DC series arc faults (DC SAF) in photovoltaic (PV) systems can lead to electrical fires and electric shock hazards. Therefore, DC SAF modeling and detection is a significant process for ensuring the safety of PV panels and is necessary for producing PV systems in actual applications. Using real data, for the first time, this study presents a DC SAF modeling technique based on machine learning (ML) algorithms. Considering the unpredictable and nonlinear nature of such arcs and the application of ML in solving nonlinear and complex problems, multilayer perceptron, radial basis function, and support vector machine algorithms are used to model DC SAF in PV systems. The performance of proposed ML‐based approaches is compared with well‐known traditional models by using error indices, which are computed using a test data set. Finally, comprehensive evaluations and results of modeling demonstrate that proposed models based on ML methods remarkably improved modeling accuracy and generalization capability in DC SAF modeling.

Integrating electric vehicle (EV)‐charging infrastructure presents environmental advantages, particularly in curbing carbon emissions within the transport sector and promoting sustainable energy solutions. However, the ascending adoption of EVs transforms the operational dynamics of low‐voltage distribution networks by introducing bidirectional power flows that challenge conventional overcurrent protection schemes. Traditional protection systems cannot effectively manage the complexities of variable load conditions and bidirectional energy transfers, specifically Grid‐to‐Vehicle (G2V) and Vehicle‐to‐Grid (V2G) operational modes. These scenarios require the development of advanced, dynamic, and real‐time protection mechanisms that are robust against challenging, faulty scenarios and cybersecurity threats. This study introduces an adaptive protection scheme that utilises digital overcurrent relays, LoRa‐enabled sensors, a battery management system (BMS) and a central protection unit (CPU). This integrated framework dynamically recalibrates relay settings based on real‐time grid conditions, ensuring optimal protection coordination during both G2V and V2G operations by employing a new optimisation algorithm called the transit search algorithm (TSA) and comparing the result to the water cycle algorithm (WCA). To assess the effectiveness of the proposed adaptive approach, simulations were performed on a 33‐bus IEEE benchmark network, investigating a variety of fault scenarios and operation grid scenarios. The results indicate that the proposed system significantly mitigates relay miscoordination and reduces fault clearance durations, thus enhancing reliable protection in distribution networks with high EV penetration.

Wind energy conversion systems (WECSs) require robust and efficient control strategies to ensure optimal energy conversion. This study proposes a nonlinear and resilient control approach using a fractional‐order proportional integral‐ and fractional‐order proportional derivative (FOPI–FOPD) controller for direct power regulation of a doubly fed induction generator (DFIG)–based WECS. To meet the control objectives, two cascaded FOPI–FOPD controllers were designed, resulting in 12 parameters requiring precise tuning. To optimize these parameters, the Gazelle optimization algorithm (GOA) was employed, targeting the minimization of key performance‐based cost functions: mean error (ME), mean absolute error (MAE), mean‐square error (MSE), and integral time absolute error (ITAE). These functions integrate dynamic response criteria such as overshoot, rise time, and settling time. Simulation results highlight the effectiveness of the GOA‐tuned FOPI–FOPD controller, particularly when using ITAE as the optimization criterion. The controller significantly reduces power ripples by 86.13% in active power and 75.66% in reactive power. It also improves transient response by reducing rise time by 0.035 ms, settling time by 0.3 ms, and completely eliminating overshoot. Moreover, the proposed strategies lower the current total harmonic distortion (THD) by approximately 21.43% compared to the basic strategy. The proposed ITAE–GOA–FOPI–FOPD technique ensures system stability and enhances performance across various operating conditions.

The conversion loss is the significant challenge due to the usage of multiple converters at different stages of a power distribution system. These stages include distribution of energy, energy storage, grid integration, and energy demand management. The conversion losses at each stage adversely impacts the performance of the power system, especially toward energy conservation if efforts are made toward it. To address this, a novel microgrid (MG) energy management scheme is introduced to mitigate conversion losses in distribution systems specifically under weak MG environment. This scheme employs a sophisticated control algorithm that assesses the potency of power available on the DC side before initiation of the conversion process. Conversion is executed only when available power meets the specific level. Otherwise, it is diverted and stored in a battery bank to prevent high losses. In this scenario, the AC loads are supplied by the utility grid while solar and battery bank catered the DC loads. The conversion process is selectively activated, prioritizing its use during indispensable circumstances. By optimizing conversion losses, this work reduces the energy prices by 1.95%. The proposed scheme guarantees economical deployment and affordability because of its effectiveness in a weak MG environment, thus promoting sustainable energy resources.

