Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods
Source abstract
Abstract
Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric Vehicles and Infrastructure · Transportation and Mobility Innovations · Vehicular Ad Hoc Networks (VANETs)
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Center on Production Management and Engineering, Universitat Politècnica de València, Plaza Ferrandiz-Carbonell, 03801 Alcoy, Spain
baobab soluciones, Jose Abascal 55, 28003 Madrid, Spain
Department of Computer Science, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain
Funding
Spanish Government with the European Union Next Generation funds · Spanish Ministry of Science and Innovation · Horizon Europe program
References (56)
Show source-provided references
1. Using machine learning methods to predict electric vehicles penetration in the automotive market10.1038/s41598-023-35366-3 ↗
2. A comprehensive analysis of Vehicle to Grid (V2G) systems and scholarly literature on the application of such systems10.1016/j.ref.2020.10.001 ↗
3. Vehicle to grid connected technologies and charging strategies: Operation, control, issues and recommendations10.1016/j.jclepro.2022.130587 ↗
4. Integrating electric vehicles as virtual power plants: A comprehensive review on vehicle-to-grid (V2G) concepts, interface topologies, marketing and future prospects10.1016/j.est.2022.105579 ↗
5. Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: A review10.1049/iet-esi.2019.0047 ↗
6. A new flexible and resilient model for a smart grid considering joint power and reserve scheduling, vehicle-to-grid and demand response
7. Exploring Barriers and Challenges of Electric Vehicles in India and Vehicle-to-Grid Optimization: A Comprehensive Review
8. An innovative approach for estimating energy demand and supply to inform local energy transitions10.1016/j.energy.2021.120731 ↗
9. Taghizad-Tavana, K., Alizadeh, A.A., Ghanbari-Ghalehjoughi, M., and Nojavan, S. (2023). A comprehensive review of electric vehicles in energy systems: Integration with renewable energy sources, charging levels, different types, and standards. Energies, 16.10.3390/en16020630 ↗
10. Martyushev, N.V., Malozyomov, B.V., Khalikov, I.H., Kukartsev, V.A., Kukartsev, V.V., Tynchenko, V.S., Tynchenko, Y.A., and Qi, M. (2023). Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption. Energies, 16.10.3390/en16020729 ↗
11. Optimizing efficiency of Vehicle-to-Grid system with intelligent management and ANN-PSO algorithm for battery electric vehicles10.1016/j.epsr.2023.109936 ↗
12. Multi-objective optimization strategy for distribution network considering V2G-enabled electric vehicles in building integrated energy system10.1186/s41601-020-0154-0 ↗
13. Energy cost optimization of hybrid renewables based V2G microgrid considering multi objective function by using artificial bee colony optimization10.1109/ACCESS.2020.2984537 ↗
14. On stability and robustness of demand response in V2G mobile energy networks10.1109/TSG.2016.2629378 ↗
15. Mathematical modeling of Li-ion battery using genetic algorithm approach for V2G applications10.1109/TEC.2014.2298460 ↗
16. Optimal scheduling of vehicle-to-grid energy and ancillary services10.1109/TSG.2011.2164099 ↗
17. Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid10.1016/j.jpowsour.2011.06.093 ↗
18. Wu, D., Chau, K., and Gao, S. (2010, January 1–3). Multilayer framework for vehicle-to-grid operation. Proceedings of the 2010 IEEE Vehicle Power and Propulsion Conference, Lille, France.10.1109/VPPC.2010.5729041 ↗
19. Ghofrani, M., Arabali, A., and Etezadi-Amoli, M. (2012, January 22–26). Electric drive vehicle to grid synergies with large scale wind resources. Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA.10.1109/PESGM.2012.6345662 ↗
20. Ghanbarzadeh, T., Goleijani, S., and Moghaddam, M.P. (2011, January 24–29). Reliability constrained unit commitment with electric vehicle to grid using hybrid particle swarm optimization and ant colony optimization. Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.10.1109/PES.2011.6039696 ↗
21. A novel method for optimal placement of vehicle-to-grid charging stations in distribution power system using a quantum binary lightning search algorithm10.1016/j.scs.2017.12.035 ↗
22. A rule-based power management strategy for Vehicle-to-Grid system using antlion sizing optimization10.1016/j.est.2021.102913 ↗
23. Multi-objective stochastic techno-economic-environmental optimization of distribution networks with G2V and V2G systems10.1016/j.epsr.2023.109195 ↗
24. A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems10.1016/j.apenergy.2017.11.101 ↗
25. ur Rehman, U., and Riaz, M. (2018, January 9–10). Real time controlling algorithm for vehicle to grid system under price uncertainties. Proceedings of the 2018 1st IEEE International Conference on Power, Energy and Smart Grid (ICPESG), Mirpur, Pakistan.10.1109/ICPESG.2018.8384522 ↗
26. Jadoun, V.K., Sharma, N., Jha, P., S, J.N., Malik, H., and Garcia Marquez, F.P. (2021). Optimal scheduling of dynamic pricing based V2G and G2V operation in microgrid using improved elephant herding optimization. Sustainability, 13.10.3390/su13147551 ↗
