong, J., et al., Multi-Agent Reinforcement Learning for Intelligent V2G Integration in Future Transportation Systems, IEEE Trans. Intell. Transport. Systems, 2023.

  27. Sami, I., et al., A Bidirectional Interactive Electric Vehicles Operation Modes: Vehicle-to-grid (V2G) and Grid-to-Vehicle (G2V) Variations within Smart Grid, in 2019 Int. Conf. on Engineering and Emerging Technologies (ICEET), IEEE, 2019.

  28. Sovacool, B.K., et al., Actors, Business Models, and Innovation Activity Systems for Vehicle-to-Grid (V2G) Technology: A Comprehensive Review, Renew. Sust. Energy Rev., 2020, vol. 131, p. 109963.

    Article  Google Scholar 

  29. Noel, L., et al., The Economic and Business Challenges to V2G, Vehicle-to-Grid, in A Sociotechnical Transition Beyond Electric Mobility, 2019, p. 91–116.

  30. Wang, M. and Craig, M.T., The Value of Vehicle-to-Grid in a Decarbonizing California Grid, J. Power Sources, 2021, vol. 513, p. 230472.

    Article  Google Scholar 

  31. Huda, M., Koji, T., and Aziz, M., Techno Economic Analysis of Vehicle to Grid (V2G) Integration as Distributed Energy Resources in Indonesia Power System, Energies, 2020, vol. 13, no. 5, p. 1162.

    Article  Google Scholar 

  32. Ali, H., et al., Economic and Environmental Impact of Vehicle-to-Grid (V2G) Integration in an Intermittent Utility Grid, in 2020 2nd Int. Conf. on Smart Power and Internet Energy Systems (SPIES), IEEE, 2020.

  33. Nizami, M.S.H., Hossain, M., and Mahmud, K., A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks, IEEE Systems J., 2020, vol. 15, no. 2, pp. 2066–2077.

    Article  ADS  Google Scholar 

  34. Tostado-Véliz, M., et al., Optimal Energy Management of Cooperative Energy Communities Considering Flexible Demand, Storage and Vehicle-to-Grid under Uncertainties, Sust. Cities Soc., 2022, vol. 84, p. 104019.

    Article  Google Scholar 

  35. Dixon, J., et al., Vehicle to Grid: Driver Plug-in Patterns, Their Impact on the Cost and Carbon of Charging, and Implications for System Flexibility, Etransportation, 2022, vol. 13, p. 100180.

    Article  Google Scholar 

  36. Alirezazadeh, A., et al., A New Flexible and Resilient Model for a Smart Grid Considering Joint Power and Reserve Scheduling, Vehicle-to-Grid and Demand Response, Sust. Energy Technol. Assess., 2021, vol. 43, p. 100926.

    Article  Google Scholar 

  37. Venegas, F.G., Petit, M., and Perez, Y., Active Integration of Electric Vehicles into Distribution Grids: Barriers and Frameworks for Flexibility Services, Renew. Sust. Energy Rev., 2021, vol. 145, p. 111060.

    Article  Google Scholar 

  38. Noel, L., et al., The Regulatory and Political Challenges to V2G, Vehicle-to-Grid, in A Sociotechnical Transition Beyond Electric Mobility, 2019, pp. 117–139.

  39. Beil, I., Whittemore, L., and Shrestha, A., Utility Experience with Vehicle-to-Grid Regulatory and Technology Challenges, and the Final Hurdles to Large-Scale V2G Deployment, in 2022 IEEE Power and Energy Society General Meeting (PESGM), IEEE, 2022.

  40. Savari, G.F., et al., Assessment of Charging Technologies, Infrastructure and Charging Station Recommendation Schemes of Electric Vehicles: A Review, Ain Shams Engin. J., 2022, p. 101938.

  41. Ravi, S.S. and Aziz, M., Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives, Energies, 2022, vol. 15, no. 2, p. 589.

  42. Amin, A., et al., A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network, Sustain., 2020, vol. 12, no. 23, p. 10160.

    Article  Google Scholar 

  43. Ma, S.-C., et al., Analysing Online Behaviour to Determine Chinese Consumers’ Preferences for Electric Vehicles, J. Cleaner Prod., 2019, vol. 229, pp. 244–255.

    Article  Google Scholar 

  44. Boehm, J., Bhargava, H.K., and Parker, G.G., The Business of Electric Vehicles: A Platform Perspective, Found. TrendsTechnol., Information Operations Management, 2020, vol. 14, no. 3, pp. 203–323.

    Article  Google Scholar 

  45. Ahmadian, A., Mohammadi-Ivatloo, B., and Elkamel, A., A Review on Plug-in Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques, J. Modern Power Systems Clean Energy, 2020, vol. 8, no. 3, pp. 412–425.

    Article  Google Scholar 

  46. Wang, M., et al., State Space Model of Aggregated Electric Vehicles for Frequency Regulation, IEEE Trans. Smart Grid, 2019, vol. 11, no. 2, pp. 981–994.

    Article  Google Scholar 

  47. Song, Y. and Hu, X., Learning Electric Vehicle Driver Range Anxiety with an Initial State of Charge-Oriented Gradient Boosting Approach, J. Intell. Transport. Systems, 2023, vol. 27, no. 2, pp. 238–256.

    Article  Google Scholar 

  48. LaMonaca, S. and Ryan, L., The State of Play in Electric Vehicle Charging Services–A Review of Infrastructure Provision, Players, and Policies, Renew. Sust. Energy Rev., 2022, vol. 154, p. 111733.

