Comprehensive framework for smart residential demand side management with electric vehicle integration and advanced optimization techniques
Status PubMed-not-MEDLINE Language English Country Great Britain, England Media electronic
Document type Journal Article
Grant support
CZ.10.03.01/00/22_003/0000048
European Union
CZ.10.03.01/00/22_003/0000048
European Union
CZ.10.03.01/00/22_003/0000048
European Union
CZ.10.03.01/00/22_003/0000048
European Union
TN02000025
National Centre for Energy II
TN02000025
National Centre for Energy II
TN02000025
National Centre for Energy II
TN02000025
National Centre for Energy II
101139527
ExPEDite (European Union's Horizon Mission Programme)
101139527
ExPEDite (European Union's Horizon Mission Programme)
101139527
ExPEDite (European Union's Horizon Mission Programme)
101139527
ExPEDite (European Union's Horizon Mission Programme)
PubMed
40121233
PubMed Central
PMC11929861
DOI
10.1038/s41598-025-93817-5
PII: 10.1038/s41598-025-93817-5
Knihovny.cz E-resources
- Keywords
- Demand side management (DSM), Electric vehicles (EVs), Home-to-vehicle (H2V), Residential demand side management (RDSM), Vehicle-to-home (V2H), Vehicle-to-vehicle (V2V),
- Publication type
- Journal Article MeSH
The exponential deployment of electric vehicles (EVs) in the residential sectors in recent years allows better energy utilization in the decentralized and centralized levels of distribution systems due to their bidirectional operation and energy storage capabilities. However, to execute these, it is necessary to adopt residential demand side management (RDSM) to schedule energy utilization effectively to fetch economical and efficient energy consumption and grid stability and reliability, particularly during peak load conditions. The paper aims to formulate a robust and efficient RDSM technique to provide an energy utilization scheduling considering various influential factors and critical roles of EVs in RDSM. A Binary Whale Optimization Algorithm (BWOA) approach is proposed as an efficient algorithm for EV's impact on the RDSM for better energy scheduling. A single-objective formulation is presented with detailed modelling considering economic energy utilization as the primary objective with all possible equality and inequality system operational constraints. Secondly, the impact of EVs on the RDSM is studied from various perspectives in result analysis, considering EVs as load, storage devices, and different bidirectional modes of operation with other vehicles, residential components, and grids. In addition, the EVs role and the mutual influence with the integration of renewable energy sources (RES) and energy storage devices (ESDs) are extensively analyzed to provide better residential energy management (REM) in terms of economic, environmental, robust, and reliable points of view. The load priority based on consumer choice is also incorporated in the formulation. Extensive simulation is done for the proposed approach to show the effect of EVs on REM, and the results are impressive to show the EV's role as a load, as a storage device, and as a mutually supportive device to RES, ESD, and grid.
College of Engineering University of Business and Technology Jeddah 21448 Saudi Arabia
Department of Computer Science Engineering Siksha 'O' Anusandhan University Bhubaneswar Odisha India
Department of Electrical and Electronics Engineering SR University Warangal 506371 Telangana India
Department of Electrical Engineering Graphic Era Dehradun 248002 India
Department of Electrical Engineering Siksha 'O' Anusandhan University Bhubaneswar Odisha India
Department of Electrical Engineering Srinix College of Engineering Gopalgoan Odisha India
ENET Centre CEET VSB Technical University of Ostrava Ostrava 708 00 Czech Republic
Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan
See more in PubMed
Panda, S. et al. Residential demand side management model, optimization and future perspective: A review. Energy Rep.8, 3727–3766 (2022).
Karuppiah, N., Mounica, P., Balachandran, P. K. & Muniraj, R. Critical Review on Electric Vehicles: Chargers, Charging Techniques, and Standards81–94 (Renewable Energy for Plug-In Electric Vehicles, 2024).
