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.

This article investigates the stabilization problem of a nonlinear networked control system (NCS) exposed to a replay attack. A new mathematical model of the replay attack is proposed. The resulting closed-loop system is defined as a discrete-time Markovian jump linear system (MJLS). Employing the Lyapunov–Krasovskii functional, a sufficient condition for stochastic stability is given in the form of linear matrix inequalities (LMIs). The control law can be obtained by solving these LMIs. Finally, a simulation of an inverted pendulum (IP) with Matlab is developed to illustrate our controller’s efficiency.

Integrating a photovoltaic (PV) microgrid system with wireless network control heralds a new era for renewable energy systems. This fusion capitalizes on the strengths of photovoltaic technology, leveraging solar energy for electricity generation while incorporating advanced networked control capabilities. Although employing network communication to facilitate information exchange among system elements offers benefits, it also introduces novel challenges which can hinder fault diagnosis, such as packet loss and communication delay. This paper focuses on a cloud-based fault detection approach for an effective boost converter within a photovoltaic system. Faults are diagnosed using a detection algorithm based on the Lyapunov function, ensuring power optimization. The effectiveness of our approach is demonstrated through simulations of a PV generator model utilizing real-time weather data collected in Brazil, illustrating its robustness through the acquired results.