Assessment of a decision tree logic-based power coordination in a DC microgrid under stochastic operational scenarios

The effective coordination of microgrids with diverse energy resources is impeded by various challenges. This includes maintaining DC link voltage stability, optimizing fuel efficiency, and prolonging the lifespan of energy storage systems. In this context, a hierarchical power coordination strategy (PCS) for an autonomous DC microgrid with a fuel cell and a hybrid energy storage system (HESS) comprising a battery and a supercapacitor is presented. At the supervisory level, the optimal power-sharing states are determined using a decision tree (DT) algorithm based on the storage element’s state of charge and instantaneous load demand. The execution layer applies model predictive control to ensure accurate tracking of the reference current. The proposed system with DC loads is simulated in MATLAB/Simulink. Aside from effective power coordination, the results show that, compared with existing control approaches, the DT-MPC framework improves DC-link voltage stability and reduces battery stress. Under diverse source and load variations, DC link voltage is improved in terms of maximum peak overshoot ( M p ), settling time ( t s ), and steady-state error ( e ss ), in contrast to the conventional approach. The DT-MPC consistently achieves highly damped tracking metrics with minimal steady-state error. Furthermore, this improved coordination technique enhances DCM stability while paving the way for hybrid electric vehicle applications to accelerate future growth and innovation through scalable solutions.