International Transactions on Electrical Energy Systems最新文献

As the proportion of renewable energy sources continues to rise, the stability and reliability of the power system face enormous challenges. Virtual synchronous generators (VSGs) enhance grid stability by simulating conventional synchronous generator characteristics and providing virtual inertia and damping to the system. However, VSGs with fixed inertia and damping parameters are difficult to adapt to the complex and changing grid environment. To this end, this manuscript proposes an adaptive control strategy based on variable universe fuzzy control to realize the adaptive adjustment of VSG inertia and damping parameters. First, the mathematical model of VSG is established to analyze the influence of inertia and damping on the power–frequency characteristics of the system, and the variable universe fuzzy controller is designed based on the principle of parameter optimization to realize the real-time optimal regulation of parameters. Second, model predictive current control (MPCC) is introduced to replace the traditional voltage and current PI regulation, and a novel three-vector model predictive current control strategy (NTV-MPCC) is proposed, which makes the synthesized voltage vector changeable in both amplitude and direction and effectively reduces harmonic distortion and current ripple. Finally, the effectiveness of the proposed control strategy is verified by simulation, which shows that the proposed method is able to improve the dynamic response capability, steady-state performance, and current quality of the VSG system.

This study proposes an improved version of the wild horse optimizer (WHO) for the optimal allocation and sizing of distributed generators (DGs) and capacitor banks (CBs) to promote the system’s susceptibility. The proposed method, namely, improved WHO (IWHO), aims to improve the performance of the system not only in terms of power loss, voltage deviation index (VDI), and voltage stability index (VSI) as in most previous studies, but also in terms of generation cost and total emissions. Five operational cases are carried out on four different systems, the IEEE 33-bus, 69-bus, 118-bus standard radial distribution systems and the real 78-bus Egyptian distribution system, to demonstrate the best performance of the proposed technique. In addition, two multiobjective functions are implemented to compare with the original WHO and other existing optimization techniques. Based on the statistical analysis, the simulation results prove that the proposed IWHO provides the best results for flexible operations, especially for large-scale complex systems. After the optimal integration of DGs and CBs, the power loss was reduced up to 94.18%, 98.53%, 92.05%, and 93.87%; the cost was reduced by 43.23%, 43.77%, 14.68%, and 99.99%; and the emissions were reduced by 99.96%, 99.99%, 76.01%, and 61.20% for 33-bus, 69-bus, 118-bus radial systems and the real 78-bus system, respectively. It is observed that the IWHO algorithm also gives recognized enhancements in conflicting objective functions such as technical, economic, and environmental objectives.

A mathematical model of a double-star permanent magnet synchronous motor is proposed, coupled with an estimator for speed, position, and currents based on an extended Kalman filter. This filter is optimized using a novel methodology. The primary objective is to determine the optimal values for the three noise covariance matrices to ensure the convergence of the parameter estimation. To enhance the estimator’s performance, several innovative optimization strategies are introduced. These combine different techniques, notably particle swarm optimization with the Nelder–Mead simplex algorithm, as well as a genetic algorithm coupled with this same hybrid method. Other approaches are also deployed, such as a fuzzy self-tuning method for success-history–based parameter adaptation for differential evolution, along with a fuzzy version of the linear population size reduction success-history–based adaptive differential evolution algorithm. Furthermore, an accelerated procedure for initializing the covariance matrices is implemented. Specifically, the error estimation covariance matrix and the measurement noise covariance matrix are fixed, while the process noise covariance matrix is the subject of the optimization. The validity of the proposed approach is demonstrated through comprehensive numerical simulations. These simulations include the machine model, its power supply, and the parameter estimator, all implemented within the MATLAB/Simulink environment.

One well-known fault that arises from water intrusion and power cable insulation failure is the incipient fault (IF) in underground power cables (UPCs). Therefore, it is necessary to provide appropriate models for modeling this fault and also to generate data close to the actual value for validating IF detection methods. This study presents a model for IFs in UPCs, which provides the necessary data for the assessment. A few articles have been devoted to modeling the IF in UPCs, despite the numerous articles that have been introduced on arc modeling in other applications. This work deals with driving effective modeling of IFs in power cables from the two modified Avdonin models using the records of experiments obtained from a laboratory setup. The transient characteristics of IFs are demonstrated in the proposed models using the idea of transient coefficients. Also, the least squares method is used to update the models’ coefficients for every power frequency cycle. Finally, two error indices are introduced to establish each model’s optimal coefficients. Probability distribution functions (PDFs) were utilized to simulate the stochastic conduct of the model coefficients, which change with each cycle. As a result, for each combination of the model’s coefficients, some PDFs are examined, and the PDF that most closely matches the actual data is chosen. Also, the proposed models of this study are compared with the modified polynomial and modified Schwarz models.

This study proposes an advanced optimization technique for maximum power point tracking (MPPT) in wind turbines (WTs) based on a permanent magnet synchronous generator (PMSG), which is crucial for maximizing energy extraction under varying wind conditions. Several MPPT strategies have been evaluated and compared, including neural networks (NNs), sliding mode control (SMC), the Whale Optimization Algorithm (WOA), and the Cuckoo Search Algorithm (CSA), to determine the most effective approach for optimizing power output and improving system efficiency. Emphasis is placed on identifying techniques that not only enhance energy capture but also reduce the complexity and cost of wind energy systems. To achieve this, the study introduces a novel hybrid algorithm that integrates the strengths of both WOA and CSA, leveraging their complementary exploration and exploitation capabilities. The proposed method aims to deliver improved tracking accuracy and faster convergence to the optimal power point. The algorithms were tested using a real wind profile from Djelfa, Algeria, a region characterized by semiarid climate and varied topography, to simulate realistic operational scenarios, providing accurate assessments of each MPPT strategy under true environmental conditions. The results obtained through MATLAB/Simulink simulations demonstrate that the newly developed hybrid WO–CSA strategy consistently outperformed others, delivering approximately 140 W more power than CSA and about 230 W more than WOA and NN at a wind speed of 10 m/s, while the SMC strategy exhibited the lowest performance, generating roughly 750 W less power compared to WOA and NN. By developing the new algorithm, the study contributes to the development of more efficient and reliable WT technologies.

As the proportion of building energy consumption in total energy consumption continues to rise, traditional energy scheduling strategies and building load regulation methods are improved to reduce energy consumption and enhance the flexibility of building scheduling. In this study, a two-stage optimization strategy for energy-efficient buildings incorporating electric vehicles (EVs) based on user satisfaction is proposed. First, a source–load–storage coordinated energy optimization system for buildings, including photovoltaic (PV) generation, energy storage systems (ESSs), EVs, light-emitting diode (LED) lights, and heating, ventilation, and air conditioning (HVAC), is established. Second, the satisfaction levels of users with multiple flexible loads are used as indicators of comfort to dynamically adjust energy consumption in buildings. Then, a multiobjective energy optimization model is formulated to minimize daily operational costs while simultaneously maximizing user satisfaction, with an emphasis on balancing comfort and economic efficiency. Third, a two-stage energy optimization model of day-ahead and intraday is constructed to reduce the impact of source–load forecasting errors on the operation of building energy systems, and an incentive demand response strategy is introduced to guide users to participate in scheduling in the intraday stage. Finally, different cases are created to test the effectiveness of the proposed strategy. The overall simulation results validate the proposed approach with operational cost reduction of 12.9% while maintaining a user satisfaction level above 0.95 and grid volatility reduction of 7.56% as compared to the traditional energy optimization strategy.

This paper proposes a multiobjective medium-term optimal scheduling model of the cascade hydro–photovoltaic (PV)–pumped storage system to increase the renewable energy accommodation capacity of the system. The uncertainties of natural water inflow and PV power output have been formulated using the information gap decision theory (IGDT), and the proposed multiobjective model is solved with the ε constraint method. A case study of a test system including 410 MW cascade hydro, 70 MW pumped storage, and 60 MW PV shows that the proposed model reduced solar curtailment rate from 22.65% to 0.23% compared to the conventional hydro–PV system, and the IGDT-based model avoids risk from the uncertainties of natural water inflow and PV power output effectively.