Nasir, T., Bukhari, S. S. H., Raza, S., Munir, H. M., Abrar, M., Muqeet, H. A. U.,… Masroor, R. (2021). Recent challenges and methodologies in smart grid demand side management: State-of-the-art literature review. Mathematical Problems in Engineering,2021, 1–16.
Al-Ogaili, A. S. et al. Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: challenges and recommendations. IEEE Access.7, 128353–128371 (2019).
Kanakadhurga, D. & Prabaharan, N. Demand side management in microgrid: A critical review of key issues and recent trends. Renew. Sustain. Energy Rev.156, 111915 (2022).
Uddin, M., Romlie, M. F., Abdullah, M. F., Halim, A., Kwang, T. C. & S., & A review on peak load shaving strategies. Renew. Sustain. Energy Rev.82, 3323–3332 (2018).
Huang, Z., Zhou, Y., Lin, Y. & Zhao, Y. Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach. Comput. Ind. Eng.195, 110416 (2024).
Feng, J., Yao, Y. & Liu, Z. Developing an Optimal Building Strategy for Electric Vehicle Charging Stations: Automaker Role1–61 (Environment, Development and Sustainability, 2024).
Nimalsiri, N. I. et al. A survey of algorithms for distributed charging control of electric vehicles in smart grid. IEEE Trans. Intell. Transp. Syst.21 (11), 4497–4515 (2019).
Mohanty, S., Panda, S., Parida, S. M., Rout, P. K., Sahu, B. K., Bajaj, M., Kamel, S. Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization. Energy Rep.8, 12466–12490 (2022).
VikramGoud, M., Biswas, P. K., Sain, C., Babu, T. S. & Balachandran, P. K. Advancement of Electric Vehicle Technologies, Classification of Charging Methodologies, and Optimization Strategies for Sustainable development-a Comprehensive Review (Heliyon, 2024). PubMed PMC
Babu, T. S., Balachandran, P. K. & Nwulu, N. Renewable Energy for Plug-In Electric Vehicles: Challenges, Approaches, and Solutions for Grid Integration. (2024).
Abdulaal, A., Cintuglu, M. H., Asfour, S. & Mohammed, O. A. Solving the multivariant EV routing problem incorporating V2G and G2V options. IEEE Trans. Transp. Electrification. 3 (1), 238–248 (2016).
Datta, U., Saiprasad, N., Kalam, A., Shi, J. & Zayegh, A. A price-regulated electric vehicle charge‐discharge strategy for G2V, V2H, and V2G. Int. J. Energy Res.43 (2), 1032–1042 (2019).
Balachandran, P. K., Nwulu, N. & Babu, T. S. A perspective review of present and future trends of electric vehicle technology. Renew. Energy Plug-In Electr. Veh., 1–10. (2024).
Yi, X., Lu, T., Li, Y., Ai, Q. & Hao, R. Collaborative planning of multi-energy systems integrating complete hydrogen energy chain. Renew. Sustain. Energy Rev.210, 115147 (2025).
Shariff, S. M., Iqbal, D., Alam, M. S. & Ahmad, F. A state of the art review of electric vehicle to grid (V2G) technology. In IOP Conference Series: Materials Science and Engineering (Vol. 561, No. 1, p. 012103). IOP Publishing. (2019).
Nagarajan, K., Rajagopalan, A., Bajaj, M., Raju, V. & Blazek, V. Enhanced Wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems. Results Eng.25, 103671 (2025).
Hafeez, A., Alammari, R. & Iqbal, A. Utilization of EV charging station in demand side management using deep learning method. IEEE Access.11, 8747–8760 (2023).
Shuvo, S. S. & Yilmaz, Y. Demand-side and utility-side management techniques for increasing Ev charging load. IEEE Trans. Smart Grid. 14 (5), 3889–3898 (2023).
Nadimuthu, L. P. R., Victor, K., Bajaj, M. & Tuka, M. B. Feasibility of renewable energy microgrids with vehicle-to-grid technology for smart villages: A case study from India. Results Eng.24, 103474 (2024).
Kumar, B. A. et al. Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience. Sci. Rep.14 (1), 7637 (2024). PubMed PMC
Kanakadhurga, D. & Prabaharan, N. Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources. Appl. Energy. 364, 123062 (2024).
Prum, P., Charoen, P., Khan, M. A., Bayati, N. & Charoenlarpnopparut, C. Energy management scheme for optimizing multiple smart homes equipped with electric vehicles. Energies17 (1), 254 (2024).
Kumar, B. A. et al. A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape. Sci. Rep.14 (1), 3261 (2024). PubMed PMC
Singh, A. R., Vishnuram, P., Alagarsamy, S., Bajaj, M., Blazek, V., Damaj, I., Othman,K. M. Electric vehicle charging technologies, infrastructure expansion, grid integration strategies, and their role in promoting sustainable e-mobility. Alex. Eng. J.105, 300–330 (2024).
Zhang, M., Yan, Q., Guan, Y., Ni, D. & Tinajero, G. D. A. Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties. Energy298, 131370 (2024).
Medeiros, A., Canha, L. N., Garcia, V. J., de Azevedo, R. M. & dos Santos, R. B. Demand side and flexible energy resource management when operating smart electric vehicle charging stations. In Advanced Technologies in Electric Vehicles (363–384). Academic. (2024).
Aggarwal, S., Singh, A. K., Rathore, R. S., Bajaj, M. & Gupta, D. Revolutionizing load management: A novel technique to diminish the impact of electric vehicle charging stations on the electricity grid. Sustain. Energy Technol. Assess.65, 103784 (2024).
Blazek, V., Pergl, I., Prokop, L., Bajaj, M. & Slanina, Z. Energy Efficiency of Vehicle-to-Grid Cycle in Real Application. In 2024 XV International Symposium on Industrial Electronics and Applications (INDEL) (pp. 1–6). IEEE. (2024), November.
Liao, W., Xiao, F., Li, Y., Zhang, H. & Peng, J. A comparative study of demand-side energy management strategies for Building integrated photovoltaics-battery and electric vehicles (EVs) in diversified Building communities. Appl. Energy. 361, 122881 (2024).
Ravindran, M. A. et al. A novel technological review on fast charging infrastructure for electrical vehicles: challenges, solutions, and future research directions. Alexandria Eng. J.82, 260–290 (2023).
Singh, A. R. et al. Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework. Sci. Rep.14 (1), 31768 (2024). PubMed PMC
Dharavat, N. et al. Optimal allocation of renewable distributed generators and electric vehicles in a distribution system using the political optimization algorithm. Energies15 (18), 6698 (2022).
Kumar, P. H. et al. Techno-economic optimization and sensitivity analysis of off-grid hybrid renewable energy systems: A case study for sustainable energy solutions in rural India. Results Eng.25, 103674 (2025).
Rajagopalan, A. et al. Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm. Sci. Rep.14 (1), 15652 (2024). PubMed PMC
Nagarajan, K. et al. Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management. Sci. Rep.14 (1), 3091 (2024). PubMed PMC
Khan, H. W., Usman, M., Hafeez, G., Albogamy, F. R., Khan, I., Shafiq, Z., Alkhammash, H. I. Intelligent optimization framework for efficient demand-side management in renewable energy integrated smart grid. IEEE Access9, 124235–124252 (2021).
Baader, F. J. et al. Mixed-integer dynamic scheduling optimization for demand side management. In Computer Aided Chemical Engineering (Vol. 48, 1405–1410). Elsevier. (2020).
Al-Jabery, K. et al. Demand-side management of domestic electric water heaters using approximate dynamic programming. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.36 (5), 775–788 (2016).
Mohseni, S., Khalid, R. & Brent, A. C. Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: an efficiency comparison of state-of-the-art metaheuristics. Appl. Energy. 341, 121007 (2023).
