In a smart grid power system, reliability performance plays a crucial factor and requires additional focus.Moreover, the integration of Battery Energy Storage (BES) scheme, Solar Photovoltaic (SPV) and wind system in the smart grid system provide significant proficiency and reliability to the utilities. However, the grid coordination with the PV and other resources tends to cause major problems such as power interruption or else power outage. The outage of the power in the grid can cause power loss to the distribution system. Therefore, the novel reliability valuation of smart grid system is developed for exaggeration of the SPV, wind and BES utilities based on the grid incorporation in Saudi Arabia. Furthermore, a novel Hobbled Shepherd Optimization (HSO) for boost converter control and Multi-Objective Based Golden Eagle (MOGE) algorithm for inverter control is proposed. The execution of this work has been done in MATLAB/Simulink. The simulation outcomes show that the projected method has attained the finest Total Harmonic Distortion (THD) and power loss. Also, the optimal reliability improvement has achieved by the projected methods while compared with the conventional methods in terms of Loss of Load Expected (LOLE), Loss of Load Probabilities (LOLP) and Expected Energy Not Supplied (EENS).
Introduction of photovoltaic (PV) systems in grid causes some complex power quality problems such as harmonics, voltage fluctuation, and load imbalance that can affect the grid coordinated PV system performance. Therefore, herein, a novel normalized reasoning‐based fuzzy neural adaptive (NRFNA) control technique is proposed for the double‐stage grid coupled solar PV system. In addition, the utmost power in a double‐stage PV system is tracked at dissimilar environmental states by the proposed novel hybrid krill herd spider monkey (HKHSM)‐based maximum power point tracking (MPPT) algorithm. The current computation control method preserves the grid current in unity power factor as sinusoidal for the system at point common coupling (PCC). The simulation of this proposed work is done on MATLAB/Simulink. Subsequently, the performance of the developed replica results is validated under dissimilar conditions of power quality. Furthermore, the proposed HKHSM‐based MPPT along with a normalized reasoning herd spider monkey (NRHSM) control technique in a grid coordinated solar PV system has achieved 1.70% load current harmonics and 3.7% grid current harmonics. Consequently, the simulation outcomes are compared with the various conventional methods for proving the significance of the proposed system.
The stability performance of smart grid power systems is critical and requires special attention. Additionally, the combination of Battery Energy Storage (BES) systems, Solar Photovoltaic (SPV), and wind systems in the intelligent grid model provides utilities with excellent efficiency and dependability. However, a coordination grid with PV and other resources frequently results in severe issues, such as outages or power disruptions. A power outage in the grid might result in a power loss in the delivery system. As a result, the distributed grid model’s dependable performance is intended for integrated wind energy, SPV arrays, and BE systems. This paper proposes a renewable intelligent grid model to sustain solar power generation. The model incorporates a boost converter to optimize the performance of solar panels by converting the DC power generated by the panels into AC power for use in the grid. The boost converter is optimized using a novel Horse Herd Optimization Algorithm (HOA) method. In this case, the HOA method is used to optimize the control parameters of the boost converter, such as the duty cycle and the inductor and capacitor values. According to the final results, the proposed method has reduced the Total Harmonic Deformation (THD) and power loss. Additionally, the proposed method outperformed existing strategies related to the Expected Energy Not Supplied (EENS), Loss of Load Probability (LOLP), and Loss of Load Expected (LOLE), indicating the sustainability of power generation.
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