International Transactions on Electrical Energy Systems最新文献

Recent advancements in electric vehicles (EVs) and modern power systems offer broad opportunities for integrating renewable energy solutions. Solar photovoltaic (PV) systems, in particular, inherently avoid harmonic injection at the source due to the absence of alternating current (AC) power. However, consistently extracting maximum power from PV panels remains a technical challenge—especially under partial shading conditions where conventional algorithms struggle to locate the global maximum on the P–V curve. The recently introduced Jaya optimization algorithm has demonstrated improved performance through its reduced control variables and lower computational demand. Despite these advantages, its random nature often results in wide output fluctuations during transient periods, leading to limited exploitation near the global maximum. To overcome these drawbacks, this article introduces an enhanced Jaya algorithm designed to improve exploitation efficiency while tracking the global maximum power point (MPPT). A Luo DC–DC converter is employed due to its low output ripple, making it suitable for stable power conversion. Extensive simulations and experimental tests were conducted using 4S and 6S PV array configurations rated at 240 W and 360 W, respectively. The proposed method was benchmarked against seven other contemporary optimization algorithms and proved superior—achieving MPPT within 0.1 s and maintaining efficiency above 99% under all shading conditions. Further validation through statistical indices such as IAE, ITAE, ISE, and ITSE confirms the proposed approach’s robustness and suitability for real-time, fast renewable energy applications.

One of the most beneficial and effective methods for reducing the power losses of the distribution networks (DNs) is using distributed generations (DGs). The issue of optimal placement and sizing of DGs is a challenge that needs to be investigated carefully, as an improper location and sizing lead to a negative effect on the DN. In this work, an IEEE 33-bus is used as a test system for optimal placement and sizing of four DGs, three of them being photovoltaic (PV) sources and the fourth is a wind turbine (WT). The environmental data (irradiance, temperature, and wind speed) of Baghdad city (latitude: 33.29°, longitude: 44.38°) are used for training the artificial neural networks (ANNs) to forecast the day ahead values of the environmental variables for calculating the power production of PVs and WT. Particle swarm optimization (PSO) technique is used to optimize the location and sizing of the DGs. The operation cost of the system is optimized using genetic algorithm (GA) depending on the optimized sizing and placement of the DGs. Four electrical vehicles charging stations (EVCSs) are interconnected to the implemented DN with considering the uncertainty of hourly charging power demand using the queuing model. The optimal cost of the EVCSs is determined by using fuzzy logic system (FLS) to optimize the energy management of the daily power dispatch and peak power shifting to meet the peak power production of the DGs. The power losses are minimized by 50%, enhancing the voltage profile of the distribution system, and the operation cost is minimized by 19%. The annual operation cost saving of EVCSs is found to be 44.3%.

This paper investigates the application of reinforcement learning (RL) techniques for optimizing proportional–integral–derivative (PID) controller parameters in gas turbine speed control systems. The research employs the Rowen mathematical model as the foundational framework and introduces a novel approach utilizing twin-delayed deep deterministic policy gradient (TD3) algorithms. The methodology integrates machine learning with classical control theory to address the persistent challenges of maintaining optimal turbine speed during both transient startup phases and steady-state operations. Implementation was conducted using a simulation environment based on MATLAB/Simulink, with the General Electric 5001M heavy-duty gas turbine serving as the reference system. The RL agent was designed to interact with the simulated environment, continuously refining controller parameters to minimize performance metrics including integral error values, rise time, and settling characteristics. Comparative analysis between the proposed TD3-optimized PID controller and conventional tuning methods demonstrates significant performance enhancements across multiple control criteria. The optimized system achieved notable reductions in settling time, overshoot magnitude, and steady-state error, while also demonstrating improved disturbance rejection capabilities under variable load conditions and sensor noise.

