International Transactions on Electrical Energy Systems最新文献

The entry of distributed generation causes the network to become complicated, and as a result, the protection coordination between the network equipment is lost. In previous studies, three decision variables were considered for each directional overcurrent relay: time dial setting in the main mode, time dial setting in the backup mode, and pickup current (I_p). However, the minimum coordination time interval between the main and backup relay was lost. In this paper, communication-free dual characteristics directional overcurrent relays are used, considering six decision variables: I_p, time dial setting of the relays in the main and backup mode, breakpoint current, and the type of curves in main and backup operations. The proposed scheme maintains the minimum time interval between the main and backup relays, which reduces the operation time of the relays compared to previous studies. The proposed scheme is implemented on the modified IEEE 30-bus network distribution section. DIGSILENT software is used for simulation, and load currents and fault currents of protection relays in grid-connected and islanded conditions are extracted based on load flow and short circuit studies. Then, the optimization problem is solved using the genetic algorithm in MATLAB software, and optimal settings are obtained to minimize the operation time of the relays. The simulation results of the proposed method shows that the operation time of the relays can be improved by 17% compared with the previous existing method.

Recent improvements in the application of distributed energy resources (DERs) and microgrids (MGs) have made controlling these resources very important. However, there are still many challenges in this field. One of the anticipated challenges in islanded MGs (IMGs) is the mismatched output impedance of DERs, which affects the volt-var regulation, shortly after the occurrence of islanding mode. Ignoring these events creates consecutive power quality challenges such as circulating currents and reactive power sharing error, voltage variations, increased power loss, and overcurrent. Even though the microsources that make up the MG are located near loads, MGs often require receiving control commands from the central controller through low-/high-bandwidth communication networks to achieve the most stable operating point. Depending on the type of control structure, the information of the units including current, voltage, and active and reactive power is received/sent between one and three control levels. This paper aims to analyze the state-of-the-art techniques that are often developed based on adaptive virtual impedance droop control (AVIDC). Hierarchical structure and multiagent system (MAS) are the most coherent class of three-level infrastructures implemented by the consensus protocol. In this survey, the opportunities and threats of the islanded AC MGs controlled by the enhanced droop method using virtual impedance have been analyzed. At the same time, these have been implemented in centralized, decentralized, hierarchical, and MAS-based distributed coordination structures. Finally, the simulation results of an IMG controlled by AVIDC and based on line X/R ratio have been analyzed in PSIM Altair software.

This study investigates the impact of increasing electric vehicle (EV) adoption on the power grid in the United Arab Emirates (UAE), focusing on grid performance, stability, and efficiency under different EV penetration scenarios. A mathematical model is developed to evaluate EV charging load profiles based on energy consumption, charging schedules, and station distribution. The results reveal that level 1 (120 V) charging stations generate a peak load of 93.6 kW, whereas level 2 (240 V) stations impose a significantly higher peak load of 187.2 kW. The study finds that while the existing power grid can support up to 40% EV penetration with level 1 charging, it risks exceeding capacity with level 2 infrastructure. By 2030, a 40% EV penetration with level 2 charging is projected to surpass the system’s margin capacity, increasing the likelihood of voltage instability and transformer overloads. This research is novel in its UAE-specific modeling of EV charging impacts, offering detailed insights into grid constraints under future EV growth. To mitigate these challenges, the study recommends dynamic pricing strategies and vehicle-to-grid (V2G) technology to optimize load distribution and enhance grid resilience. The findings provide essential guidance for policymakers, utilities, and industry stakeholders in developing a sustainable and efficient EV charging infrastructure.

To meet the demand for cold chain logistics through green transportation, this study designed a solar-powered vehicle with energy storage ability for cold chain logistics operations. The designed vehicle has solar panels on its roof that power the refrigeration system of the vehicle during transportation. This use of solar energy enables the fuel and energy consumption of the vehicle to be reduced. Moreover, the energy storage ability of the designed vehicle enables its refrigeration system to continue running continuously even when the vehicle is switched off. The designed vehicle reduces carbon and CO₂ emissions by at least 36.3%, thereby mitigating urban air pollution. This study develops a business model to help relevant companies reduce carbon credit costs, ensure compliance with regulations and standards, and enhance corporate image. Thus, the designed vehicle contributes to the achievement of the sustainable development goals (SDGs) of the United Nations and the global objective of realizing net-zero carbon emissions by 2050; particularly, SDG 9 relates to the industry, innovation, and infrastructure; SDG 12 relates to responsible production and consumption; and SDG 13 relates to climate action.

Detecting and classifying various faults on high voltage DC transmission (HVDC) lines and pinpointing their locations are crucial tasks for the power system’s efficient operation. This paper presents a Teager–Kaiser energy operator (TKEO) technique with a decision tree–based fault type classifier to monitor power system faults on the HVDC transmission line. The change identification filter technique is used to identify the fault location and record it as the change initiation point (CIP). There are only three samples of the average current (I_avrg) used at the CIP of the HVDC link. The eight indices for fault analysis are produced by the suggested TKEO approach by processing average current (I_avrg) signals not the differential current. Electricity networks may be restored as soon as practical while minimizing economic losses to the greatest extent possible, thanks to the new method’s speedy problem identification. This state-of-the-art technique improves fault localization, categorization, and identification efficiency. It also reduces the time and computational complexity needed to find faults. It is even more cost-effective because the suggested method is connected to a nearby microgrid, which supplies a small portion of the total electricity produced by the two wind and solar farms. With a fault-detecting efficiency of 97%, the suggested method shows a significant improvement in accuracy.

