Ragab A. El-Sehiemy
6504618921
Publications - 21
Generic optimal power flow solution associated with technical improvements and emission reduction by multi-objective ARO algorithm
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
In modern power engineering, the optimal operation aims to achieve the basic requirements of the electrical power grid, meet various technical and economic aspects, and preserve the environmental limits within their accepted bounds. In this line, the current paper finds the optimal operational scheduling of the power generation units that cover the load requirements, considering different frameworks of the optimal power flow (OPF) problem involving single- and multi-objective functions. Technical, economic, and emissions objective functions are considered. Artificial rabbits’ optimization (ARO) is developed to find the optimal OPF framework solution. The effectiveness of the proposed algorithm is evaluated through a comprehensive comparison study with the existing works in the literature. With six IEEE standard power systems, 22 different cases are implemented to test the ARO performance as an alternative to solve the OPF problem. Two of these systems are considered small-size systems, 30-, and 57-test systems, while the other four are large-scale power systems (IEEE 300, 1354, 3012, and 9241 test systems) to expand the validation scope of this paper. This comparison validates the scalability and efficiency of the ARO algorithm. The impact of varied population size and maximum iteration number is tested for two test systems, the most benchmarking test systems. It was proven that the routine of ARO has robust and superior competitive performance compared with others at fine convergence rates. Significant improvements are acquired in the range of 47% in the technical and economic issues by accepting the environmental concerns.
Open Access: Yes
Generic multidimensional economic environmental operation of power systems using equilibrium optimization algorithm
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The economic emission load dispatch (EELD) problem is one of the main challenges to power system operators due to the complexity of the interconnected power systems and the non-linear characteristics of the objective functions (OFs). Therefore, the EELD problem has attracted significant attention in the electric power system because it has important objectives. Thus, this paper proposes the equilibrium optimization algorithm (EOA) to solve the EELD problem in electrical power systems by minimizing the total fuel cost and emissions, considering system and operational constraints. The OFs are optimized with and without considering valve point effects (VPE) and transmission system loss. The multi-OF, which aims to optimize these objectives simultaneously, is considered. In the proposed EOA, agents are particles and concentrations that express the solution and position, respectively. The proposed EOA is evaluated and tested on different-sized standard test systems having 10, 20, 40, and 80 generation units through several case studies. The numerical results obtained by the proposed EOA are compared with other optimization techniques such as grey wolf optimization, particle swarm optimization (PSO), differential evolution algorithm, and other optimization techniques in the literature. To show the reliability of the proposed algorithm for solving the considered OFs on a large-scale power system with and without considering different practical constraints such as VPE, ramp-rate limits (RRL), and prohibited operating zones (POZs) of generating units, the proposed EOA is evaluated and tested on the 140-unit test system. Also, the proposed multi-objective EOA (MOEOA) successfully acquires the Pareto optimal front to find the best compromise solution between the considered OFs. Also, the statistical analysis and the Wilcoxon signed rank test between the EOA and other optimization techniques for solving the EELD problem are performed. From numerical results, the total fuel cost obtained without considering VPE using the proposed EOA is reduced by 0.1414%, 0.1295%, 0.6864%, 5.8441% than the results of PSO, with maximum savings of 150 $/hr, 78 $/hr, 820 $/hr, and 14,730 $/hr for 10, 20, 40, and 80 units, respectively. The total fuel cost considering VPE is reduced by 0.0753%, 0.2536%, 2.8891%, and 3.6186% than the base case with maximum savings of 80 $/hr, 158 $/hr, 3610 $/hr, 9230 $/hr for 10, 20, 40, and 80 units, respectively. The total emission is reduced by 1.7483%, 12.8673%, and 7.5948% from the base case for 10, 40, and 80 units, respectively. For the 140-unit test system, the total fuel cost without and with considering VPE, RRL, and POZs is reduced by 6.4203% and 7.2394%, than the results of PSO with maximum savings of 107,200 $/hr and 126,400 $/hr. The total emission is reduced by 2.5688% from the base case. The comparative studies show the superiority of the EOA for the economic/environmental operation of the power system by solving the EELD problem with more accuracy and efficiency, especially as the system size increases.