The increasing pace of urbanization has intensified the global demand for clean and decentralized energy systems, placing solar energy at the forefront of sustainable urban transitions. While prior studies have separately explored photovoltaic (PV) technologies, urban form, or energy policy frameworks, few have synthesized these dimensions into an integrated roadmap for solar adoption in smart cities. This study addresses that gap by introducing the policy–technology–morphology nexus (PTMN), a novel conceptual model developed through the cross-analysis of 120 peer-reviewed studies and urban case implementations. The PTMN framework unifies three essential pillars: policy instruments (e.g., feed-in tariffs, net metering), enabling technologies (e.g., AI-based solar mapping, smart grids, battery optimization), and urban morphological variables (e.g., building density, orientation, and shading).Through comparative tables and geospatial insights, the review reveals that morphology-sensitive design, when coupled with intelligent technologies and regulatory incentives, can enhance solar efficiency by up to 40% in selected cities such as Geneva, Stonehaven, and Shenzhen. Methodologically, the study integrates GIS-based assessments, deep learning approaches, and system-level classification typologies to map deployment scales, performance gaps, and policy relevance. Findings highlight the critical role of digital twins and smart storage integration in enabling equitable and scalable solar transitions. Limitations include the reliance on location-specific data and the absence of multicity dynamic simulations. Future research should focus on enhancing AI-driven predictive modeling for solar energy optimization, developing novel energy storage technologies, and fostering interdisciplinary collaborations among policymakers, engineers, and urban planners.

Electric vehicle (EV) technologies have become crucial in the current times as they are projected to be one of the major contributors to energy transition in global transportation and power system. They have been identified to offer social, technical, economic, and environmental benefits. Solar and hybrid EV chargers offer more significant advantages over grid-tied chargers. Despite the many advantages that EVs bring, there are also drawbacks associated with this technology. This paper therefore provides an extensive review on EV charging technologies and methods, international standards, and protocols. The work reviews solar power for EV charging stations, where grid-tied and off-grid systems are intensely examined. The system architecture and configuration, and charging station layouts are presented. An in-depth comparative review of charging technologies’ infrastructure Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) cost analysis is examined. Eight global solar EV charging projects are closely analyzed and compared. From these case studies, lessons learnt and best practices are derived and a summary is provided. The challenges and future trends are also reviewed and presented in this work. The review presented in this work is useful to a wide range of individuals and groups, including but not limited to governments, potential buyers, policymakers, and researchers.

In day-ahead electricity markets with high renewable penetration, price prediction errors are prevalent. These errors significantly increase the downside risk of energy storage arbitrage, potentially diminishing profits or even causing sustained losses. To address the lack of effective downside protection for energy storage systems operating in highly uncertain environments, this paper proposes a reinforcement learning–based battery-dispatch method. The method is enhanced by three mechanisms to improve policy robustness and risk management capabilities. Residual injection disturbs predictive inputs to simulate various bias scenarios, guiding agents toward more conservative decision-making. Action hard projection maps outputs in real time onto feasible regions, ensuring physical feasibility and training stability. Teacher model behaviour cloning incorporates low-risk demonstrations based on actual prices, accelerating convergence and avoiding high-risk actions. The approach underwent long-term empirical validation using highly volatile data from the Germany–Luxembourg market for 2020–2024. Results indicate that, although the proposed method yields slightly lower average returns compared to the traditional prediction-and-optimization baseline, it significantly reduces maximum drawdowns, loss probability and profit volatility, thereby demonstrating robust downside-risk protection. This study validates reinforcement learning’s capacity for effective risk control in energy storage dispatch and provides a viable pathway for robust asset management in highly volatile electricity markets.