    Article  Google Scholar 

  49. Hemavathi, S. and Shinisha, A., A Study on Trends and Developments in Electric Vehicle Charging Technologies, J. Energy Storage, 2022, vol. 52, p. 105013.

    Article  Google Scholar 

  50. Ahmad, F., et al., Optimal Location of Electric Vehicle Charging Station and Its Impact on Distribution Network: A Review, Energy Rep., 2022, vol. 8, pp. 2314–2333.

    Article  Google Scholar 

  51. Arun, V., et al., Review on Li-Ion Battery vs Nickel Metal Hydride Battery in EV, Adv. Mat. Sci. Engin., 2022, vol. 2022.

  52. Ramya, P. and Ajaikrishnan, M., A Review on Future Challenges of Ev Range Extension, 2022.

  53. Xing, Y., et al., Multi-Type Electric Vehicle Load Prediction Based on Monte Carlo Simulation, Energy Rep., 2022, vol. 8, pp. 966–972.

    Article  Google Scholar 

  54. Hatherall, O., et al., Load Prediction Based Remaining Discharge Energy Estimation Using a Combined Online and Offline Prediction Framework, in 2022 IEEE Conf. on Control Technology and Applications (CCTA), IEEE, 2022.

  55. Li, X., et al., Electric Vehicle Behavior Modeling and Applications in Vehicle-Grid Integration: An Overview, Energy, 2023, p. 126647.

  56. Lauvergne, R., Perez, Y., and Tejeda, A., Modeling Electric Vehicle Charging Patterns: A Review, Revue d’économie industrielle, 2023, pp. 247–286.

  57. Abolfazli, M., et al., A Probabilistic Method to Model PHEV for Participation in Electricity Market, in 2011 19th Iranian Conf. on Electrical Engineering, IEEE, 2011.

  58. Huang, S. and Infield, D., The Potential of Domestic Electric Vehicles to Contribute to Power System Operation through Vehicle to Grid Technology, in 2009 44th Int. Universities Power Engineering Conference (UPEC), IEEE, 2009.

  59. Bahmani, M.H., et al., Introducing a New Concept to Utilize Plug-in Electric Vehicles in Frequency Regulation Service. in The 2nd Int. Conf. on Control, Instrumentation and Automation, IEEE, 2011.

  60. Gan, L., Topcu, U., and Low, S.H., Stochastic Distributed Protocol for Electric Vehicle Charging with Discrete Charging Rate, in 2012 IEEE Power and Energy Society General Meeting, IEEE, 2012.

  61. Kempton, W., et al., A Test of Vehicle-to-Grid (V2G) for Energy Storage and Frequency Regulation in the PJM System, Results Industry-University Res. Partnership, 2008, vol. 32, pp. 1–32.

    Google Scholar 

  62. Kempton, W., Electric Vehicles: Driving Range, Nature Energy, 2016, vol. 1, no. 9, pp. 1/2.

    Article  Google Scholar 

  63. Norris, J.R., Markov Chains, Cambridge University Press, 1998.

    Google Scholar 

  64. Chung, K.L., Markov Chains, New York: Springer-Verlag, 1967.

    Book  Google Scholar 

  65. Revuz, D., Markov chains, Elsevier, 2008.

    Google Scholar 

  66. Douc, R., et al., Markov chains, Springer, 2018.

    Book  Google Scholar 

  67. Quirós-Tortós, J., Ochoa, L.F., and Lees, B., A Statistical Analysis of EV Charging Behavior in the UK, in 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), IEEE, 2015.

  68. Bertuccelli, L.F. and How, J.P., Estimation of Non-Stationary Markov Chain Transition Models, in 2008 47th IEEE Conference on Decision and Control, IEEE, 2008.

  69. Salzenstein, F., et al., Non-Stationary Fuzzy Markov Chain, Patt. Recognit. Lett., 2007, vol. 28, no. 16, pp. 2201–2208.

    Article  ADS  Google Scholar 

  70. Li, B., et al., Modeling the Impact of EVs in the Chinese Power System: Pathways for Implementing Emissions Reduction Commitments in the Power and Transportation Sectors, Energy Policy, 2021, vol. 149, p. 111962.

  71. Zecchino, A., et al., Large-Scale Provision of Frequency Control via V2G: The Bornholm Power System Case, Electric Power Systems Res., 2019, vol. 170, pp. 25–34.

    Article  Google Scholar 

  72. Masood, A., et al., Transactive Energy for Aggregated Electric Vehicles to Reduce System Peak Load Considering Network Constraints, IEEE Access, 2020, vol. 8, pp. 31519–31529.

    Article  Google Scholar 

  73. Yan, D., Ma, C., and Chen, Y., Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility, IEEE Trans. Sust. Energy, 2022, vol. 14, no. 1, pp. 356–370.

    Article  ADS  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    M. H. Bahmani

  2. Department of Biosystems Engineering (Renewable Energy Systems), Tarbiat Modares University, Tehran, Iran

    M. Esmaeili Shayan

  3. Department of Engineering and Architecture, University of Parma, Parma, Italy

    G. Lorenzini

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  1. M. H. Bahmani
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  2. M. Esmaeili Shayan
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  3. G. Lorenzini
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Correspondence to M. H. Bahmani, M. Esmaeili Shayan or G. Lorenzini.

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Bahmani, M.H., Shayan, M.E. & Lorenzini, G. A Novel Method Based on a Non-Stationary Discrete Markov Chain for Tracking Variations in the Quantity of Reserved Energy and the Number of Electric Vehicles. J. Engin. Thermophys. 32, 758–775 (2023). https://doi.org/10.1134/S1810232823040094

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