Open Access: Yes
High precision experimentally validated adaptive neuro fuzzy inference system controller for DC motor drive system
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The control of a dc motor drive system is performed using two Proportional-Integral (PI) controllers, mostly comprising the outer (speed) and inner (torque) loops. However, the tuning of the PI controllers is challenging, because of the significant overshoot and substantial settling time. The overshoot cannot be absolutely removed without sacrificing the speediness. In this paper, an experimental implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) based high-precision controllers for a dc motor drive system is presented for eliminating the overshoot contemporaneously improving the settling time. In this regard, for fair comparison, Bode Plot method is used for obtaining most satisfactory values of Kp and Ki of both the controllers and the performance of the proposed ANFIS controllers is compared with the tuned PI controllers. For the sake of validation, an experimental setup, a 2-quadrant dc motor drive based on DS1104 R&D controller board from dSPACE is used. The designed ANFIS controllers are implemented and also authenticated using simulations. The obtained simulation as well as experimental results indicated that the ANFIS controller successfully eliminates the overshoot and significantly improves settling time. Thereby, outclass the tuned PI controllers by giving experimental results as 0% overshoot and just 0.18 s settling time.
Open Access: Yes
Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Energy hubs (EHs) are considered a promising solution for multi-energy resources, providing advanced system efficiency and resilience. However, their operation is often challenged by the need for techno-economic trade-offs and the uncertainties related to supply and demand. This research presents a multi-objective optimizing framework for EH operations tackling these techno-economic aspects under uncertainty. Utilizing artificial neural networks (ANN)-based active learning (AL), the proposed approach dynamically enhances the model’s capability to achieve optimal scheduling and planning while considering complex, fluctuating energy demands and system constraints. The optimization approach under uncertainty provides robust predictive abilities across various scenarios, allowing the system to optimize energy management effectively, enhancing operational efficiency while minimizing overall energy losses, costs, and emissions. Results demonstrate significant improvements in system reliability, cost efficiency, and flexible operation, validating the effectiveness of ANN-based AL to optimize EHs management and ensure sustainable operation complexities. The AL algorithm enhances the ANN model’s predictive ability, resulting in a 57.9% decrease in operating costs and a 0.010682 loss of energy supply probability (LESP) value. It ensures energy efficiency while sustaining system flexibility, adapting to frequent load dynamics and intermittent renewable energy supply. The algorithm minimizes electrical and thermal deviations, achieving a balance of flexible operation with efficient energy management. Despite uncertainties and intermittent renewable energy supply, the AL optimizes renewables utilization and demand adjustments, reducing energy losses, costs, and emissions by 80.3The optimized system achieves an output of 13,687.8 kW per day. The system’s implementation is performed using MATLAB R2023b software, ensuring precision and efficiency.
Open Access: Yes
A proposed wavelet analysis based fault diagnosis scheme of power transformers using fault signatures and CT saturation
Publication Name: Results in Engineering
Publication Date: 2025-09-01
Volume: 27
Issue: Unknown
Page Range: Unknown
Description:
Diagnosis of concealed internal faults within power transformer is a key for high grid reliability to ensure continuity of power supply to customers. One of the urgent situations of power transformer is the faults under CT saturation and the operation under inrush currents that lead to huge failure of fault identification of the power transformer. In this paper, a fault identification scheme is designed using details and approximate coefficients obtained by discreet wavelet transform applied to a differential current signal under different situations. Also, this paper considers the impact of transformer internal faults such as turn to earth and turn to turn faults, external faults, and inrush currents. The signature of processing differential current is employed for identifying these fault conditions since such fault has a distinct differential current signature. The simulation tests are performed on a 115/22 kV power transformer using ATP-EMTP real-time simulator. Different wavelet families are assessed to show that the optimum mother wavelet, db1, has high fault detection and classification performance. The proposed scheme is verified for transformer energization conditions, and the influence of CT saturation is also considered in this study. Moreover, one of the most important proposed scheme features is simplicity with high lights aspects toward all fault conditions and fault types at different fault location and different fault resistances. Intensive simulation results are obtained to prove the improved selectivity and sensitivity of the proposed scheme for identifying internal transformer faults. Furthermore, sensitivity analysis is not only conducted in terms of transformer loading and fault resistance variation, but transformer scalability study is also verified. Finally, to evaluate the performance of the proposed scheme, an assessment study is adopted to show the accuracy and reliability of differential protection scheme.