The integration of wireless power transfer (WPT) and vehicle-to-grid (V2G) technologies is essential for the sustainable operation of lunar multienergy virtual power plants (MEVPPs), where rovers, habitats, and in situ resource utilization (ISRU) facilities rely on adaptive energy management. Unlike terrestrial systems, lunar environments present extreme challenges, including long-duration night cycles, regolith dust accumulation, severe temperature fluctuations, and dynamic rover mobility, all of which disrupt efficient power delivery. This paper proposes a reinforcement learning–based adaptive beam steering framework to optimize WPT scheduling, ensuring continuous and efficient energy transmission for both mobile and stationary lunar assets. Unlike traditional fixed-beam or heuristic-based WPT methods, the proposed system utilizes deep reinforcement learning (DRL) with proximal policy optimization (PPO) to autonomously adjust beam direction, power intensity, and charging priority in response to real-time rover movements, V2G interactions, and fluctuating energy demands. The proposed framework models WPT optimization as a Markov decision process (MDP), where the agent learns to dynamically adapt beam steering based on rover speed, response delay, solar power availability, and charging station congestion. The reward function penalizes energy misallocation and misalignment losses while maximizing charging efficiency and systemwide energy resilience. A case study simulating a 30-day mission near Shackleton Crater evaluates the effectiveness of the AI–driven WPT system, demonstrating a 54.6% reduction in energy downtime and a 41.3% improvement in beam alignment efficiency compared to static power scheduling methods. In addition, the system reduces latency-induced power deficits by 39.8%, ensuring reliable power distribution for ISRU oxygen extraction, habitat life support, and rover recharging stations. This study represents a novel advancement in lunar power infrastructure, integrating AI–driven adaptive WPT with intelligent energy scheduling to enhance V2G interactions in extraterrestrial environments. The results validate the feasibility of DRL–based WPT control, paving the way for scalable, resilient, and self-optimizing wireless power grids on the Moon. Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.

Renewable energy sources (RESs) are growing exponentially due to need for sustainable energy. The hosting capacity (HC) is the amount of RESs that can be installed in a distribution network without exceeding its operational limitations, such as bus voltages and line flows. Energy storage systems (ESSs) have been utilized to enhance the HC in the literature. However, high investment cost of ESSs is the main obstacle to their widespread deployment. This highlights value of multipurpose energy storages (MPESs) that have multiple purposes besides the electrical aspect. Potential in the irrigation of the agriculture sector based on small pumped-hydro storages (PHSs) has been employed in this paper to enhance the HC of rural distribution networks. For this purpose, a new practical model has been proposed for the PHS considering residential and agricultural water consumption management. Also, the HC of the distribution network for photovoltaic is investigated based on a mixed integer nonlinear programming (MINLP) model. Simulation results on the IEEE 33-bus test system in GAMS and the comparison with the ESSs showed that the PHSs in addition to their main application increase the HC by 70 kW and reduce the cost and losses by 36 kWh and $47,562, respectively.

In response to the issue of low search efficiency caused by the large number of expanded nodes when mobile robots use traditional A^∗ algorithm for path planning in complex environments, an improved A^∗ algorithm based on four-way search has been proposed. This algorithm leverages Euclidean distance to weight the heuristic function on the basis of the traditional A^∗ algorithm and reduces the number of expanded nodes and search time through the four-way search algorithm. Subsequently, experiments were conducted using maps to verify the performance of the improved algorithm. The experimental results indicate that the simulation of the improved A^∗ algorithm based on four-way search can achieve higher search efficiency, with fewer expanded nodes, which is more conducive to the path planning of mobile robots.

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.

In islanded microgrid configurations, synchronization of distributed generators (DGs) becomes imperative. Achieving synchronization and control necessitates the establishment of communication links. However, communication channels are susceptible to various challenges, with cyber-attacks emerging as a primary concern. This paper examines the vulnerability of cooperative hierarchical controllers in the face of diverse cyber-attacks, including DoS, sensor and actuator attacks, and hijacking attacks. DGs are considered a multiagent system for stabilization and global synchronization of the network. Cyber-attacks on the secondary controller have been formalized, and an appropriate controller is designed for system synchronization and stability. The appropriate Lyapunov function is introduced to prove the stability. Then, the simultaneous stabilization and global synchronization conditions have been investigated by proving suitable theorems. A comprehensive case study is executed via simulation in MATLAB/Simulink, incorporating cyber-attack scenarios. The effects of cyber-attacks on this controller are eliminated, and the DGs are synchronized. For comparison, the resilience indicator has been used. In this controller, the cyber-attacks of the sensor and hijacking attack are well controlled. A DoS cyber-attack is more effective than other attacks and causes some DGs to go off the network. Also, comparing this controller to other controllers shows its greater resilience.