In recent years, the importance of deregulated power systems has grown significantly, resulting in positive effects on stability, reliability, innovation, and investment in new energy grid technology. The competitive landscape among energy providers and distributors has empowered consumers to not only save money on their energy bills but also incorporate sustainable energy sources into the grid. To efficiently manage electricity distribution, deregulated power systems must include a smart grid and microgrid (MG). Herein, the potential for sustainable expansion of these systems, as well as their economic and environmental implications, are examined. A comprehensive grid system that integrates smart grids and MGs can offer a complete solution, catering to the evolving energy needs of communities and businesses. The advantages of establishing such a system, including improved grid stability, reliability, and increased utilization of renewable energy sources (RES), are highlighted. Furthermore, the integration of MGs and smart grids enhances the management of distributed generation, allowing power companies to optimize system operations for profitability and efficiency. By following these suggestions, businesses and stakeholders in the power sector can enhance the efficiency and responsibility of their systems, resulting in benefits for both the economy and the environment.

Recently the grid-following inverter-based power generation systems (IBPGSs) dominate over synchronous generators (SGs) in load sharing in some areas of power grids in many countries worldwide. The trend of increasing penetration of IBPGSs to grid results in decreasing number of SGs in operation. This can result in a slowly decreasing system inertia which results in fast dynamics during transient conditions, that may lead to failures of phase-locked loops (PLLs), which are widely used in grid-following IBPGSs. Consequently, the grid-following IBPGS-dominated grids are facing stability issues in their operations. To support these grid-following IBPGSs for their stable operations, a stable grid-forming IBPGS is required that can operate as SGs. In this paper, a grid-forming controller is designed for IBPGSs that can enable IBPGSs to mimic SGs and work as grid-forming inverters that operate like SGs to maintain the grid dynamics suitable for the stable operation of the IBPGSs. For this purpose, a detailed mathematical model of the SG and its associated controller has been used with the proposed grid-forming controller. From the steady-state and transient dynamics, a mathematical relationship between the inertia support from the grid-forming controller and an SG has been developed to control the inverter in the grid-forming IBPGS. The performance of the grid-forming IBPGS during steady-state and transients due to load change, fault, and grid-isolation has been compared with the SG. The simulation results have validated the effectiveness of the proposed controller and grid-forming IBPGSs to help control the voltage and frequency of the IBPGS-dominated power systems.

This article proposes the use of a novel saturated amorphous alloy core-based fault current limiter (SAACFCL) to improve the low voltage ride through (LVRT) capability of doubly-fed induction generator (DFIG) based wind energy systems, especially during voltage sag events. Compared to the traditional cores, which are widely used in fault current limiter (FCL), amorphous alloy core possesses a very narrow B-H loop, which indicates that the SAACFCL requires a smaller dc excitation current, and it will incur low core losses. Under normal conditions, this developed SAACFCL accomplishes low impedance and has a negligible impact on the network operation. During grid faults, a deep voltage sag causes large fault currents that desaturate the SAACFCL core, increasing its impedance, which limits the fault currents and improves the LVRT capability of DFIG systems. The SAACFCL is designed and characterized by ANSYS software. To validate the performance of the SAACFCL, a DFIG-based system equipped with the SAACFCL has been modeled in MATLAB/Simulink. The simulation results with and without the SAACFCL show that the SAACFCL is capable to achieve the LVRT successfully and can meet the grid code requirement. The performance of the SAACFCL has also been compared with that of the saturated iron core FCL (SICFCL) and superconducting FCL (SFCL). The SAACFCL promises better performances than SICFCL and SFCL. A small scale SAACFCL has been designed, developed, and tested in the laboratory to check its performance. The experimental results show that the proposed SAACFCL can effectively reduce the level of fault current and mitigate voltage sag experienced by the DFIG.

