This paper introduces a novel energy management strategy incorporating stochastic elements designed for off-grid photovoltaic (PV) systems supplying multiple loads in environments marked by unpredictable power usage. In these PV microgrid applications, unpredictable power consumption can lead to discrepancies between energy supply and demand, compromising system reliability and efficiency. This issue is especially pertinent in providing reliable electricity to remote or rural areas where conventional grid infrastructure is not available or reliable. To overcome this challenge, this paper addresses the random variability in load consumption by modeling it as a Markov decision process (MDP). The MDP framework facilitates the development of an effective decision-making process, accounting for the probabilistic nature of energy consumption patterns. Furthermore, by integrating MDP-based load consumption prediction into the energy management system, real-time optimization of both PV power and battery charging and discharging within the microgrid is achieved. This integration balances energy production and consumption, enhancing overall system efficiency. Three scenarios were examined to evaluate the effectiveness of the suggested strategy in enhancing the real-time operation of off-grid PV systems: standard test conditions, time-varying climatic profiles, and real-time weather situations. The findings indicate that the proposed strategy can adapt to dynamic load profiles, ensuring efficient energy utilization while maintaining microgrid stability.

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments.

This paper presents a stabilization control for positive, Takagi-Sugeno fuzzy systems subject to Markov jump parameters. In the continuous-time formulation, the approach guarantees mean-square stability with constraints on the control—the main condition hinges upon linear matrix inequalities. The proposed method’s usefulness is illustrated by a practical-oriented example, which was designed to control the output voltage of a DC-DC boost converter subject to both voltage and load variations driven by a Markov chain.

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 $99.93\%$.