This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system’s stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to $99.93\%$.

Off-grid photovoltaic (PV) systems require resilient energy storage to overcome power intermittency. While conventional batteries provide short-term buffering, they struggle to maintain long-term energy management. Green hydrogen emerges as a complementary solution, serving both as a scalable long-term storage medium and a clean energy carrier. However, maximizing hydrogen generation remains challenging due to the variability of solar irradiance and load demand. This paper proposes a stochastic optimal control strategy for autonomous PV systems combining battery storage with a proton exchange membrane (PEM) electrolyzer. The energy management problem is formulated as a Markov decision process (MDP), enabling real-time decisions under uncertain operating conditions. The controller prioritizes load supply, regulates battery state of charge (SoC), and allocates surplus PV energy to hydrogen production. Simulation results demonstrate that the proposed MDP-based optimal controller significantly outperforms the conventional perturb-and-observe (P&O) method. In particular, it has a reduced average tracking error of 0.344 instead of 7.517, an improved energy conversion efficiency of 99.9% compared to 96.5%, and converges to the optimal operating point within 0.1s instead of 0.5s. These improvements enhance energy utilization, stabilize hydrogen output, and extend battery lifespan. Overall, the findings confirm the effectiveness of stochastic control for optimizing hydrogen production in standalone PV microgrids, reinforcing their role in advancing the clean energy transition.