Open Access: Yes
Accurate multi-phase unsupervised and supervised approach to fault detection in power transmission networks
Publication Name: Neural Computing and Applications
Publication Date: 2025-08-01
Volume: 37
Issue: 24
Page Range: 19751-19772
Description:
Transmission line faults can cause both power loss and failure. To mitigate the effects of such faults, the energy supply must quickly identify and remove faults, as well as ensure grid restoration after a failure occurs. As a result, it is critical to design a system capable of quickly and reliably identifying and eliminating errors. The level of fault identification accuracy is an important indicator for ensuring the reliability of main equipment in power systems, such as generators and transformers. This paper proposes a two-phase approach for identifying faults in transmission systems. In the first phase, unsupervised learning techniques like K-Mean clustering are used to assign labels to datasets for transmission line fault classification. During the second phase, four machine learning techniques called logistic regression (LR), decision tree classifier (DTC), random forest classifier (RFC), and XGBoost Classifier (XGB) are employed to identify faults. Applications are validated on fault detection datasets. The tested approach provides an efficient model for fault detection and classification in transmission lines, as well as a productive framework for fault detection prediction based on machine learning and ensemble learning methods. The experimental simulation results from this study show an accuracy of 83.6% for LR, 99% for DTC, 99% for RFC, and 99.9% for XGB, LightGBM, and CatBoost at 0.99995%. The paper's findings demonstrate the effectiveness of machine learning and ensemble learning techniques in accurately identifying and classifying transmission line faults at competitive performance indices.
Open Access: Yes
Optimal parameter extraction of equivalent circuits for single- and three- phase Power transformers based on arctic puffin algorithm accomplished with experimental verification
Publication Name: Results in Engineering
Publication Date: 2025-06-01
Volume: 26
Issue: Unknown
Page Range: Unknown
Description:
The power transformer is a critical device in power systems. This paper addresses one of the major problems which hopes to enhance the accuracy of estimation of parameters, which is critical in power transformer modeling, maintenance, and operating efficiency. In that context, this work estimates the parameters of single- and three-phase power transformers by a new optimizer called Arctic Puffin Optimization Algorithm (APO). The algorithm is intended to improve estimation of transformer parameters with the goal of reducing the error incurred between the estimated and actual values of the parameters. To verify the accuracy of the APO, experimental measurements were conducted on single- and three-phase transformers. The assessment of the algorithm's effectiveness was performed against the effectiveness of other commonly used estimating methods. The results have shown that APO increases the accuracy of estimation of the parameters of both single- and three-phase transformers to considerable levels. Dependability of the APO was established by experimental verification, which disclosed an ultimate connection between the resultant quantities and actual measurements. The study also confirmed APO can be useful for transformer parameter estimation because APO converges more rapidly and more precisely compared with traditional methods of the literature.
Open Access: Yes
Adaptive Speed Tuning of Permanent Magnet Synchronous Motors Using Intelligent Fuzzy Based Controllers for Pumping Applications
Publication Name: Processes
Publication Date: 2025-05-01
Volume: 13
Issue: 5
Page Range: Unknown
Description:
This study focuses on enhancing the performance of Permanent Magnet Synchronous Motors (PMSMs) in pumping applications by improving motor torque through the integration of advanced control strategies. The dq-axis model of a PMSM is utilized to facilitate precise control and dynamic response. The proposed approach combines Fuzzy Logic Control (FLC) and Fuzzy Proportional-Integral-Derivative (fuzzy PID) controllers with Vector Control (VC) inverters, specifically designed for PMSMs with salient rotor structures. The salient rotor design inherently provides higher torque density, making it suitable for demanding applications like pumping. The FLC and fuzzy PID controllers are employed to optimize the motor’s dynamic response, ensuring precise torque control and improved efficiency under varying load conditions. The VC inverter further enhances the system’s performance by enabling rapid torque and flux control, reducing torque ripple, and improving overall motor stability. The simulation results demonstrate that the proposed control strategy significantly increases motor torque, enhances energy efficiency, and reduces operational losses in pumping applications. This makes the system more reliable and cost-effective for industrial and agricultural pumping systems, where high torque and energy savings are critical. The integration of FLC, fuzzy PID, and VC with a salient-rotor PMSM offers a robust solution for achieving superior motor performance in real-world pumping scenarios. This work contributes to the development of smarter, more efficient pumping systems, paving the way for enhanced industrial automation and energy management.
