Smart Energy Management Using Vehicle-to-Home, Photovoltaics, and AI Optimization Applications in Residential Systems and Cloud Datacenters

Mohannad Al-Kubaisi; Salman Yussof; Moamin A Mahmoud

doi:10.29284/f36cxr23

Authors

Mohannad Al-Kubaisi College of Computing and Informatics, Universiti Tenaga Nasional, Selangor, Malaysia
Salman Yussof College of Computing and Informatics, Universiti Tenaga Nasional, Selangor, Malaysia
Moamin A Mahmoud College of Computing and Informatics, Universiti Tenaga Nasional, Selangor, Malaysia,

DOI:

https://doi.org/10.29284/f36cxr23

Keywords:

Smart Energy Management; Renewable-Aware Scheduling; AI Optimization Algorithms; Vehicle-to-Home (V2H); Seasonal Adaptation; Cloud Datacenter.

Abstract

The effective integration of photovoltaic (PV) generation with Vehicle-to-Home (V2H) technology is crucial for sustainable smart homes but poses a significant optimization challenge due to the complex, constrained, and seasonally-variable nature of the energy scheduling problem. Current optimization methods, including standard metaheuristics, often fail to adapt to the drastic shifts in PV availability between summer and winter, leading to suboptimal year-round performance. To address this critical research gap in seasonal algorithm adaptation, this paper introduces two novel metaheuristic algorithms: Gradient Particle Swarm Optimization (GPSO), which enhances convergence in high-PV conditions via gradient-guided search, and the Geese V-Formation Algorithm (GVFA), a bio-inspired cooperative optimizer designed for robustness during low-generation periods. Using a year of real-world data from Iraq within an integrated V2H-PV-battery model, we demonstrate that algorithm performance is intrinsically seasonal: GPSO reduces daily energy costs by up to 89% in summer, while GVFA achieves a 46.5% reduction in winter compared to rule-based baselines. This key finding, that no single algorithm provides year-round optimality, establishes adaptive, season-aware algorithm selection as an essential paradigm for next-generation energy management systems. The framework is extended with cybersecurity measures and its scalability to cloud datacenters is demonstrated, offering both algorithmic innovation and practical deployment guidance.

References

[1] K. Kaygusuz, "Energy for sustainable development: A case of developing countries," Renewable and sustainable energy reviews, vol. 16, no. 2, pp. 1116-1126, 2012.

[2] S. Ilager and R. Buyya, "Machine learning in energy and thermal-aware resource management of cloud data centers: A taxonomy and future directions," in 2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS), 2024: IEEE, pp. 21-34.

[3] K. Ukoba, K. O. Olatunji, E. Adeoye, T.-C. Jen, and D. M. Madyira, "Optimizing renewable energy systems through artificial intelligence: Review and future prospects," Energy & Environment, vol. 35, no. 7, pp. 3833-3879, 2024.

[4] M. Benaissa, "Optimization of the IT and electrical sizing process of a green, medium-sized datacenter powered exclusively by renewable energy sources, considering environmental and external uncertainty," Université Bourgogne Franche-Comté, 2024.

[5] K. Shao, H. Fu, and B. Wang, "An efficient combination of genetic algorithm and particle swarm optimization for scheduling data-intensive tasks in heterogeneous cloud computing," Electronics, vol. 12, no. 16, p. 3450, 2023.

[6] Z. Yang and F.-Y. Gong, "Utilizing street view images to estimate solar energy potential for photovoltaic-powered buses," Applied Geography, vol. 177, p. 103567, 2025.

[7] S. Charadi, H. E. Chakir, A. Redouane, A. El Hasnaoui, and B. El Bhiri, "A novel hybrid imperialist competitive algorithm–particle swarm optimization metaheuristic optimization algorithm for cost-effective energy management in multi-source residential microgrids," Energies, vol. 16, no. 19, p. 6896, 2023.

[8] B. Beena, C. Prashanth, T. S. S. Manideep, S. Saragadam, and G. Karthik, "A Green Cloud-Based Framework for Energy-Efficient Task Scheduling Using Carbon Intensity Data for Heterogeneous Cloud Servers," IEEE Access, 2025.

