Energy Science & Engineering最新文献

The increasing complexity and scale of modern power systems, combined with fluctuations in demand, modeling uncertainties, evolving network configurations, and time-varying characteristics, make load frequency control (LFC) increasingly challenging. Large frequency deviations can disrupt electric clock synchronization, alter AC motor speeds, affect magnetizing currents in transformers and induction motors, and impair the coordinated operation of systems. Conventional control methods often fail to effectively manage these uncertainties. This study evaluates LFC performance in a multi-area, multi-source interconnected power system using a novel Asymmetrical-Fuzzy-based Two-Degree-of-freedom Tilt-Integral-Derivative Controller with Low-Pass-Filter (AF-2DOF-TIDF) as a secondary control mechanism. The system comprises three unequal areas that integrate hybrid-thermal and hydropower plants, representing real-world asymmetries in capacity, inertia, and interconnections. The dynamic influence of Hydrogen-Aqua-Electrolyzer-Fuel-Cell (HAE-FC) units is analyzed relative to conventional setups. Controller parameters for TIDF, 2DOF-TIDF, and AF-2DOF-TIDF are optimized using the Skill Optimization Algorithm (SOA). Simulation results demonstrate that the proposed controller significantly enhances dynamic response, reducing overshoot, damping oscillations, and shortening settling times. The integration of a Unified Power Flow Controller (UPFC) further enhances frequency and power stability. Sensitivity analyses confirm the robustness of the proposed controller under varying loads and parameter uncertainties, with an average reduction of 68.4% in oscillation amplitude achieved.

Partial shading is a significant concern that causes a current mismatch between rows, resulting in local power peaks. Dynamic reconfiguration methods may not completely eradicate the current mismatch. Hence, a battery of similar capacity injects a compensation current to nullify the current mismatch. The main limitation of this approach is the selection of a battery with a similar capacity for all the rows. To address this shortcoming, the proposed study introduces the experimental verification of the optimal section of the battery-based adaptive reconfiguration (OBAR) technique is verified on 4 × 4 total-cross-tied PV array to reduce the current mismatch. The OBAR is implemented in two steps: initially, the adaptive reconfiguration technique is performed by switching circuit 1 to reduce the current mismatch. The OBAR algorithm monitors the existence of a current mismatch; if the mismatch persists, the switching circuit 2 selects the battery of suitable capacity from a battery bank of three ranges: 0.5 Ah and 18 V, 1 Ah and 18 V, and 1.5 Ah and 18 V based on the current mismatch. The OBAR is tested experimentally, and its performance is related to that of the total cross-tied array, adaptive reconfiguration, and battery-based current mismatch reduction technique. The experimental results reveal that the battery of 0.50 Ah is the optimal selection with a power enhancement of 67% to nullify the current mismatch. The economic analysis of the OBAR indicates its viability and it can be prolonged to PV array of any size.

The integration of the concentrated photovoltaic photothermal (CPVT) process with energy storage is an effective approach to address the volatility of direct normal irradiance. However, the direct adoption of electrical energy storage is confronted with issues related to energy density and cost. To address this, this article introduces an operation strategy utilizing a hydrogen and biomass collaborative energy storage to regulate the power supply and demand. When power falls short of demand, a hydrogen fuel cell (HFC) is deployed to bridge the deficit, and the remaining shortfall is offset through biopower. This ultimately provides a constant power load, as well as heat, cooling, and ammonia. Additionally, it includes CPVT, wind turbines, biomass gasification, gas turbine, pressure swing adsorption, ammonia synthesis reactor, waste heat recovery unit, proton exchange membrane water electrolysis, and HFC. Consequently, the overall operational performance is evaluated on a monthly and yearly basis. The results show that the system achieves annual average energy and exergy efficiencies of 87.77% and 67.16%, respectively. For the biomass pyrolysis heating process, the CPVT provides 19.18 MWh of heat, and the heat required for biomass pyrolysis is 19.16 MWh. The system's yearly average outputs of electricity, heat, and cooling are 104.28, 7.90, and 11.56 MWh, respectively, with hydrogen and ammonia storage reaching 258.94 and 59.07 kg/h, respectively. Economically, the system achieves profitability in the 6th year with a net present value of 24.35 MUSD. This study provides theoretical foundations for constructing high-efficiency, high-stability solar concentrated photovoltaic photothermal utilization.

