Helmy M. El Zoghby

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.

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.

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids' dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC's success in showing remarkable improvement in the microgrid's dynamic response and enhancement of its stability. Moreover, it handles different contingency conditions successfully by giving the desired frequency support. Ant colony optimization (ACO) technique is used to find the optimal values of the RROSTF-PID based on VIC parameters. Furthermore, using MATLAB TM/Simulink, RROSTF-PID based on VIC response is compared to Optimal Fuzzy PID (OF-PID) based VIC, Fuzzy PID (F-PID) based VIC, PID-based VIC, conventional VIC responses, and the microgrid without VIC response under different operation conditions.

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.

Modern energy infrastructures may face critical impacts on distributed generation and microgrids in presence of renewable and conventional energy sources. Fast restorations for these networks through proposing convenient proactive protection systems become mandatory for securing energy particularly after severe faults. This paper deals with presenting a descriptive modelling and comprehensive analysis of both steam and wind turbines using optimal real time emulators with unique testbench. Based on the dynamics of each turbine, both emulators are performed using 4kW, 180V, 1500r.p.m separately exited DC motor coupled to 2kW, 380V, 50Hz, 1500r.p.m three-phase synchronous generator. For real-time interface implementation, the mathematical models of steam and wind turbines are realized using LabVIEW^TM software. The characterization and verification of both emulated steam and wind turbines are examined at different normal operating conditions in terms of steam valve position and wind speed, respectively. To regulate the current for both systems despite their diverse dynamics, a simple industrial proportional-integral (PI) controller is considered. Unlike other artificial intelligence-based controllers, the offline-controller gains are scheduled using genetic algorithm (GA) via Matlab^TM software to ensure the due fast response to cope with unexpected faults. The experimental validity of both emulators is tested at the most severe abnormal operating conditions. The three-phase short circuit is considered at the generator terminals with different fault periods until reaching out-of-step conditions. From numerical analysis and experimental results, the characterization of both emulated steam and wind turbines explicitly mimics their real large-scale turbines in normal conditions. The emulators’ fast responses using the proposed GA-PI control approach are verified. Besides, the experimental dynamic behavior convergence and interoperability between the emulated and real systems for both steam and wind turbines are validated under severe conditions. The practical results confirm the fast-nature performance of the GA in avoid risky instability conditions.

In this paper Static Synchronous Compensator (STATCOM) is used for improving the performance of the power grid with wind turbine that drives synchronous generator. The main feature of the STATCOM is that it has the ability to absorb or inject rapidly reactive power to grid. Therefore the voltage regulation of the power grid with STATCOM device is achieved. STATCOM also improves the stability of the power system after occurring severe disturbance such as faults, or suddenly step change in wind speed. The proposed STATCOM controller is a Proportional-Integral (PI) controller tuned by Genetic Algorithm (GA). An experimental model was built in Helwan University to the proposed system. The system is tested at different operating conditions. The experimental results prove the effectiveness of the proposed STATCOM controller in damping the power system oscillations and restoring the power system voltage and stability.