With the increasing integration of renewable energy sources into the power system, challenges such as wind curtailment and operational flexibility are becoming more prominent. Therefore, this paper proposes a low-carbon optimised strategy for integrated energy system (IES) that considers the efficient use of hydrogen energy and the flexible operation of carbon capture power plant (CCPP)–methane reactor (MR)–hydrogen-doped combined heat and power (HCHP) combination. First, a model for the efficient utilisation of hydrogen energy containing wind power to hydrogen, hydrogen to thermoelectricity, gas-mixed hydrogen and hydrogen to methane was established. Secondly, the co-ordination mechanism among CCPP, HCHP and MR is explored, and the flexibility improvement of CCPP and HCHP is introduced by the liquid storage tank (LST) and Kalina cycle, respectively, and the joint CCPP-MR-HCHP flexible operation model is constructed. Finally, the integrated demand response (IDR) of electricity and heat is introduced, and a novel low-carbon optimisation model of the IES is established by integrating low-carbon and economic considerations. The simulation part of the example set up different scenarios for comparison, and the results showed that the introduction of an efficient hydrogen energy utilisation model can effectively improve the level of wind power consumption and reduce the total system cost and carbon emissions by about 11.35% and 24.73%, respectively. In addition, the proposed CCPP-MR-HCHP model can significantly improve the operational flexibility of the system, reducing the total system cost and carbon emissions by approximately 8.51% and 11.06%, respectively, compared to traditional operating modes.

Accurate estimation of neutral current (I_n) in industrial three-phase power systems is critical for harmonic suppression, equipment protection, and operational safety. This study proposes an ensemble regression framework optimized by a multiobjective genetic algorithm (GA) using 12,328 real-field measurements based on 29 electrical characteristics (P, Q, S; I_rms; U_rms; PF, dPF; I_THD, etc.). The GA simultaneously determines the selection and weights of the base learners (SVR, ANN, GPR, RF, GBR, XGB, DT, and GPR-RQ), improving eight performance metrics together: RMSE, MAE, SMAPE, MdAPE, R², EVS, maximum error, and PBIAS. Comparative analyses show that GA achieves high accuracy in 10-fold cross-validation compared to PSO, SA, random search, and average voting strategies (e.g., R² = 0.9972, RMSE = 1.83, and SMAPE = 10.31%); unseen test data maintained competitive overall performance (e.g., R² = 0.9820; SMAPE = 56.17%). In noise robustness, R² = 0.9933 was achieved in target-injected disturbance scenarios. Optimization reached Pareto convergence in approximately 50 generations. In the explainability analysis, SHAP and LIME outputs showed significant differences (p < 0.05) in 28 out of 29 variables; despite low inter-method correlation (Pearson ≈ −0.022), they provided complementary insights. The results demonstrate that the GA-XAI–supported ensemble provides high accuracy, interpretability, and applicability for I_n prediction. To the best of our knowledge, this study presents the first I_n prediction framework that statistically compares SHAP and LIME when used together with a GA-optimized ensemble and reports the process in a reproducible MATLAB script. We translate these distinctions into a practical protocol: SHAP for global monitoring and policy and LIME for case-level triage, thus enabling practitioners to confidently leverage complementary XAI signals during operations.

Reliable electricity access is crucial for sustainable development, yet Bangladesh’s coastal regions face challenges due to an unreliable grid. The off-grid hybrid system based on renewable energy is recommended in the existing research for Bhasan Char, optimized through the application of HOMER Pro software with the components being solar PV (48.3 kW), wind turbine (40 kW), diesel generator (50 kW), battery storage (91 strings), and a system converter (34.7 kW). Five different system configurations were analyzed, and Case 1 was the most cost-effective with a net present cost (NPC) of 25.87 million Bangladeshi taka (BDT), cost of energy (COE) of 16.29 BDT/kWh, and operating cost of 958,523 BDT/year. The system also offers a high renewable fraction (93.9%), low emissions (7651 kg CO₂/year), and payback period of 2.74 years. In addition, sensitivity analysis and heatmap correlation using Python were also utilized to compare system performance under various situations. Results show a low-cost and clean model that uses low fossil fuel but is highly economically feasible. The study submits an expandable model for off-grid coastal areas’ sustainable electrification that is consistent with Bangladesh’s energy security and conservation policies.

