Optimized Power Flow and Efficient Load Sharing in DC Microgrid Systems

Abstract

Existing power management strategies for interconnected DC microgrids often face limitations such as poor adaptability to nonlinear dynamics, inefficient handling of renewable intermittency, and slow convergence of optimization-based controllers, which hinder stable bidirectional power sharing and voltage regulation. To address these gaps, this work proposes an Adaptive Neuro-Fuzzy Inference System with Dingo Optimizer (ANFIS-DO) for intelligent power flow management in DC microgrids integrating PV generation, battery energy storage, and a Dual Active Bridge (DAB) converter. The PV subsystem with MPPT and boost converter maximizes energy harvesting under variable irradiance, while the DAB ensures efficient bidirectional transfer and voltage stability. ANFIS models complex nonlinear relationships among inputs such as bus voltage error, power mismatch, PV variation, state of charge, and duty cycle to generate optimized control signals. The Dingo Optimizer fine-tunes ANFIS parameters by mimicking encircling, hunting, and attacking behaviors, thus balancing exploration and exploitation for faster convergence. Simulation results validate that ANFIS-DO achieves minimal tracking errors (RMSE 0.08 kW), balanced grid load sharing (48% vs. 44%), and superior computational efficiency (1.8 s, 65 iterations), consistently outperforming Firefly Algorithm (FFA) and Whale Optimization Algorithm (WOA), thereby establishing its effectiveness in adaptive, stable, and reliable DC microgrid power management.