Ravibabu, P., Praveen, A., Chandra, C. V., Reddy, P. R. & Teja, M. K. R. An approach of DSM techniques for domestic load management using fuzzy logic. In 2009 IEEE International Conference on Fuzzy Systems (pp. 1303–1307). IEEE. (2009), August.
Abedrabboh, K. & Al-Fagih, L. Applications of mechanism design in market-based demand-side management: A review. Renew. Sustain. Energy Rev.171, 113016 (2023).
Muratori, M. & Rizzoni, G. Residential demand response: dynamic energy management and time-varying electricity pricing. IEEE Trans. Power Syst.31 (2), 1108–1117 (2015).
Panda, S., Mohanty, S., Rout, P. K., Sahu, B. K., Parida, S. M., Kotb, H., Shouran, M. An insight into the integration of distributed energy resources and energy storage systems with smart distribution networks using demand-side management. Appl. Sci.12(17), 8914 (2022).
Korkas, C. D., Terzopoulos, M., Tsaknakis, C. & Kosmatopoulos, E. B. Nearly optimal demand side management for energy, thermal, EV and storage loads: an approximate dynamic programming approach for smarter buildings. Energy Build.255, 111676 (2022).
Bayrak, G. & Meral, H. A new artificial intelligence-based demand side management method for EV charging stations. In Intelligent Learning Approaches for Renewable and Sustainable Energy (31–45). Elsevier. (2024).
Ma, K., Yang, J. & Liu, P. Relaying-assisted communications for demand response in smart grid: cost modeling, game strategies, and algorithms. IEEE J. Sel. Areas Commun.38 (1), 48–60 (2019).
Ma, K., Yu, Y., Yang, B. & Yang, J. Demand-side energy management considering price oscillations for residential Building heating and ventilation systems. IEEE Trans. Industr. Inf.15 (8), 4742–4752 (2019).
Wang, W., Xie, R. K. & Ding, L. Stability Analysis of Load Frequency Control Systems with Electric Vehicle Considering time-varying Delay (IEEE Access, 2024).
Finn, P., Fitzpatrick, C. & Connolly, D. Demand side management of electric car charging: benefits for consumer and grid. Energy42 (1), 358–363 (2012).
Javaid, N. et al. An intelligent load management system with renewable energy integration for smart homes. IEEE Access.5, 13587–13600 (2017).
Ahmad, A., Khan, A., Javaid, N., Hussain, H. M., Abdul, W., Almogren, A., Azim Niaz, I. An optimized home energy management system with integrated renewable energy and storage resources. Energies10(4), 549 (2017).
Mirjalili, S. et al. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv. Eng. Softw.114, 163–191 (2017).
Ibrahim, R. A., Ewees, A. A., Oliva, D., Abd Elaziz, M. & Lu, S. Improved salp swarm algorithm based on particle swarm optimization for feature selection. J. Ambient Intell. Humaniz. Comput.10, 3155–3169 (2019).
Tubishat, M. et al. Dynamic salp swarm algorithm for feature selection. Expert Syst. Appl.164, 113873 (2021).
Mirjalili, S. & Lewis, A. The Whale optimization algorithm. Adv. Eng. Softw.95, 51–67 (2016).
Hussien, A. G., Hassanien, A. E., Houssein, E. H., Amin, M. & Azar, A. T. New binary Whale optimization algorithm for discrete optimization problems. Eng. Optim.52 (6), 945–959 (2020).
Panda, S., Mohanty, S., Rout, P. K. & Sahu, B. K. A conceptual review on transformation of micro-grid to virtual power plant: issues, modeling, solutions, and future prospects. Int. J. Energy Res.46 (6), 7021–7054 (2022).
Panda, S. et al. A comprehensive review on demand side management and market design for renewable energy support and integration. Energy Rep.10, 2228–2250 (2023).