International Journal of Energy Research最新文献

Forecasting generation and demand forms the foundation of power system planning, operation, and a multitude of decision-making processes. However, traditional deterministic forecasts lack crucial information about uncertainty. With the increasing decentralization of power systems, understanding, and quantifying uncertainty are vital for maintaining resilience. This paper introduces the uncertainty binning method (UBM), a novel approach that extends deterministic models to provide comprehensive probabilistic forecasting and thereby support informed decision-making in energy management. The UBM offers advantages such as simplicity, low data requirements, minimal feature engineering, computational efficiency, adaptability, and ease of implementation. It addresses the demand for reliable and cost-effective energy management system (EMS) solutions in distributed integrated local energy systems, particularly in commercial facilities. To validate its practical applicability, a case study was conducted on an integrated energy system at a logistics facility in northern Germany, focusing on the probabilistic forecasting of electricity demand, heat demand, and PV generation. The results demonstrate the UBM’s high reliability across sectors. However, low sharpness was observed in probabilistic PV generation forecasts, attributed to the low accuracy obtained by the deterministic model. Notably, the accuracy of the deterministic model significantly influences the accuracy of the UBM. Additionally, this paper addresses various challenges in popular evaluation scores for probabilistic forecasting with implementing new ones, namely a graphical calibration score, quantile calibration score (QCS), and percentage quantile calibration score (PQCS). The findings presented in this work contribute significantly to enhancing decision-making capabilities within distributed integrated local energy systems.

The efficiency of solar photovoltaic (PV) energy conversion is significantly impacted by temperature, and soiling remains a critical factor influencing module performance. Alternative solutions, including cleaning, antisoiling coatings, the use of tracking systems, and the implementation of thermal mitigation strategies, have been explored to minimize the effects of soiling and thermal impacts on solar cell performance. This study approached the problem from a different perspective by employing a three-dimensional (3D) computational fluid dynamics (CFD) model to analyze the correlation between soiling and PV module temperature. The simulations incorporated varying dust thermophysical properties, installation geometries, and environmental conditions using user-defined functions (UDFs). Key findings revealed strong relationships between dust density, specific heat capacity, thermal conductivity, and cell temperature, mediated by thermal density. Maximum temperature rises were observed with low thermal density dust, elevating cell temperatures by up to 3.15%. Fixed configurations maintained lower temperatures by up to 1.7% compared to tracking systems. Dust temperature averaged 1.15% higher than the underlying cell, while directly soiled cells exhibited a 1.93% temperature increase compared to clean modules. Higher tilt angles experienced enhanced wind turbulence, reducing solar cell temperatures, whereas collectors oriented to prevailing winds showed lower temperatures, with minimal effects when winds aligned parallel to the installation azimuth. The study highlighted the dual role of dust thermal conductivity in heat transfer, where low values acted as insulators, elevating cell temperatures, and high values facilitated efficient heat dissipation. Soiling-induced thermal impacts contributed to a maximum 12% energy reduction, emphasizing the importance of mitigating these effects. Tracking systems, although susceptible to higher temperatures, demonstrated potential to reduce soiling impacts and improve overall module efficiency. These findings provide actionable insights for optimizing solar PV performance under diverse environmental and operational conditions.

Oxygen evolution reaction (OER) properties of nickel oxide electrodes are improved by the transition of its oxidation state due to lithium incorporation. The high solubility of Li into the NiO electrode lattice structure synergistically enhances the average oxidation state of Ni^3+/2+ ions, improving the reaction kinetics on active sites. The optimal incorporation level of Li into NiO is found to be 10 wt.%. The cobalt and lanthanum coating catalysts exhibited inherent properties without synergistic improvement. The electrochemical analysis results using a rotating disk electrode (RDE) system indicated the lowest OER overpotential for 10 wt.% Li-incorporated catalyst (480 mV), compared with Co-coated (534 mV) and bard NiO (696 mV) catalysts. The obtained results are expected to improve the reaction kinetics of oxygen evolution catalysis using nickel oxide-based catalysts, specifically for clean and sustainable hydrogen production via alkaline electrolysis.

In order to reduce the inconsistency of lithium battery packs and ensure the safety of battery charging and discharging, this paper presents an equalization topology structure with three working modes: direct cell to cell (DC2C), cell to pack (C2P), and pack to cell (P2C), which uses a flyback converter as the energy transmission element, and an adaptive three-threshold equalization strategy based on parameter analysis. To prevent overcharging and overdischarging, this equalization strategy defines the priority of each mode under different working states. By analyzing the real-time state of charge (SOC) parameters of the battery pack, the equalization circuit can adaptively select the current equalization mode to reduce the inconsistency of the current battery pack. Verified by simulation experiments, compared with the equalization circuit of the traditional flyback converter, the equalization circuit has increased the equalization speed by 26.96%, 38.25%, and 14.07%, respectively, under the three working states of standing, charging, and discharging, and the equalization efficiencies have reached 86.63%, 88.84%, and 90.91%, respectively.