This paper introduces a pioneering model for short‐term planning of an energy hub (EH) that goes beyond traditional approaches by considering a comprehensive multicarrier energy system. The proposed model focuses on minimizing energy buffering costs while ensuring system operation and optimizing economic performance. The novelty of this study lies in its integrated approach, which simultaneously addresses operational efficiency, energy storage requirements, and overall system performance. The EH in this study is modeled as a prosumer within a day‐ahead energy market, where both inflows and outflows of energy are optimized. The system’s capability to interact with upstream energy networks, including gas, heat, and electricity, is a critical aspect of the model. This interaction is managed through various technologies that enhance the hub’s ability to meet local demands efficiently. By employing an advanced improved particle swarm optimization (IPSO) algorithm, this model solves the complex multiobjective optimization problem inherent in EH management. The proposed model’s effectiveness is validated through extensive simulation on a test system, where its performance is compared with conventional heuristic optimization algorithms. The results demonstrate the superior efficiency and applicability of the IPSO algorithm, confirming that the proposed model offers a significant advancement in the field of sustainable energy management.

Line failures, particularly in the form of N‐1 contingencies, are a significant cause of interruptions in power grids, negatively affecting system reliability. Effective risk‐based contingency analysis (RBCA) is essential in modern power systems to address increasing uncertainties and complexities. This paper proposes an RBCA framework that calculates the probabilistic risk index (RI) based on transmission line severity functions for single‐ and multiarea bulk power systems. The approach identifies critical transmission lines and least reliable buses, enabling utilization of load curtailment strategies. Meta‐heuristic techniques, particle swarm optimization (PSO), and gray wolf optimization (GWO), are employed for optimal load curtailment, achieving approximately 30% reduction in curtailed load compared to the analytical proportional load curtailment approach. The methods chosen for their robustness and ability to balance exploration and exploitation in various optimization problems provide practical insights for system operators to enhance reliability under diverse operating conditions. Furthermore, the study examines the impact of flexible thermal rating (FTR) on multiarea systems considering variations in weather conditions, and a comparison is drawn with the static thermal rating (STR) system. Key reliability indices, including EENS, EDNS, BPECI, MBPCI, and EIC, are determined and analyzed for the proposed study. The proposed approach benefits system operators in preventing outages and formulating contingency action plans and emphasizes the study’s contribution to ensuring a stable and reliable power supply which is critical for modern societal needs. The proposed approach has been tested and validated on IEEE 24 reliability test system (RTS).

To address the economic challenges posed by the integration of a large number of electric vehicles (EVs) into microgrids, while leveraging their mobile energy storage (MES) capabilities and accounting for the impact of EV users’ travel patterns on charging and discharging behaviors, a microgrid scheduling model is proposed that incorporates the MES characteristics of EVs under user travel habits. Firstly, based on the spatial and temporal characteristics of the EV travel chain, the upper and lower bounds of the state of charge (SOC) that EVs must maintain at specific moments during their driving process are determined. Secondly, a mathematical model of a microgrid operation incorporating EV mobile storage batteries, wind power, photovoltaic systems, stationary batteries, and micro‐gas turbines is developed. This model considers the costs of electricity purchase and sale, wind and solar curtailment, and natural gas consumption, with the objective of minimizing the total operating cost. To validate the effectiveness of the proposed approach, the optimal scheduling model is implemented and solved using YALMIP and GUROBI. Simulation results demonstrate that the proposed model significantly reduces the total operating cost of the microgrid compared to traditional methods. It also improves the profitability of EV users to a certain extent, promoting new energy consumption when new energy resources are abundant.

Challenges in power quality and reliability present significant difficulties in conventional power grids for both service providers and customers. Smart grids (SGs) provide the opportunity to integrate renewable energy resources, and integrating Internet of Things (IoT) in the grid can enhance the capabilities of the SG. This provides solutions to various challenges in power generation and distribution. This article aims to discuss the challenges and solutions encountered during the implementation of IoT in SG by revising the authors and their ideas. In this review, numerous applications such as advanced metering infrastructure (AMI), data distribution service (DDS), and supervisory control and data acquisition (SCADA) and how they can improve reliability and effectiveness in SG were discussed. However, there are still challenges faced when using IoT in a SG, such as the security threats and storage of large amounts of data as well as the exchange of information between equipment and control systems. Therefore, future research should focus on new security protocols that are specifically designed to address the unique challenges of IoT in SGs.

Based on the closed‐loop frequency domain model of permanent magnet synchronous generator–based wind power generation system (PMSG‐WPGS), the stability domain of the control parameters is constructed through the D‐partition method in this paper to acquire the range of machine‐side converter (MSC) and grid‐side converter (GSC) parameters. First, the basic stability domain of the controller parameters is established according to the control structure and transfer function of the back‐to‐back converter. Then, based on the frequency domain performance indexes, amplitude margin and phase angle margin, the obtained basic stability domain is adjusted to further gain the control parameter values that meet the frequency domain index. Furthermore, the rationality of the stability domain range is verified by drawing root locus curves of variable controller parameters on the machine side and grid side. Finally, the low‐power scale experimental platform of PMSG‐WPGS is built in the lab to validate the feasibility of the proposed stability domain construction method.