Open Access: Yes
DOI: 10.3390/pr13051393
Detection of Harmonic and Interharmonics Contents in Water Desalination Plants' Distribution System Based on Deep Learning Algorithms
Publication Name: Energy Science and Engineering
Publication Date: 2025-11-01
Volume: 13
Issue: 11
Page Range: 5451-5464
Description:
Water desalination plants are significant consumers of electric power, making them some of the largest energy users in power grids. Their electricity consumption presents an urgent challenge for efficient and sustainable operation, and they are among the most power-quality-threatening customers for utilities. This study presents three distinct strategies to enhance prediction accuracy and extend forecasting horizons, aiming to reduce algorithmic and hardware delays. Additionally, it suggests effective methods to compensate for voltage fluctuations, voltage flicker, and dips arising from desalination plants. The paper also discusses forecasting harmonics and interharmonics in current signals. Furthermore, it integrates the above techniques into a comprehensive computing system, along with an active power filter (APF) scheme, within the Simulink framework. A comparison is drawn between the performance of predictive techniques in an APF and a conventional, non-predictive APF. The proposed data augmentation method successfully increases prediction accuracy. By effectively forecasting upcoming waveforms, it reduces algorithmic and hardware delays. These techniques are designed to address multiple power quality issues simultaneously, including harmonics, interharmonics, flicker, and voltage dips, which often coexist in the spectrum as interharmonics. The suggested approach employs Long Short-Term Memory (LSTM) networks combined with the Jetson TX2 embedded artificial intelligence computer to accelerate machine learning applications. This method has proven effective in predicting and classifying time series data, including harmonics, interharmonics, and raw current signals, achieving 100% accuracy. This eliminates the need for designing specific low-pass filters. The evaluation results for this time-domain deep learning-based technique will be reported in the subsections below. The implementation is conducted in Python using the KERAS deep learning framework and TensorFlow backend, and it is evaluated on a workstation equipped with an Intel i7 processor running at 4.0 GHz and 48 GB of RAM.
Open Access: Yes
DOI: 10.1002/ese3.70251
Optimal harmonics prediction for distribution systems powered by multi-energy sources using bidirectional long-short term memory combined with data sequence
Publication Name: Applied Soft Computing
Publication Date: 2025-12-01
Volume: 184
Issue: Unknown
Page Range: Unknown
Description:
A multi-energy resource aims to maintain a balance between energy output and load consumption and to ensure power continuity during different operating conditions. The harmonic distortions can be estimated from the output current of a harmonic source, which may not fully reflect its true harmonic distortions due to the interactions between the state changes at the power network level and the harmonic sources. System operators monitor each system's harmonic performance under different conditions of operation to find the actual contribution of grid-connected systems to harmonic-related issues. Development of machine learning algorithms leads to effective progress in the harmonic prediction and computation. In this paper, the combined data sequencing, and Bidirectional Long-Short Term Memory (Bi-LSTM) network has been exploited for the real-time harmonic prediction of future events in multi-energy sources. The validity of the proposed Model including the applications of ANFIS, ANNs, MLRA and LSTM is conducted on the two standard systems as IEEE 9-bus and IEEE 34-bus multi energy resources system that is associated with PV systems. The simulation results, based on climate changes of solar irradiance and ambient temperature in PV systems, demonstrate that the proposed methods can accurately forecast changes in total harmonic distortion (THD) as well as the voltage profile at the point of common coupling. The performance of Bi-LSTM, original LSTM, Machine Linear Regression (MLR), and Artificial Neural Networks (ANNs) techniques were assessed. These findings provide valuable insights. Four performance validation indices, RMSE, R-squared and MSE are considered to assess the performance of the competitive learning algorithms. The results showed that in the model IEEE 9-bus, Bi-LSTM outperformed all the applied methods as its RMSE value was 0.000019 while its MSE value was 3.61e-10 and finally, the Bi-LSTM had a higher value squared error (R2) was equal 1 which indicates the effectiveness of Bi-LSTM for predicting sequential total harmonic distortion. On the other hand, in case study of IEEE 34-bus, the RMSE, MSE and R2 are 0, 3.276e-30 and 1 using Bi-LSTM which means that the Bi-LSTM leads to the best performance validation indices compared to other competitive algorithms for the tested multi-energy systems.
Open Access: Yes
Data-driven modelling of thermal conductivity in electrically aligned PDMS–diamond composites with experimental verification
Publication Name: Applied Thermal Engineering
Publication Date: 2025-12-01
Volume: 280
Issue: Unknown
Page Range: Unknown
Description:
Polymer-based composite material optimization is a key technology for achieving the desired thermal management in heat conduction sheets used in electronics and aerospace. Diamond particles are widely used as thermally conductive fillers in a liquid of poly di-methyl siloxane (PDMS) matrix because of their unique thermophysical properties. Electrical alignment is a powerful approach for filler alignment to achieve higher thermal conductivity. Meanwhile, practical experiments require substantial time, resources and consumable energy due to extensive testing. Therefore, it is essential to develop a highly robust predictive model for estimating thermal conductivity. This paper proposes a data-driven-based model that investigates a novel decision tree (DT) regression model for predicting thermal conductivity based on electrical alignment parameters, aiming to identify the optimal experimental conditions that achieve higher thermal conductivity. In this study, electrical alignment parameters, namely voltage, frequency, and rotational speed, are selected as descriptors for modelling and computing thermal conductivity. Correlation and multicollinearity analyses are conducted to evaluate the relationships among these descriptors. Three machine learning approaches, including Decision Tree, Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), are investigated alongside six empirical regression models. The predictive model-based refined DT achieves high accuracy with the lowest mean square error of 0.0004 and a higher coefficient of determination (R-squared) of 0. 9751on testing data, respectively. This indicates that the model is capable of accurately predicting the thermal conductivity of hybrid nanofluids over a wide range of hybrid nanoparticle combinations with high closeness to the experimental records. This predictive model condition highlights the potential of DT-based method to precisely compute the thermal conductivity of PDMS-diamond composite based on the applied electrical alignment parameters.
Open Access: Yes
Multi-objective transit search optimizer for dynamic reconfiguration of standard and realistic radial distribution networks with hosted renewables and EV charging stations
Publication Name: Results in Engineering
Publication Date: 2025-12-01
Volume: 28
Issue: Unknown
Page Range: Unknown
Description:
The expansion of electric vehicle (EV) adoption, seasonal changes in power demand, and the adoption of renewable energy sources (RES) have posed new, intricate operational problems within distribution networks. These include additional power losses, excess yield gaps, drops in voltage levels, and reduced operational flexibility. This work propose a complete optimization model that consists of several steps to enhance the performance and resilience of radial distribution systems (RDS) with respect to daily and seasonal variations using the Transit Search Optimization (TSO) algorithm. Such strategies include optimal allocation of RES, placement of electric vehicle charging stations (EVCS), dynamic network reconfiguration, and improved unit commitment strategies to enhance power quality, mitigate losses, and maintain stable voltage levels. A multi-objective function opts to achieve optimal RES hosting capacity together with optimal tie-switch settings across scenario-based summer, winter, spring, and fall shifts. Simulations were done on both a modified IEEE 33-bus system and a practical 51-bus distribution network. A multi-function framework on TSO is employed for optimal dynamic reconfiguration strategy to improve the performance of the distribution network considering the dynamic behavior of power profiles of wind speed and solar irradiation over a 24-hour period with varying seasons. The aim of the proposed methodology is to optimize the system performance throughout the day under hourly changes in loads and weather conditions. Technical and economic benefits for selection of RES and reconfiguration strategy applied to boost system effectiveness and accommodate more customers. The results of this study show that joint placement of RES integrated with seasonal reconfiguration done markedly enhances efficiency, improves voltage and the voltage stability index, increases operational flexibility, and guarantees reliable supply during peak EV charging and periods of low renewable generation. The optimization also demonstrates robustness against generation and demand uncertainties.
Open Access: Yes
Development of hybrid optimization approach combined with AI-based techniques for prediction of electrical fields in overhead transmission lines
Publication Name: Journal of Supercomputing
Publication Date: 2025-11-01
Volume: 81
Issue: 16
Page Range: Unknown
Description:
Getting a precise estimate of electric fields around extra-high-voltage (EHV) transmission lines is essential for keeping the public safe, ensuring environmental compliance, and planning infrastructure effectively. Unfortunately, traditional numerical methods often struggle with accuracy and can be slow to converge, which makes them less suitable for large-scale projects. This study introduces a hybrid computational framework that combines the Charge Simulation Method (CSM) with the Firefly Algorithm (FA). This combination helps optimize the number, position, and strength of simulation charges, leading to better modeling accuracy and efficiency. Additionally, we have trained three artificial intelligence (AI) models: Multilayer Perceptron Neural Network (MLPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Squares Support Vector Machine (LS-SVM) on real-world field data to reliably predict electric field values. Notably, LS-SVM is being used in this context for the first time and has shown to outperform the other models in accuracy, generalization, and speed. We evaluated the proposed CSM-FA hybrid model alongside AI predictions using metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2), revealing significant improvements over traditional methods. Given the heavy computational demands of the optimization and learning phases, we utilized high-performance computing (HPC) resources for implementation. This work not only advances algorithmic innovation and AI-assisted simulation but also enhances HPC applications, providing a scalable and precise solution for real-time field monitoring and regulatory assessments. The methodology aligns well with the scientific goals of The Journal of Supercomputing and fosters advanced research in intelligent power system modeling.