Structural damage to batteries is a major contributing factor to safety incidents in electrochemical energy storage systems. Among the various types of damage, thermal abuse is particularly prevalent. However, effective methods for quantifying and detecting such damage remain in the early stages of development. Further investigations are required to gain a deeper understanding of the characteristics of battery structural damage and develop more accurate detection techniques. This study explores the thermal abuse behavior of lithium iron phosphate (LFP) batteries. The study examines the damage process induced by thermal abuse and presents a detection method that utilizes the generation of H₂ and CO as diagnostic markers. This method was validated under four distinct operational conditions, demonstrating its effectiveness in identifying thermal abuse. The findings introduce a novel gas-based approach for detecting structural damage. This method utilizes H₂ and CO as universal markers, enabling it to overcome the limitations of traditional impedance and temperature monitoring techniques. The method demonstrates exceptional efficacy in real-time identification of thermal abuse across various operating conditions.

The growing penetration of variable wind and solar generation poses operational challenges for power grids, while the rapid adoption of electric vehicles (EV) further intensifies system load stress. This study develops an optimized time-of-use (TOU) pricing strategy that incentivizes EV owners to shift charging to off-peak periods and curtail charging during peak demand, jointly minimizing system load volatility and user charging costs. A Wasserstein GAN with gradient penalty (WGAN-GP), an enhancement over the original WGAN, is employed to synthesize high-fidelity wind–solar generation scenarios that serve as reliable inputs for tariff optimization. A moving boundary method is applied to segment EV charging demand into dynamic time periods. Building on these components, we formulate a bi-objective TOU pricing model that explicitly incorporates EV users' price responsiveness. Case studies demonstrate the superior scenario generation performance of WGAN-GP and the tangible benefits of the proposed TOU strategy. The optimized pricing achieves a 9.09% reduction in average user charging cost and a 24.11% reduction in the peak–valley load gap, thereby mitigating system stress, enhancing the operational flexibility of the virtual power plant (VPP), improving renewable (wind–solar) utilization, reducing reliance on energy storage systems (ESS), and increasing the wind–solar absorption ratio.

The research considers an hourly residential load demand with a daily average of 988 kWh/day and investigates possible standalone systems, including solar panels (photovoltaic [PV]), wind turbines (WTs), diesel generator (DG), biogenerator (BG), and battery bank (Bat), to provide the load demand, for a case study located in Tabuk, Saudi Arabia, where the monthly solar radiation and wind speed are 5.74 kWh/m²/day and 5.33 m/s, respectively. In this study, enviroeconomic factors, including inflation and discount rates, capacity shortage and load demand, CO₂ and SO₂ penalties, diesel and biomass prices are considered, while they were not considered in the previous studies in Saudi Arabia. The results show that the net present cost and cost of energy of the optimized system are $1.03 M and 0.178 $/kWh, respectively. Additionally, the prices of diesel fuel and biomass have a significant impact on the CO₂ emissions of the system, even with a 10% increase in the renewable fraction. The results of sensitivity analyses show that increasing the CO₂ emission penalty from 20 to 80 $/ton leads to a decrease in CO₂ emissions by 50%. The effect of the initial cost of WT on the configuration of the optimal system is higher than that of PV, and increasing both prices significantly leads to an increase in CO₂ emissions.

There is significant importance in accurate prediction of axial flow compressor characteristics at different conditions. Different techniques and models have been proposed for performance prediction of axial compressors. Because of its lower computational cost and high speed the meanline algorithm has been widely applied in preliminary design and analysis of axial compressors; however, there are some inaccuracies at design and off-design conditions since the method relies on empirical correlations, which may be weak when applied to unconventional airfoil types. Applying some modifications to the meanline algorithm could improve its performance for a wider operating range with higher accuracy. This study aims to propose a modification for accuracy enhancement of meanline techniques to obtain characteristics of axial flow compressors at design and different off-design conditions. For this purpose, three scenarios are considered to modify the models. In the 1st scenario, coefficients are used for the deviation models while the pressure loss was not changed, in the 2nd scenario coefficients are applied for the pressure loss models and the deviation model is used as the base model and in the 3rd scenario, coefficients are used for both models. The coefficients are optimized by use of multi-objective genetic algorithm. It was found that use of the 3rd scenario leads to the best accuracy. In this scenario, the average absolute error in the estimated isentropic efficiency is reduced from 2.41% to 0.75% at 80% design rotational speed while the improvement in estimated mass flow rate at this speed is relatively minor. However, at design rotational speed after optimization, the average absolute error in the estimated isentropic efficiency drops from 6.35% to 0.96% while the estimated mass flow rate decreases from 4.97% to 0.094%.

This study investigated the combustion characterization of Petroleum Motor Spirit (PMS) and Liquefied Petroleum Gas (LPG) due to fuel tanker explosions in Nigeria using Aspen Plus software. The simulation characterized and determined the emission factors (EFs) of the associated emissions from explosions. The results showed that the associated air emissions are: NO₂, NO, CO₂, CO, SO₂, H₂O_(g), sulfur particulates (S_(s)), and carbon particulate/soot (C_(s)). On average, for PMS at any tanker explosion, the EFs are: 0.00041 kg/kg (S_(s)), 9.19E–06 kg/kg (SO₂), 0.01930 (CO₂), 8.02E–11 kg/kg (C_(s)), 0.98111 kg/kg for H₂O, to 8.02E–11 kg/kg for C_(s), 5.79E–11 kg/kg (NO₂), 2.06279 kg/kg (CO) 0.98111 kg/kg (H₂O_(g) and 6.05E–05 kg/kg (NO). For LPG at any tanker explosion, the EFs are: 2.9183E–14 kg/kg for (C_(s)), 1.57505 kg/kg (H₂O_(g)), 1.10006 kg/kg (CO₂), 3.6534E-08 kg/kg (NO₂), 0.00212 kg/kg (NO), 1.22086 kg/kg (CO), and 3.6337E-05 kg/kg (SO₂). EFs of the emission would be effective tools for the stakeholders and regulatory agencies of governments for proactive actions to quantify the arrest the negative impacts of emissions that are associated with PMS and LPG tanker explosions.

This paper proposes a new output feedback controller based on global fast terminal sliding mode with disturbances rejection to achieve accurate position tracking control for electro-hydraulic cylinder system. The typical mathematical model of the asymmetric cylinder electro-hydraulic system is established as Brunovsky form. Then high-order sliding mode observer is designed to estimate system states with only available displacement signal, and nonlinear disturbance observer is introduced to estimate and compensate for lumped disturbances including external disturbances, modeling errors and parameter uncertainties. Besides, a global fast terminal sliding mode control method is proposed to improve system convergence speed and position tracking accuracy, whose stability is proved through Lyapunov theory. Furthermore, simulations are carried out to verify the estimation performance of the designed state observer and disturbance observer, and the estimation accuracy of the disturbance observer reaches 82.71%. Finally, an asymmetric cylinder test rig is constructed and the experimental results show that the tracking accuracy of sinusoidal motion with 100 mm amplitude and step motion with 20 mm amplitude can reach 97.2% and 93.9% respectively compared with the total stroke of the motion, indicating the superiority of the designed output feedback controller.

The urgent need to combat climate change and reduce greenhouse gas emissions underscores the importance of transitioning to renewable energy as a sustainable alternative to fossil-fuel-based electricity. This study evaluates a novel financing mechanism for renewable electricity in Iran that leverages profits from the petrochemical industry and carbon tax revenues to support a feed-in tariff (FIT) model. By reallocating natural gas from inefficient fossil-fuel power generation to high-value petrochemical production, the approach enhances economic value, reduces CO₂ emissions, and promotes renewable energy deployment. Iran plans to expand its petrochemical production from 91.5 million tons in 2022 to 183 million tons by 2033, which will drive a 166% increase in demand for fuel and feedstock. Given these resource constraints, integrating renewable electricity into the grid is critical for sustaining industrial growth. System dynamics modeling indicates that carbon tax revenues could reach between $3.8 billion and $37.7 billion by 2033. Meanwhile, the profitability of the petrochemical sector shows wide variability depending on product prices, with a 326% spread between optimistic and pessimistic scenarios. The resulting FIT ranges from 6.24 to 20.29 cents per kilowatt-hour, with higher carbon taxes being particularly beneficial under low-price scenarios. This study presents a sustainable, market-aligned strategy for renewable energy financing that can enhance economic resilience and environmental performance in fossil-rich nations.