Islanded microgrids, powered by renewable energy sources, offer a sustainable electricity solution for remote areas. However, maintaining frequency stability in these systems remains a challenge due to the intermittent nature of renewables. This research proposes an approach to enhance microgrid stability by integrating a green hydrogen energy storage system (GHESS) and employing advanced AI-based control strategies. The GHESS plays a pivotal role in storing excess renewable energy as hydrogen and then converting it back to electricity when needed, reducing reliance on traditional backup generators. To optimize microgrid performance, a hybrid single-neuron PID (SNPID) controller, augmented by machine learning techniques, is developed and compared against conventional proportional, integral, and derivative PID and fuzzy self-tuning PID (FSTPID) controllers. The system’s performance was evaluated using four realistic scenarios. In all cases, the SNPID controller significantly outperformed the existing options. It achieved a 58% reduction in frequency fluctuations compared to the fuzzy self-tuning PID (FSTPID) controller and an impressive 87% reduction compared to the traditional PID controller. The Simulation results underscore the SNPID controller’s exceptional performance in frequency stability, emphasizing the transformative potential of AI for microgrid management.

Two interconnected AC microgrids are proposed based on three renewable energy sources (RESs): wind, solar, and biogas. The wind turbine drives a permanent magnet synchronous generator (PMSG). A solar photovoltaic system (SPVS) with an appropriate inverter was incorporated. The biogas genset (BG) consists of a biogas engine coupled with a synchronous generator. Two interconnected AC microgrids, M₁ and M₂, were considered for study in this work. The microgrid M₂ is connected to a diesel engine (DE) characterized by a continuous power supply. The distribution power loss of the interconnected AC microgrids comprises in line loss. The M₁ and M₂ losses are modeled as an objective function (OF). The power quality enhancement of the interconnected microgrids will be achieved by minimizing this OF. This research also created a unique frequency control method called virtual inertia control (VIC), which stabilizes the microgrid frequency using an optimal controller. In this paper, the following five controllers are studied: a proportional integral controller (PI), a fractional order PI controller (FOPI), a fuzzy PI controller (FPI), a fuzzy fractional order PI controller (FFOPI), and a VIC based on FFOPI controller. The five controllers were tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, step changes in wind/solar input power, and eventually grid following/forming contingencies as well as the virtual inertia control of renewable energy resources used in the structure of the microgrids.

Cleaning frequency of the solar PV power system plays a major role in energy harvesting. This paper proposed an optimized cleaning frequency for the PV power plants. The objective is to maximize energy harvesting and increase financial profit. The proposed technique is tested in roof top grid connected 20 kW PV power plant in Cairo, Egypt. Experimental results are collected for one year throw the period from January 2021 to December 2021 based on real time monitoring under the regular cleaning of the PV plant every fifteen days. The collected data and results are used for deriving a formula to find the optimal frequency for cleaning the PV cells. This formula can be used for any PV power plant under different operating conditions. Therefore, the formula can detect the financial losses due to the dust accumulated on the surfaces of PV panels. The model proposed in this research is verified by the comparison between the generated power from the photovoltaic power plant and the cost of cleaning (water and cleaning workers) in case of manual cleaning, taking into consideration the various operating conditions though whole the period of the study.