Open Access: Yes
A Review of Modern Strategies for Enhancing Power Quality and Hosting Capacity in Renewable-Integrated Grids: From Conventional Devices to AI-Based Solutions
Publication Name: CMES Computer Modeling in Engineering and Sciences
Publication Date: 2025-01-01
Volume: 145
Issue: 2
Page Range: 1349-1388
Description:
Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic (PV) systems integration associated with varying loading and climate conditions. This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation, with a focus on harmonic mitigation, voltage regulation, power factor correction, and optimization techniques. The impact of passive and active filters, custom power devices such as dynamic voltage restorers (DVRs) and static synchronous compensators (STATCOMs), and grid modernization technologies on power quality is examined. Additionally, this paper specifically explores machine learning and AI-driven solutions for power quality enhancement, discussing their potential to optimize system performance and facilitate renewable energy integration. Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation, including the allocation of distributed energy resources and the tuning of control parameters. Added to that, this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation. By synthesizing existing research, this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization, highlighting future research directions that enhance power quality and facilitate renewable energy integration.
Open Access: Yes
Optimal scheduling of electric vehicle charging and discharging using two optimization paradigms
Publication Name: Results in Engineering
Publication Date: 2026-03-01
Volume: 29
Issue: Unknown
Page Range: Unknown
Description:
Electric Vehicles (EVs) play a pivotal role in advancing environmental sustainability and accelerating the transition toward clean energy systems. However, large-scale EV adoption poses significant operational challenges, particularly when charging and discharging activities are uncoordinated, potentially leading to elevated peak demand and increased grid stress. Effective scheduling techniques are therefore essential to ensure reliable integration of EVs into modern power systems. This study provides a rigorous comparative evaluation of two metaheuristic optimization paradigms for EV charging and discharging scheduling: the traditional Particle Swarm Optimization (PSO) algorithm and the more recent Transit Search Optimization (TSO) algorithm. Using an identical system configuration and EV dataset, the study assesses the performance of both approaches based on peak power reduction, cost minimization, and overall system efficiency. Results demonstrate that while enhanced PSO scenarios exhibit noticeable improvements over earlier literature, TSO consistently achieves superior outcomes due to its stronger exploration-exploitation balance. In particular, TSO attains a 46.23 % reduction in average EV charging cost and achieves the lowest power-loss levels across all tested scenarios. Relative to the best previously published benchmarks, TSO further improves peak power consumption by 1.6 % and total charging cost by 6.1 %. These findings highlight TSO’s strong potential as a high-efficiency scheduling tool for large-scale EV integration in future smart grid environments.
Open Access: Yes
Multi-Stage Centralized Energy Management for Interconnected Microgrids: Hybrid Forecasting, Climate-Resilient, and Sustainable Optimization
Publication Name: CMES Computer Modeling in Engineering and Sciences
Publication Date: 2025-01-01
Volume: 145
Issue: 3
Page Range: 3783-3811
Description:
The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO2 emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address these issues, this paper presents a novel three-stage centralized EMS for interconnected microgrids. The first stage involves comprehensive data analysis to extract meaningful patterns. The second stage introduces a hybrid forecasting framework that integrates stochastic (Prophet) with machine learning (BiLSTM) techniques to improve prediction accuracy under uncertainty. In the third stage, a modified linear programming approach leverages the improved short-term forecasts to optimize energy sharing between microgrids, with the aim of reducing operational costs, minimizing carbon emissions, and improving system stability under climate variability. The proposed EMS is designed to accommodate diverse microgrid configurations while maintaining computational efficiency. Four scenarios are considered to evaluate the proposed energy management strategy. The obtained results demonstrate that the proposed EMS significantly improves both forecasting accuracy and operational performance. The combined methods achieve the best performance among all tested models, with an RMSE of 0.0070, MAE of 0.0043, and R2 of 0.9988, corresponding to improvements of ΔRMSE = −0.2122 and ΔR2 = +0.7126 relative to Prophet. These substantial gains in predictive accuracy translate into more precise battery scheduling, reduced grid dependency, and optimized power dispatching, thereby significantly enhancing system efficiency, reliability, and sustainability. Overall, the results highlight the effectiveness of integrating hybrid forecasting with optimization-based EMS, providing a viable pathway toward high penetration of renewable energy sources in future power systems.
