International Transactions on Electrical Energy Systems最新文献

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.

An intelligent power system is either a system that is smartly designed from zero to 100, or a system that was not smartly designed but currently uses all its facilities to be smartly operated in different sectors. This paper presents a novel data-driven model for real time voltage instability diagnosis and instability mitigating. The method combines deep recurrent neural techniques to forecast future voltage stability and mathematical morphology (MM) tools to pinpoint the specific on-load tap changers (OLTCs) contributing to instability and issuing blocking commands to prevent their operation and consequently instability. The approach for voltage stability assessment is centralized, using real-time data, while the method for voltage instability mitigation is localized, focusing on real-time voltage magnitude related to the secondary side of the load transformer. The network was trained and tested on the Nordic32 test system. Results show that the method accurately predicted the stability status just one second after a disturbance, and successfully mitigated all voltage instability events related to load restoration by blocking only the OLTCs that were effective in causing instability. This selective approach provides a significant selectivity index and improves the system resiliency index.

This paper introduces a novel hybrid multiport converter (MPC) for water irrigation systems. The proposed MPC is characterized by its simplicity and the ability to maintain a consistent DC-link voltage. A power management technique has been developed to ensure the maximum power utilization sources even if either one of the sources is absent and simultaneously manages power demanded by the load. This technique facilitates active power sharing, rapid output voltage control, and handling the load disturbances. The proposed converter’s performance is assessed through simulation tools and experimental validation for a 1-kW system, considering three different scenarios. Thus, the proposed converter achieves high gain, high efficiency of 96%, and faster dynamic response.

In this study, we proposed a novel multiobjective optimization technique for electric vehicles’ (EVs) charging and vehicle-to-grid (V2G) scheduling. The ring seal search (RSS) algorithm ensures the optimum compatibility of the EV charging and discharging profiles revolving around multiple objectives, such as cost of charging, peak load demand reduction, and grid stability. The proposed algorithm is tested on the distribution model through the IEEE 33-bus system. A comprehensive model with real-time data from EV charging station operators (CSOs) is also ported in this research so that the EVs can be illustrated in the power distribution network. A convenient energy management strategy (EMS) called multiobjective optimization has been introduced to provide practical solutions for CSOs and EV users. The strategy had been developed based on multiple objectives to counter different trade-offs, including EV charging and discharging profiles suitable for numerous objectives encompassing charging costs, peak load demand reduction, and grid stability. The efficiency of the deterministic approach has been verified via extensive simulations and analysis, and the outcomes incorporate a definite enhancement in metrics since the RSS algorithm considerably optimized EV charging and V2G scheduling. The EV charging and discharging profiles had been optimized to take better advantage of available resource requirements by accommodating priority-based scheduling. The RSS will be able to investigate the modified parameter and the modified real system, which tells about the versatility and adaptability of the proposed method, i.e., it will be able to incorporate the changing implementation and its real-world efficacy, which is an immense merit for the adaptation of the real system. The proposed method offers a comprehensive, advanced EV charging and V2G scheduling solution. It addresses the limitations of previous methods by considering multiple objectives, utilizing a novel optimization algorithm, handling uncertainties, promoting renewable energy integration, and providing ancillary grid support. These enhancements make our method more effective, flexible, and capable of supporting the transition to a sustainable and efficient energy system.

The medium voltage distribution network is a key bridge between the power sector and electricity users. In the process of increasing user demand for electricity, the medium voltage distribution network system has encountered problems such as insufficient reactive power, unreasonable distribution, and insufficient voltage at the end nodes of the line, which have affected the power supply quality and stability of the power system. Therefore, a multiobjective reactive power optimization planning method for medium voltage distribution networks based on an improved genetic algorithm is studied. Establish a mathematical model for medium voltage distribution network planning based on the multiobjective functions of active power loss, total voltage deviation of system nodes, and minimum total compensation amount of system compensation devices. The balance equation between active and reactive power of power nodes and power absorption losses is taken as the equality constraint, and the maximum and minimum constraints of variables such as voltage at the generator end and tap position of the on-load tap changer are taken as the constraints of the model. By combining the advantages of the standard genetic algorithm and simulated annealing algorithm, an improved genetic algorithm is formed to effectively solve the constructed mathematical model. After countless iterations, the effective solution of the model is obtained to achieve multiobjective reactive power optimization planning for medium voltage distribution networks. The experimental results show that this method can achieve multiobjective reactive power optimization in medium voltage distribution networks and improve the stability of the power system.

The stability of the power system is largely affected by low-frequency oscillations, so early warning research on low-frequency oscillations in power grids has become an urgent task. Traditional low-frequency oscillation early warning methods are still deficient in handling incomplete and highly discrete information. Compared with the existing methods, we have pioneered a synergistic mechanism of discrete attribute screening and continuous probabilistic feature fusion by combining the dynamic attribute approximation algorithm of rough sets with the cloud model, which effectively solves the loss of information caused by the discretization of continuous data in the traditional methods. Firstly, we analyze the principle of grid oscillation, use rough sets to process the raw data and indicators, remove redundant attributes, and get the set reflecting the relationship of different attributes. Then we construct a standard cloud based on grid operation data and a comprehensive cloud based on PMU data and obtain the oscillation warning evaluation. Finally, through the validation and simulation of 10 machine and 39 node systems in New England, as well as the comparison with other methods, the rationality and effectiveness of the proposed method are proved to be of theoretical and practical application value.