International Transactions on Electrical Energy Systems最新文献

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.

The integration of renewable energy sources into hybrid microgrids (HµGs) holds the potential to improve grid voltage profiles, but without proper optimization, it can also lead to performance degradation. This study offers an explorative investigation into the dynamic behavior of HµGs under various configurations, operating in both grid-connected and standalone modes. Through technical analyses, an energy system design is presented for comparing performance across different scenarios. In contrast to previous research, HµGs incorporating solar photovoltaic (PV) systems, wind turbine generation (WTG), diesel generators (DG), and battery energy storage systems (BESS) are modeled. Two operational cases—grid connected (Case 1) and standalone (Case 2)—are simulated, each evaluated through three scenarios using MATLAB/Simulink. Key parameters such as HµG voltage, frequency, power contributions, and battery state of charge (SoC) are analyzed, revealing significant challenges and insights into system behavior. The study shows that changes in system configuration impact HµG voltage and frequency, with maximum deviations reaching 54 Hz, 17 kV, and 5.8 kV. Frequency instability is observed in scenarios involving WTG integration, while sensitivity analysis highlights the critical role of load variations on frequency stability. This research provides actionable benchmarks for network planners and operators to ensure efficient integration of renewable energy into power grids.

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).

Flexible load resources on the demand side are characterized by diverse types, small capacity, and wide distribution. Since these load resources cannot be individually invoked at the system level, it is becoming increasingly important to consider how to establish heterogeneous flexible load aggregates and utilize their adjustable characteristics. In response to the above problems, this paper establishes mechanistic models for three specific heterogeneous load resources, uses the basic homothetic polytope to approximate the original feasible region of loads, and aggregates the feasible region of heterogeneous load resources using the Minkowski sum, while ensuring the aggregation speed and accuracy. Finally, for scenarios of system load shedding and high wind and solar penetration, a demand response strategy on the load side is introduced, and the role of this method in system power supply guarantee and new energy consumption is illustrated through a numerical example.

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.