27. Sarkar, R., Saha, P.K., Mondal, S., and Mondal, A. (2020, January 22–26). Intelligent scheduling of V2G, V2V, G2V operations in a smart microgrid. Proceedings of the Eleventh ACM International Conference on Future Energy Systems, Virtual Event.10.1145/3396851.3403517 ↗
28. Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques10.1016/j.rser.2015.09.012 ↗
29. Application of a hybrid PSO-GA optimization algorithm in determining pyrolysis kinetics of biomass10.1016/j.fuel.2022.124344 ↗
30. Ghorbani, E., Fluechter, T., Calvet, L., Ammouriova, M., Panadero, J., and Juan, A.A. (2023). Optimizing energy consumption in smart cities’ mobility: Electric vehicles, algorithms, and collaborative economy. Energies, 16.10.3390/en16031268 ↗
31. Castaneda, J., Ghorbani, E., Ammouriova, M., Panadero, J., and Juan, A.A. (2022). Optimizing transport logistics under uncertainty with simheuristics: Concepts, review and trends. Logistics, 6.10.3390/logistics6030042 ↗
32. Ammouriova, M., Herrera, E.M., Neroni, M., Juan, A.A., and Faulin, J. (2022). Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization. Appl. Sci., 13.10.3390/app13010101 ↗
33. A combinatorial artificial intelligence real-time solution to the unit commitment problem incorporating V2G10.1007/s00202-012-0263-5 ↗
34. Solving the stochastic team orienteering problem: Comparing simheuristics with the sample average approximation method10.1111/itor.13302 ↗
35. Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation
36. Bayliss, C., and Panadero, J. (2023). Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events. J. Simul., 1–20.10.1080/17477778.2023.2166879 ↗
37. Noel, L., Zarazua de Rubens, G., Kester, J., Sovacool, B.K., Noel, L., Zarazua de Rubens, G., Kester, J., and Sovacool, B.K. (2019). The Regulatory and Political Challenges to V2G. Vehicle-to-Grid: A Sociotechnical Transition Beyond Electric Mobility, Palgrave Macmillan.10.1007/978-3-030-04864-8 ↗
38. Agile optimization of a two-echelon vehicle routing problem with pickup and delivery10.1111/itor.12796 ↗
39. Biased randomization of heuristics using skewed probability distributions: A survey and some applications10.1016/j.cie.2017.06.019 ↗
40. On the use of agile optimization for efficient energy consumption in smart cities’s transportation and mobility10.3390/futuretransp2040048 ↗
41. Understanding one-way ANOVA using conceptual figures10.4097/kjae.2017.70.1.22 ↗
42. A review of analysis and sample size calculation considerations for Wilcoxon tests10.1213/ANE.0b013e31827f53d7 ↗
43. A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety10.1016/j.jclepro.2020.122779 ↗
44. Rigas, E., Akasiadis, C., Vardaki, E., and Chalkiadakis, G. (2022, January 7–9). AI and Social Anthropology for Large-Scale Vehicle-to-Grid Schemes. Proceedings of the 12th Hellenic Conference on Artificial Intelligence, Corfu, Greece.10.1145/3549737.3549757 ↗
45. Dehaghani, E.S., and Williamson, S.S. (2012, January 18–20). On the inefficiency of vehicle-to-grid (V2G) power flow: Potential barriers and possible research directions. Proceedings of the 2012 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA.
46. Characterization of Li-ion batteries for intelligent management of distributed grid-connected storage10.1109/TEC.2009.2032579 ↗
47. Durability and reliability of electric vehicle batteries under electric utility grid operations: Bidirectional charging impact analysis10.1016/j.jpowsour.2017.05.015 ↗
48. The viability of vehicle-to-grid operations from a battery technology and policy perspective10.1016/j.enpol.2017.11.015 ↗
49. Reinforcement learning for electric vehicle applications in power systems: A critical review10.1016/j.rser.2022.113052 ↗
50. Mangipinto, A., Lombardi, F., Sanvito, F., Quoilin, S., Pavičević, M., and Colombo, E. (2020, January 6–8). RAMP-mobility: Time series of electric vehicle consumption and charging strategies for all European countries. Proceedings of the EMPE, Brussels, Belgium.
51. Multi-Agent Reinforcement Learning for Intelligent V2G Integration in Future Transportation Systems10.1109/TITS.2023.3284756 ↗
52. Ouramdane, O., Elbouchikhi, E., Amirat, Y., and Sedgh Gooya, E. (2021). Optimal sizing and energy management of microgrids with vehicle-to-grid technology: A critical review and future trends. Energies, 14.10.3390/en14144166 ↗
53. Noel, L., Zarazua de Rubens, G., Kester, J., Sovacool, B.K., Noel, L., Zarazua de Rubens, G., Kester, J., and Sovacool, B.K. (2019). The technical challenges to V2G. Vehicle-to-Grid: A Sociotechnical Transition Beyond Electric Mobility, Palgrave Macmillan.10.1007/978-3-030-04864-8 ↗
54. A 10 kW solar-powered bidirectional EV charger compatible with chademo and COMBO10.1109/TPEL.2018.2829211 ↗
55. High-voltage stations for electric vehicle fast-charging: Trends, standards, charging modes and comparison of unity power-factor rectifiers10.1109/ACCESS.2021.3093696 ↗
56. Faults in smart grid systems: Monitoring, detection and classification10.1016/j.epsr.2020.106602 ↗