Open Access: Yes
Optimal parameter extraction of fuel cells based on interval branch-and-bound optimization algorithm
Publication Name: Energy Reports
Publication Date: 2026-06-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Fuel cells play an important role in reducing environmental impacts to produce cleaner electricity. Numerical models are used to simulate their performance and build efficient observers in real use. The accuracy of these models is a major concern, as they can be parameterized by several values. Most of the previous works study the estimation of these parameters using various metaheuristics. While these methods are stochastic and do not provide any proof of optimality, the current paper introduces a global optimization method to accurately bound the optimal root mean square error between the parameterized model and some experimental data. The proposed algorithm is based on a deterministic Interval Branch-and-Bound optimization (IBBO) framework. Interval arithmetic ensures set-based computations to safely bound the objective function value. Four types of fuel cells, with their experimental data, are used to demonstrate the efficiency of the proposed methods. IBBO results are compared with some competing optimization methods used in the literature. They show a better accuracy for the computed feasible solutions (upper bounds) and a guaranteed value of the best possible solutions (lower bounds). This last information is not possible to obtain with metaheuristic algorithms. Compared to other Branch-and-Bound algorithms, IBBO proposes a new mix of mechanisms (e.g. advanced constraint propagation, specific search heuristic and feasible point finding method). Due to the deterministic nature of IBBO, results can be repeated. Its convergence analysis is detailed on four fuel cells from which a real test system based on Scribner technology is used to demonstrate the accuracy and robustness of IBBO on several usage scenarios.
Open Access: Yes
Hybrid Brown-Bear and Hippopotamus Optimization with Quasi-Opposition-Based Learning for Optimal Power Flow with Renewable Energy Integration
Publication Name: Computers and Electrical Engineering
Publication Date: 2026-03-01
Volume: 131
Issue: Unknown
Page Range: Unknown
Description:
The optimal power flow (OPF) problem is a highly nonlinear and complex multi-dimension optimization problem, especially with the increased penetration of uncertain renewable energies (RES). In this line, this paper presents the Hybrid Brown-Bear and Hippopotamus Optimization Algorithms with Quasi-Opposition-Based Learning (HBOA-QOBL) to enhance multi-dimension OPF solution. The algorithm combines the strengths of Brown-Bear optimizer, which excels in exploration and adaptive search mechanisms, and the Hippopotamus optimizer, known for its social behavior modeling and localized search strategies. By integrating QOBL, the HBOA-QOBL improves exploration through the generation of quasi-opposite solutions, allowing for a wider search of the solution space and reducing the risk of premature convergence. Adaptive search mechanisms embedded in HBOA-QOBL enhance exploitation by dynamically adjusting search behaviors during iterative power dispatch tuning, enabling improved fine-tuning of generation schedules and voltage profiles. The effectiveness of the proposed method is evaluated on the IEEE 30-bus, 57-bus, and 118-bus test systems for multiple dimension OPF objectives, including fuel cost minimization, emission reduction, power loss reduction, voltage deviation minimization, reactive power loss reduction and the voltage stability indicator (L-index). Simulation results indicate faster convergence compared to conventional techniques, achieving near-optimal solutions within 200 iterations, with a standard deviation of 63.8%, demonstrating superior technical and economic performance relative to previous research. Key convergence parameters such as population size, maximum iterations, and learning factor are explicitly tuned to enhance both exploration and exploitation. Simulation results confirm that HBOA-QOBL outperforms conventional optimization techniques in terms of solution quality, convergence speed, and stability, establishing significant improvement in the technical and economic issues.