The integration of solar power systems into cruising ships is gaining popularity in the marine sector due to the restrictions imposed by the Marine Pollution Protocol and the rapid growth of photovoltaic (PV) technology. However, this integration brings various challenges in the operation of the ship energy system, including resource uncertainty, power imbalance, and reduced service reliability. Therefore, this study proposes a novel three-stage operation strategy for ship multienergy systems to compensate for the uncertainty of PV generation. In Stage 1, a day-ahead scheduling process is performed to determine the setpoints of major system components. The goal is to minimize operating costs while meeting electrical, heating, and cooling demands. In Stage 2, a deep neural network-based PV prediction model is developed. Particle swarm optimization is used to achieve fast convergence and high accuracy. A detailed statistical analysis is then applied for early detection of data drift, which may cause a significant drop in prediction accuracy. The uncertainty of PV output is then estimated based on the new trends observed in the incoming dataset. In Stage 3, a demand response (DR)-based scheme is introduced to compensate for the uncertainty of PV power, identified in Stage 2. The DR programs allow sharing the load demand among different intervals by adjusting controllable loads. As a result, the amount of power mismatches caused by the uncertainty factor has decreased. Finally, simulation results also demonstrate that the amount of load shedding requirement in the ship energy system is significantly reduced using the proposed method.

This research offers a hands-on examination of using Jordan’s naturally abundant, high-purity Wadi Rum silica sand (SiO₂ > 99%) as an affordable material for thermal energy storage (TES) in concentrated solar power (CSP) systems aimed at home-scale applications. During the 3 days of continuous testing, the setup achieved a peak heat transfer rate of 18.7 kW, heating water by 27°C, reaching a top outlet temperature of 54.9°C. The silica sand proved to be an effective thermal reservoir, attaining internal temperatures between 67°C and 69°C. On average, the system produced 11.2 kWh of thermal energy per day, with an overall efficiency of 50.2%, while cutting daily CO₂ emissions by about 2.07 kg. The economic assessment showed a payback time of just 1.49 years, which reduced to 1.04 years with a 30% subsidy. Altogether, the findings confirm that Wadi Rum silica sand offers a practical, sustainable, and financially attractive pathway for thermal storage, directly advancing Jordan’s drive toward a cleaner and more self-reliant energy future.

Under the carbon neutrality framework, the traditional coal chemical industry requires the upgrade and transformation of industrial parks to reduce carbon emissions while maintaining economic benefits. This study establishes a green electricity–hydrogen coupled coal chemical system and proposes a robust optimization model incorporating uncertainties in wind and solar power. First, a model for green electricity-driven coal chemical production is developed based on thermodynamic principles, considering material and energy flows. Second, utilizing vine copula theory and Markov transition matrices, a confidence interval-based uncertainty set is constructed to characterize the stochastic nature of renewable energy. Finally, a robust optimization model integrating system dynamics and uncertainty sets is formulated, implemented on the MATLAB–YALMIP platform, and solved using the CPLEX solver. Results show that the proposed uncertainty set enhances wind–solar variability capture (correlation 0.0253 higher than the polyhedral uncertainty set). The system achieves about 1.2-Mt CO₂ yr⁻¹ reduction and annual revenue between 0.48 and 3.45 billion CNY (average 1.42 billion CNY), proving both robustness and economic advantage. In terms of economic assessment, the model not only overcomes the limitations of wind–solar data acquisition but also enables reasonable evaluation under diverse scenarios. This work provides novel insights into the green transformation and economic assessment of the coal chemical industry and contributes to economic budgeting and benefit evaluation for other types of industrial parks.

In recent years, managing power in the electrical systems that utilize intelligent infrastructures has become a modern solution for operators. This technology enables more effective control and improves the overall performance of electrical networks. Accordingly, this paper focused on economic and technical power management in an intelligent electrical distribution network (IEDN) with demand response programs (DRPs) at day-ahead. The proposed approach is implemented in IEDN by the bilayer optimization approach considering the contribution of the electrical distribution company (EDC) and consumers. In the first layer, implementation of the DRPs such as local power generation (LPG) by battery storage systems (BSSs), power load curtailment (PLC) program, and power load shifting (PLS) program is scheduled for minimizing bills of consumers. On the other side, in the second layer optimization, income of EDC is maximized and power losses of IEDN are minimized considering scheduled load demand in the first layer optimization. The optimization in both the layers is modeled as multiobjective functions, and optimization of consumers’ bills is done subject to power prices in EDC. The effect of the suggested approach is examined on technical metrics such as voltage profile and peak-to-average ratio (PAR) index. The improved grasshopper optimization algorithm (IGOA) and Shannon entropy decision-making method are used for solving bilayer optimization approach and multiobjective functions. In the end, the results reveal the optimal values of the objective functions of each layer, based on a comparative examination of different case studies, thereby considering consumer engagement.