Hybrid Renewable Energy System for Electric Vehicle Charging Stations with Adaptive Energy Fairness Scheduling in Semi-Urban Microgrids
Abstract
The accelerating global adoption of electric vehicles (EVs) has introduced substantial pressure on electrical infrastructure, particularly in semi-urban and peri-urban regions where grid capacity is constrained and supply reliability remains inconsistent. Addressing this challenge necessitates a departure from conventional, grid-dependent charging paradigms toward architectures that leverage locally available renewable energy resources. This paper presents a comprehensively designed hybrid renewable energy-powered EV charging station tailored for semi-urban microgrid environments. The proposed system integrates solar photovoltaic (PV) arrays, wind turbine generators, a lithium-ion battery energy storage subsystem, and a controlled grid-interconnection module, all governed by an intelligent multi-layer control framework.
Three novel conceptual contributions distinguish this work from prior art. First, a Self-Learning Energy Behaviour Model (SLEBM) continuously assimilates historical data on renewable generation patterns and EV utilisation trends to refine short-horizon demand forecasts. Second, a Carbon-Aware Decision Engine (CADE) quantifies the real-time carbon intensity of each available energy source and constructs a priority dispatch sequence that minimises greenhouse gas emissions. Third, an Energy Fairness Scheduling Index (EFSI) governs charging-slot allocation by simultaneously accounting for vehicle state-of-charge (SoC), queue residence time, and prevailing energy availability, thereby preventing systemic inequities in service delivery. Simulation experiments conducted in MATLAB/Simulink under seasonally varying semi-urban meteorological profiles demonstrate statistically significant improvements in renewable energy utilisation, reductions in grid dependency, diminished carbon output, and equitable charging distribution relative to both grid-only and rule-based hybrid baselines. The architecture is designed for modular scalability and is well-suited for deployment in energy-constrained developing-region contexts.
References
Yilmaz, M., & Krein, P. T. (2013). Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Transactions on Power Electronics, 28(5), 2151–2169.
Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016). Integration of electric vehicles in smart grid: A review on vehicle-to-grid technologies and optimisation techniques. Renewable and Sustainable Energy Reviews, 53, 720–732.
Naderipour, A., Abdul-Malek, Z., Nowdeh, S. A., Ramachandaramurthy, V. K., Kalam, A., & Guerrero, J. M. (2020). Optimal allocation for combined heat and power system with respect to maximum allowable capacity for reduced losses and improved voltage profile and reliability of microgrids considering loading condition. Energy, 196, 117–124.
Diaz, N. L., Hernandez, A. C., Vasquez, J. C., & Guerrero, J. M. (2017). Intelligent distributed generation and storage units for DC microgrids — A new concept on cooperative control without communications beyond droop control. IEEE Transactions on Smart Grid, 5(5), 2476–2485.
Tushar, W., Yuen, C., Huang, S., Smith, D. B., & Poor, H. V. (2016). Cost minimisation of charging stations with photovoltaics: An approach with EV classification. IEEE Transactions on Intelligent Transportation Systems, 17(1), 156–169.
Ioakimidis, C. S., Thomas, D., Rycerski, P., & Genikomsakis, K. N. (2018). Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot. Energy, 148, 148–158.
Wang, Z., & Wang, S. (2013). Grid power peak shaving and valley filling using vehicle-to-grid systems. IEEE Transactions on Power Delivery, 28(3), 1822–1829.
Fathabadi, H. (2017). Novel standalone hybrid solar/wind/fuel cell/battery power generation system. Energy, 140, 454–465.
Refbacks
- There are currently no refbacks.