Advancing Electric Vehicle Charging Ecosystems With Intelligent Control of DC Microgrid Stability

Abstract

The increasing adoption of renewable energy sources (RES), such as solar photovoltaics and wind turbines, is transforming electricity generation. However, integrating RES within DC microgrids (DCM) for applications such as fast DC charging in electric vehicles (EVs) presents challenges, including low inertia, power fluctuations, and voltage instability. This study addresses these challenges with novel control strategies and optimization algorithms. A hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach is used to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. In addition, a comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. Crucially, a hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging.