Intelligent adaptive energy management framework for hybrid solar-grid EV fast-charging stations with energy storage and time-based demand response optimization

Abstract

The rapid expansion of electric vehicle (EVs) is significantly increasing pressure on the conventional electrical grid and introducing new challenges in load management, peak demand, and grid stability. Conventional grid systems, dependent on fossil fuel-based generation, contribute to greenhouse gas emissions and limit the environmental benefits of EV adoption. Although several studies have integrated photovoltaic (PV) systems with EV charging networks, most have not addressed real-time coordination between multiple power sources, time-of-use (ToU) pricing, and dynamic demand response (DR). To overcome these limitations, this paper proposes an Intelligent Adaptive Energy Management Framework (IAEMF) for a 1 MW hybrid solar-grid EV DC fast-charging station equipped with a 600-kWh energy storage system (ESS). The framework incorporates a time-adaptive multi-mode control algorithm that dynamically allocates power among PV generation, ESS, and the utility grid based on irradiance, electricity pricing, and load demand. The proposed system supports five operational modes PV-to-ESS, PV-to-EV, Grid-to-EV, ESS-to-EV, and PV-to-Grid facilitating fully bidirectional energy exchange across the DC bus. A variable-step Perturb and Observe (P&O) MPPT algorithm is implemented to maximize solar energy extraction, while the adaptive allocation strategy continuously adjusts PV distribution factors according to ESS state-of-charge (SOC) and demand variations. Time-of-use (ToU) pricing enables cost-effective energy management by shifting grid charging to off-peak periods and the demand response (DR) enables priority-based scheduling during peak periods by balancing user urgency, PV utilization, and grid cost simultaneously. MATLAB/Simulink simulations demonstrate that the proposed IAEMF achieves 87.8% renewable energy utilization, a grid export-to-import ratio of 4.2:1, and 71.8% average daily solar contribution, while maintaining stable DC bus voltage and efficient ESS utilization. Furthermore, the proposed system achieves CO₂ emission reduction of 659.5 tonnes/year by minimizing through grid imports. In addition, the total PV generation-based CO₂ offset reaches 1,853 tonnes per year. The results validate that the proposed hybrid architecture provides a scalable and sustainable solution for next-generation EV fast-charging infrastructure.