Application of Mixed Strategist Dynamics and Grid‑Oriented Optimization for Evaluation of Plug‑in Electric Vehicle Load Scheduling

New Energy Exploitation and Application

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Application of Mixed Strategist Dynamics and Grid‑Oriented Optimization for Evaluation of Plug‑in Electric Vehicle Load Scheduling

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https://doi.org/10.54963/neea.v5i1.1714
Kandipalli, T., & Vaisakh, K. (2026). Application of Mixed Strategist Dynamics and Grid‑Oriented Optimization for Evaluation of Plug‑in Electric Vehicle Load Scheduling. New Energy Exploitation and Application, 5(1), 29–43. https://doi.org/10.54963/neea.v5i1.1714
Volume 5 Issue 1: March 2026
Copyright (c) 2026 Tejasri Kandipalli, Kanchapogu Vaisakh
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Authors

  • Tejasri Kandipalli

    Department of Electrical Engineering, College of Engineering, Andhra University, Visakhapatnam 530003, India
  • Kanchapogu Vaisakh

    Department of Electrical Engineering, College of Engineering, Andhra University, Visakhapatnam 530003, India

Received: 19 October 2025; Revised: 19 December 2025; Accepted: 26 December 2025; Published: 6 February 2026

This paper presents an enhanced and comprehensive framework for optimal scheduling of Plug-in Electric Vehicle (PEV) charging by integrating Mixed Strategist Dynamics (MSD) with Forward Dynamic Programming (FDP) to achieve both user-level fairness and grid-oriented optimization. The MSD mechanism generates probabilistic charging strategies that distribute demand across time slots while incorporating equity-based payoff functions to prevent synchronized charging peaks. Building on these probabilistic schedules, an FDP-based deterministic refinement layer is introduced to ensure accurate State-of-Charge (SoC) fulfilment, minimize operating cost, and satisfy grid operational constraints. To ensure technical feasibility, the proposed hybrid MSD–FDP approach is validated on the IEEE 34-bus radial distribution system using Backward/Forward-Sweep (BFS) power-flow analysis. A voltage-penalty cost component is incorporated to restrict bus-voltage deviations within 0.955–1.05 p.u. and to prevent transformer overloading under high EV penetration. The model also integrates Vehicle-to-Grid (V2G) capability, enabling controlled discharging during peak-load conditions to support voltage recovery and improve feeder stability. Simulation results demonstrate that the proposed hybrid framework achieves substantial improvements over MSD-only scheduling and uncoordinated charging. Peak-load demand is reduced by approximately 27%, and the minimum bus voltage is improved from 0.91 p.u. to 0.958 p.u. Additionally, fairness among EVs is significantly enhanced with entropy values averaging 1.77–1.79, indicating balanced access to charging resources. The findings confirm that coordinated charging with V2G support can effectively transform EV fleets into flexible distributed energy assets while ensuring cost efficiency, technical reliability, and scalability for real-world smart-grid applications.

Keywords:

Plug‑in Electric Vehicles (PEV) Mixed Strategist Dynamics (MSD) Forward Dynamic Programming (FDP) Grid‑Oriented Optimization Vehicle‑to‑Grid (V2G) IEEE 34‑Bus

References

  1. Clement-Nyns, K.; Haesen, E.; Driesen, J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans. Power Syst. 2010, 25, 371–380. DOI: https://doi.org/10.1109/TPWRS.2009.2036481
  2. Clement-Nyns, K.; Haesen, E.; Driesen, J. The impact of vehicle-to-grid on the distribution grid. Electr. Power Syst. Res. 2011, 81, 185–192. DOI: https://doi.org/10.1016/j.epsr.2010.08.007
  3. Zhao, Y.; Wang, C.; Zhang, H.; et al. Optimal vehicle-to-grid scheduling using reinforcement learning and dynamic programming hybrid models. IEEE Trans. Smart Grid 2021, 12, 4071–4083.
  4. Zhang, B.; Bailleul, J. Dynamic programming-based scheduling for electric vehicle charging with tariff constraints. IEEE Trans. Smart Grid 2020, 11, 1356–1366.
  5. Liu, X.; Chen, L.; Wen, J. A deep reinforcement learning approach for coordinated electric vehicle charging and renewable integration. Appl. Energy 2022, 311, 118624.
  6. Ali, F.; Khan, M.A.; Ahmad, A. Forward dynamic programming-based energy management for plug-in electric vehicles in smart grids. Electr. Power Syst. Res. 2023, 221, 109513.
  7. Bertsimas, D.P. Dynamic Programming and Optimal Control; Athena Scientific: Belmont, MA, USA, 2012.
  8. Boyd, S.P.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, UK, 2004.
  9. Akagi, H.; Watanabe, E.H.; Aredes, M. Instantaneous Power Theory and Applications to Power Conditioning; IEEE Press/Wiley: Hoboken, NJ, USA, 2007.
  10. Bergen, A.R.; Vittal, V. Power Systems Analysis; Prentice Hall: Upper Saddle River, NJ, USA, 2000.
  11. Kersting, W.H. Distribution System Modelling and Analysis; CRC Press: Boca Raton, FL, USA, 2001.
  12. Gan, L.; Topcu, U.; Low, S.H. Optimal decentralized protocol for electric vehicle charging. IEEE Trans. Power Syst. 2013, 28, 940–951. DOI: https://doi.org/10.1109/TPWRS.2012.2210288
  13. Jin, C.; Tang, J.; Ghosh, P. Optimizing electric vehicle charging: A customer’s perspective. IEEE Trans. Veh. Technol. 2013, 62, 2919–2927. DOI: https://doi.org/10.1109/TVT.2013.2251023
  14. Nguyen, V.L.; Tran-Quoc, T.; Bacha, S.; et al. Charging strategies to minimize the energy cost for an electric vehicle fleet. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, Istanbul, Turkey, 12–15 October 2014.
  15. Dyke, K.J.; Schofield, N.; Barnes, M. The impact of transport electrification on electrical networks. IEEE Trans. Ind. Electron. 2010, 57, 3917–3926. DOI: https://doi.org/10.1109/TIE.2010.2040563
  16. He, Y.; Venkatesh, B.; Guan, L. Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans. Smart Grid 2012, 3, 1095–1105. DOI: https://doi.org/10.1109/TSG.2011.2173507
  17. Bouallaga, A.; Davigny, A.; Courtecuisse, V.; et al. Methodology for technical and economic assessment of electric vehicles integration in distribution grid. Math. Comput. Simul. 2017, 131, 172–189. DOI: https://doi.org/10.1016/j.matcom.2016.05.003
  18. IEEE PES Test Feeders. Available online: https://site.ieee.org/pes-testfeeders/resources/ (accessed on 12 October 2023).
  19. EUROBAT. Available online: https://www.eurobat.org/ (accessed on 12 October 2023).
  20. International Energy Agency (IEA). Global EV Outlook 2016: Beyond One Million Electric Cars; IEA: Paris, France, 2016. Available online: https://iea.blob.core.windows.net/assets/c6fb4849-c171-407e-91de-43d0532c7df9/Global_EV_Outlook_2016.pdf
  21. Sun, Z.; Tang, S.; Zhang, K. Grid-oriented optimization of electric vehicle charging using distributed game theory and FDP techniques. IEEE Trans. Ind. Inform. 2023, 19, 5122–5134.
  22. Kim, H.; Lee, D. Multi-agent dynamic scheduling of electric vehicle fleets considering grid constraints. Energy Rep. 2024, 10, 2762–2775.