[9] I. Keskin and G. Soykan, "Distribution grid electrical performance and emission analysis of combined cooling, heating and power (CCHP)-photovoltaic (PV)-based data center and residential customers," Journal of Cleaner Production, vol. 414, p. 137448, 2023.

[10] A. O. Ali, M. R. Elmarghany, M. M. Abdelsalam, M. N. Sabry, and A. M. Hamed, "Closed-loop home energy management system with renewable energy sources in a smart grid: A comprehensive review," Journal of Energy Storage, vol. 50, p. 104609, 2022.

[11] K. V. Konneh et al., "Optimal design and performance analysis of a hybrid off-grid renewable power system considering different component scheduling, PV modules, and solar tracking systems," IEEE Access, vol. 9, pp. 64393-64413, 2021.

[12] J. Osawa, "Optimizing technological configurations in residential energy systems with vehicle-to-home integration and community-level power sharing," Energy Reports, vol. 12, pp. 3533-3547, 2024.

[13] S. Aguacil, S. Duque, S. Lufkin, and E. Rey, "Designing with building-integrated photovoltaics (BIPV): A pathway to decarbonize residential buildings," Journal of Building Engineering, vol. 96, p. 110486, 2024.

[14] A. O. Maka and T. N. Chaudhary, "Performance investigation of solar photovoltaic systems integrated with battery energy storage," Journal of Energy Storage, vol. 84, p. 110784, 2024.

[15] A. M. Adeyinka, O. C. Esan, A. O. Ijaola, and P. K. Farayibi, "Advancements in hybrid energy storage systems for enhancing renewable energy-to-grid integration," Sustainable Energy Research, vol. 11, no. 1, p. 26, 2024.

[16] M. E. C. Bento, "Load margin assessment of power systems using physics-informed neural network with optimized parameters," Energies, vol. 17, no. 7, p. 1562, 2024.

[17] N. D. Baghbani, H. Zeynali, and A. Karabuga, "Cost Reduction of Integrated Renewable Energy Sources in Home Energy Management Systems (HEMS) using Particle Swarm Optimization," in 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2024: IEEE, pp. 1-5.

[18] C. Menos-Aikateriniadis, I. Lamprinos, and P. S. Georgilakis, "Particle swarm optimization in residential demand-side management: A review on scheduling and control algorithms for demand response provision," Energies, vol. 15, no. 6, p. 2211, 2022.

[19] M. Ayyadurai and R. Harish, "V-Shaped Bird Algorithm (VSBA) For Energy-Efficient and Reliable Underwater Sensor Network," in 2024 International Conference on Emerging Research in Computational Science (ICERCS), 2024: IEEE, pp. 1-6.

[20] R. M. Mohan, S. Pradeepa, and V. Boddapati, "GGO Algorithm: A Nature-inspired Solution for High Renewable Energy Penetration in Grids," International Journal of Intelligent Engineering & Systems, vol. 18, no. 4, 2025.

[21] A. I. Ikram, A. Ullah, D. Datta, A. Islam, and T. Ahmed, "Optimizing energy consumption in smart homes: Load scheduling approaches," IET Power Electronics, vol. 17, no. 16, pp. 2656-2668, 2024.

[22] A. Fawaz, I. Mougharbel, K. Al-Haddad, and H. Y. Kanaan, "Energy routing protocols for Energy Internet: A review on multi-agent systems, metaheuristics, and Artificial Intelligence approaches," IEEE Access, 2025.

[23] Y. Xu, Y. Li, and W. Gao, "Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems," Buildings, vol. 14, no. 9, p. 2645, 2024.

[24] A. Raza, L. Jingzhao, Y. Ghadi, M. Adnan, and M. Ali, "Smart home energy management systems: Research challenges and survey," Alexandria engineering journal, vol. 92, pp. 117-170, 2024.

[25] H. Wu, "Design and Implementation of IoT-Based Smart Home System," in 2024 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC), 2024: IEEE, pp. 7-11.

[26] S. Hira and S. Hira, "Smart energy management using vehicle-to-vehicle and vehicle-to-everything," in Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems: Elsevier, 2024, pp. 253-290.