Open Access: Yes
Mitigation of inductive coupling effects on buried pipelines using gradient control conductors of overhead line configuration and hippopotamus optimization algorithm
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
By electromagnetic perturbation effect, the extra-high voltage (EHV) overhead transmission lines can cause significant induced voltages and currents on buried metallic pipelines located in the immediate vicinity. These voltages can present a source of hazard both for the structural I ntegrity of the metallic pipeline and for the safety of personnel responsible for operation and maintenance. This paper proposes the quasi-static modeling of the electromagnetic interference to which a buried metallic pipeline will be subjected nearby an extra-high voltage (EHV) overhead transmission line, under steady-state operating conditions of the power electrical grid. Using the electrical network analysis method to evaluate the induced voltage levels and its effects on the buried pipeline; also, to propose a mitigation strategy if necessary. The results obtained show that the values of the AC induced voltage on the buried pipeline are significant and exceed the limits defined by the international NACE standard. They can cause a risk of electrocution for intervention personnel and accelerate the process of metal corrosion. Therefore, the gradient control mitigation technique of the conductors and their optimal geometric arrangement of EHV transmission line using Hippopotamus Optimization (HO) algorithm were proposed to reduce AC induced voltages within the permissible safety limits, according to the requirements of the NACE Standard. Finally, it should be noted that the implementation of these mitigation approaches have led to remarkable results in eliminating potential risks.
Open Access: Yes
Optimal techno-economic framework for the design and control of off-grid solar-battery EV charging stations in Benban-Egypt
Publication Name: Journal of Energy Storage
Publication Date: 2026-07-30
Volume: 167
Issue: Unknown
Page Range: Unknown
Description:
This study presents a full design and validation method for an independent PV-battery-based Electric Vehicle (EV) charging station in Benban, Aswan. Using HOMER Pro, the system's techno-economic sizing is done. MATLAB/Simulink simulations are used to check its dynamic operation and MPPT performance. A realistic EV profile is considered (600 kWh per day, with a peak demand of 48.9 kW). Simulink uses population-based algorithms like Gazella Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) to improve dual PI controllers. The study looks at how well the system works for both the economy and the environment. The best setup has an Levelized Cost of Energy (LCOE) of $0.111/kWh, a Net Present Cost (NPC) of $430,468, and saves about 212 tons of CO₂ per year, with an unmet load of less than 2%. The results of the simulation show that the DC bus voltage is stable, the MPPT tracking is efficient, and the battery can be charged and discharged reliably even when the sun is not shining. In general, the results show that the proposed off-grid EV charging system is both technically sound and economically competitive for areas with high solar potential.
Open Access: Yes
Collaborative precise modeling of fuel cells based on adaptive Huber loss function and wild horse optimizer with critical statistical analysis
Publication Name: International Journal of Hydrogen Energy
Publication Date: 2026-06-15
Volume: 242
Issue: Unknown
Page Range: Unknown
Description:
Precise estimation of fuel cell parameters is critical for optimizing performance and developing energy systems. However, experimental data are often affected by outliers stemming from inaccurate measurements, transient operating conditions, or environmental variations. In this line, this study proposes a robust approach for estimating proton exchange membrane fuel cell (PEMFC) parameters. This study focuses on the steady-state current–voltage (I–V) characteristics and performs parameter extraction for a semi-empirical model. The proposed estimation framework employs the collaboration of the Huber loss function (HLF) in conjunction with adaptive hyperparameter and the metaheuristic Wild Horse Optimizer (WHO) to compute seven unknown PEMFC parameters. The impact of different hyperparameter (δ) values is examined on the performance of the HLF while estimating key fuel cell parameters. The sensitivity of the estimating process to the δ-value is explored using measured and estimated datasets, including accuracy, convergence rate, and resilience. The WHO-based approach is adopted to address and mitigate issues such as premature convergence and entrapment in local optima, which are common challenges in existing optimization strategies. The proposed model has been tested and verified through three test samples of standard commercial PEMFC units as benchmarks. The simulation results demonstrate that the WHO exhibits robust performance across the three benchmark PEMFC systems. Furthermore, the proposed model's generalization capability is validated under a range of operating conditions using polarization curves generated at different temperatures and cathode stoichiometries. A single globally specified parameter set reliably predicts fuel cell performance across these diverse conditions, as evidenced by its consistent ability to deliver high-quality solutions with an extraordinary level of precision under predefined experimental conditions. The proposed estimation framework outperforms three commercial PEMFC units (NedStack-PS6, Horizon-500 W, and BCS-500W), achieving Huber loss values of 1.03277845, 0.00562094, and 0.00584889, respectively. The adaptive HLF with hyperparameter (δ) ranging from 0.5 to 2.0 efficiently tackles outliers and improves convergence speed. While the hyperparameter (δ) in previous studies was kept constant, δ = 1. The proposed estimation framework closely matches the experimental data and offers significantly higher accuracy compared to existing competing methods in the literature. The results reveal that the suggested HLF enhances the robustness and immunity of the WHO optimizer, and it outperforms traditional approaches such as steady-state error.
Open Access: Yes