In this article, a new saturated amorphous alloy core-based inrush current limiter (ICL) has been designed and built to limit the inrush current of a power transformer. A toroidal core with a middle limb is developed using the Metglas amorphous alloy and the middle limb is excited with a dc current to saturate the amorphous alloy core. Only a very small dc excitation current is required using such a core when compared to that using a similar design with conventional materials. Under normal conditions, the saturated amorphous alloy ICL core steady-state impedance is low and has a negligible effect on the operation of the system. During the energization of a power transformer connected to the ICL, the large inrush current desaturates the amorphous alloy core, resulting in an increase in the ac winding impedance, which limits the peak amplitude of the transformer inrush current and harmonics. To validate the proposed saturated amorphous alloy core-based ICL, a prototype ICL has been built and tested in the laboratory. The experimental results show that the core power loss, core saturation, the fundamentals, and harmonics of the inrush current of the proposed saturated amorphous alloy core-based ICL are significantly better compared with those from the sheet steel core-based ICL. It is expected that the proposed saturated amorphous alloy core will be used in future inrush current limiters that can help to improve the performance of the power transmission and distribution system.

Electric vehicles (EVs) are pivotal in future transportation due to their energy efficiency and environmental sustainability. However, their stochastic load characteristics on the radial distribution network (RDN) pose challenges such as power quality issues and increased network losses. This paper proposes an approach to strategically deploy EV charging stations (EVCS) integrated with photovoltaic (PV) units in RDN. The main objective is to reduce real power loss and enhance the reliability indices. The scenarios that include and exclude PV units have been considered to analyze the impact of introducing PV in the RDN with different EVCS loads. The capacity of the PV unit has also been optimized in this work. The proposed methodology has been assessed by using the IEEE 69 bus RDN. The IEEE 69 bus RDN has been divided into four zones and each zone consists of one EVCS and one PV to ensure widespread charging facilities and improve the network performance. The backward forward sweep power flow technique has been applied to perform the analysis by considering the variable parameters of the electrical system. The issues have been framed as an optimization task and tackled by applying the symbiotic organisms search technique. The outcomes have been compared with Grey Wolf Optimizer, particle swarm optimization (PSO), and whale optimization algorithm (WOA) to validate the effectiveness of the optimal planning to allocate the EVCS and PV units. The methodology is validated through multiscenario optimization on the IEEE 69-bus system and a case study-based static and dynamic simulation on the IEEE 13-bus feeder. These simulations, conducted using MATLAB and OpenDSS, confirm both the steady-state accuracy and the real-time operational feasibility of the proposed planning strategy.

The increasing integration of renewable energies into electrical grids necessitates accurate forecasting of meteorological variables, particularly solar irradiance. This study presents a novel long-term solar irradiance forecasting approach, utilizing meteorological data from the National Renewable Energy Laboratory spanning 1988–2022. Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this study evaluates the predictive performance of 13 data-driven models, comprising ten machine learning (ML) and three deep learning (DL) algorithms. Among them, gradient boosting regressor (GBR) and recurrent neural network (RNN) emerged as top performers in ML and deep learning, respectively. In order to choose the most suitable model for the long and short term, four forecast time-horizons (1, 8, 16, and 24 h) were also taken into consideration for the accurate models. A feature selection process using Pearson’s coefficient identified the most relevant inputs, while quantile regression was employed for uncertainty assessment, mean prediction interval, and prediction interval coverage probability models. This study demonstrates that RNN excels in short-term predictions, while GBR is more effective for long-term forecasts. A new hybrid approach GBR-RNN model was developed, achieving superior performance in terms of RMSE, MAE, and R2 metrics. This multi-model approach, integrating both ML and DL techniques, enhances solar irradiance forecasting by addressing input uncertainty and considering various forecast horizons. The findings contribute to the ongoing advancement of renewable energy forecasting by providing robust, accurate, and uncertainty-aware predictive models. Moreover, this approach helps identify the best-performing model, enabling more reliable and precise solar irradiance forecasts for energy management. This highlights both the improvement in forecasting methods and the importance of selecting the best model for accuracy.