Review Paper on Load Demand Management Optimization for Accommodating Electric Vehicle

Authors

  • Km Seema Maurya Azad Institute of Engineering & Technology, Lucknow
  • Dr. Imran Khan Azad Institute of Engineering & Technology, Lucknow
  • Dr Malik Rafi RR Institute of Engineering & Technology, Lucknow

Keywords:

FAME, DSM, electric vehicles, smart grid, CIAS based DSM

Abstract

 The shift of transportation technology from internal combustion engine (ICE) based vehicles to electric vehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency has brought EV technology to the forefront of the electric power distribution systems due to their ability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoption of EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillary service-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenario by incorporating demand-side management (DSM) through EV integration. The integration of EVs with DSM techniques is hurdled with various issues and challenges addressed throughout this literature review. The various research conducted on EV-DSM programs has been surveyed. This review article focuses on the issues, solutions, and challenges, with suggestions on modeling the charging infrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separately to enhance the EV-DSM operation. Gaps in current research and possible research directions have been discussed extensively to present a comprehensive insight into the current status of DSM programs employed with EV integration. This extensive review of EV-DSM will facilitate all the researchers to initiate research for superior and efficient energy management and EV scheduling strategies and mitigate the issues faced by system uncertainty modeling, variations, and constraints.

References

Ayub S., Ayob S. Md, Tan C. W, Ayub L, and Bukar A. L, “Optimal residence energy management with time and device-based preferences using an enhanced binary grey wolf optimization algorithm,” Sus-tainable Energy Technologies and Assessments, vol. 41, p. 100798, Oct. 2020, https://doi.org/10. 1016/j.seta.2020.100798

Shami T. M., Grace D., Burr A., and Mitchell P. D., “Single candidate optimizer: a novel optimization algorithm,” Evol Intell, Aug. 2022, https://doi.org/10.1007/s12065-022-00762-7

Sharma R. and Gopal M., “Synergizing reinforcement learning and game theory—A new direction for control,” Appl Soft Comput, vol. 10, no. 3, pp. 675–688, Jun. 2010, https://doi.org/10.1016/j.asoc.2009. 10.020

Macedo M. N. Q., Galo J. J. M., de Almeida L. A. L., and Lima A. C. de C, “Demand side management using artificial neural networks in a smart grid environment,” Renewable and Sustainable Energy Reviews, vol. 41, pp. 128–133, Jan. 2015, https://doi.org/10.1016/j.rser.2014.08.035

Shehab M. et al., “A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization,” Archives of Computational Methods in Engineering, vol. 30, no. 2, pp. 765–797, Mar. 2023, https://doi.org/10.1007/s11831-022-09817-5 PMID: 36157973

Dehkordi A. A., Sadiq A. S., Mirjalili S., and Ghafoor K. Z., “Nonlinear-based Chaotic Harris Hawks Opti-mizer: Algorithm and Internet of Vehicles application,” Appl Soft Comput, vol. 109, p. 107574, Sep. 2021, https://doi.org/10.1016/j.asoc.2021.107574

Abdollahzadeh B., Gharehchopogh F. S., and Mirjalili S., “African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems,” Comput Ind Eng, vol. 158, p. 107408, Aug. 2021, https://doi.org/10.1016/j.cie.2021.107408

Jahangiri M., Hadianfard M. A., Najafgholipour M. A., Jahangiri M., and Gerami M. R., “Interactive auto-didactic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems,” Comput Struct, vol. 235, p. 106268, Jul. 2020, https://doi.org/10.1016/j. compstruc.2020.106268

Yang X.-S., “Cuckoo Search,” in Nature-Inspired Optimization Algorithms, Elsevier, 2014, pp. 129– 139. https://doi.org/10.1016/B978-0-12-416743-8.00009–9

Li S., Chen H., Wang M., Heidari A. A., and Mirjalili S., “Slime mould algorithm: A new method for sto-chastic optimization,” Future Generation Computer Systems, vol. 111, pp. 300–323, Oct. 2020, https:// doi.org/10.1016/j.future.2020.03.055

Castillo V. Z., de Boer H.-S., Muñoz R. M., Gernaat D. E. H. J., Benders R., and van Vuuren D., “Future global electricity demand load curves,” Energy, vol. 258, p. 124741, Nov. 2022, https://doi.org/10.1016/ j.energy.2022.124741

Ho J. C. and Huang Y.-H. S., “Evaluation of electric vehicle power technologies: Integration of techno-logical performance and market preference,” Cleaner and Responsible Consumption, vol. 5, p. 100063, Jun. 2022, https://doi.org/10.1016/j.clrc.2022.100063

Savari G. F. et al., “Assessment of charging technologies, infrastructure and charging station recom-mendation schemes of electric vehicles: A review,” Ain Shams Engineering Journal, vol. 14, no. 4, p. 101938, Apr. 2023, https://doi.org/10.1016/j.asej.2022.101938

Rodrigues F., Cardeira C., Calado J. M. F., and Melicio R., “Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review,” Energies (Basel), vol. 16, no. 10, p. 4098, May 2023, https://doi.org/10.3390/en16104098

Bibak B. and Tekiner-Mogulkoc H., “The parametric analysis of the electric vehicles and vehicle to grid system’s role in flattening the power demand,” Sustainable Energy, Grids and Networks, vol. 30, p. 100605, Jun. 2022, https://doi.org/10.1016/j.segan.2022.100605

Omar A. I., Sharaf A. M., Shady H A. Abdel E, Mohamed A. A, and Essam E. A. E.-Z, “Optimal Switched Compensator for Vehicle-to-Grid Battery Chargers Using Salp Optimization,” in 2019 21st International Middle East Power Systems Conference (MEPCON), IEEE, Dec. 2019, pp. 139–144. https://doi.org/ 10.1109/MEPCON47431.2019.9008229

Wen L., Zhou K., Feng W., and Yang S., “Demand Side Management in Smart Grid: A Dynamic-Price-Based Demand Response Model,” IEEE Trans Eng Manag, pp. 1–30, 2022, https://doi.org/10.1109/ TEM.2022.3158390

Awad M., Ibrahim A. M., Alaas Z. M., El-Shahat A., and Omar A. I., “Design and analysis of an efficient photovoltaic energy-powered electric vehicle charging station using perturb and observe MPPT algo-rithm,” Front Energy Res, vol. 10, Aug. 2022, https://doi.org/10.3389/fenrg.2022.969482

Downloads

Published

2026-02-17

How to Cite

Km Seema Maurya, Dr. Imran Khan, & Dr Malik Rafi. (2026). Review Paper on Load Demand Management Optimization for Accommodating Electric Vehicle. iJournals:International Journal of Software & Hardware Research in Engineering ISSN:2347-4890, 14(2). Retrieved from https://ijournals.in/journal/index.php/ijshre/article/view/402