Herein, amorphous vanadium oxide (a-VO_x) nanoparticle-impregnated three-dimensional (3D) microspheres comprising highly conductive and porous reduced graphene oxide (rGO) and nitrogen-doped carbon nanotubes (N-CNTs) framework (a-VO_x@rGO-N-CNTs) were designed as functional interlayers for lithium–sulfur batteries (LSBs). N-CNTs were successfully formed on the rGO sheet surfaces, uniformly distributed between rGO and mesopores, via the catalytic effect of metallic Co–Fe. The rGO and N-CNTs framework not only provided an additional pathway for electron transport but also improved structural durability of the electrode materials. Moreover, polar a-VO_x nanoparticles involved within the conduction pathway offered numerous chemisorption sites for anchoring polysulfides, thereby improving the utilization of active materials. The cell employing a-VO_x@rGO-N-CNTs-coated separator as a functional interlayer exhibited excellent rate capabilities (473 mA h g⁻¹ at 1.5 C) and cycling performance (800 cycles at 1.0 C and an average decay rate of 0.09% per cycle) at high C-rate. This outstanding performance was mainly ascribed to the synergistic effects of rGO, N-CNTs framework, and polar a-VO_x nanoparticles. The design strategy proposed in this study offers insights into the development of porous and conductive nanostructures for extensive energy storage applications including LSBs.

Photovoltaic (PV) panels play a pivotal role in advancing renewable energy adoption by offering a clean and sustainable alternative to fossil fuels. However, elevated operating temperatures diminish PV cell performance, reducing energy output and accelerating material wear. This research evaluates the cooling efficiency of a PV panel equipped with a three-dimensional oscillating heat pipe (3D-OHP) integrated with hybrid nanofluids consisting of graphene oxide–copper oxide (GO–CuO), carbon nanotube–CuO (CNT–CuO), and multiwalled CNT–CuO (MWCNT–CuO). The OHP is charged with two concentrations of each nanofluid, specifically 0.1 and 0.2 g/L, to evaluate their impact on the thermal management of the PV panel. The study involved experimental tests using two PV panels: one equipped with a 3D-OHP as the cooled panel and the other as a reference panel under identical environmental conditions. Hybrid nanofluids were prepared by dispersing nanoparticles in a base fluid, and their thermal properties were characterized prior to use. Energy and exergy analyses quantify the enhancements in thermal efficiency and the reduction in entropy generation. Experimental results reveal that CNT–CuO with a concentration of 0.2 g/L remarkably improves the electrical power output by 12.07%, outperforming other studied systems with the maximum exergy efficiency of 31.2%. The findings also highlight notable gains in first-law efficiency. Furthermore, the levelized cost of energy (LCOE) and levelized cost of storage (LCOS) are analyzed, demonstrating the economic feasibility of hybrid nanofluid-based cooling for PV systems.

Ternary hybrid nanofluids (THNFs) are modern fluids introduced to enhance the performance of conventional hybrid nanofluids (HNFs). Their unique properties make them suitable for diverse applications, ranging from heat exchangers to advanced industrial and medical treatments. Due to the practical applications and innovative features of THNFs, this paper aims to analyze the performance of these fluids to improve the efficiency of modern devices. The THNF is formulated by adding nanoparticles of three different kinds of aluminum oxide (Al₂O₃), silicon dioxide (SiO₂), and copper (Cu) into water (H₂O) based ethylene glycol (C₂H₆O₂). The momentum equation is formulated considering the influences of Darcy Forchheimer, permeability, and magnetic field. Thermal radiation, intermolecular friction force, and Joule heating effects are accounted in the thermal field equation. Mass concentration relation is acquired considering binary chemical reaction and activation energy (AE). Additionally, the influence of stratifications (thermal and solutal) at the boundary of the cylinder is considered. The physical phenomenon representing partial differential equations is reduced into ordinary ones utilizing the transformations and then solved via Runge–Kutta Fehlberg (RKF-45) numerical scheme in Mathematics. Influence of involved sundry variables on HNF and THNF velocity, thermal field, mass concentration, surface drag force (skin friction coefficient), mass, and heat transfer rates were examined. The results showed that the velocity fields of THNF and HNF decay through variables Darcy Forchheimer, porosity, and Hartman number. Thermal field of THNF and HNF improves via radiation parameter, Eckert number, and Hartman number. Local heat transfer rate upsurges versus curvature variable and Prandtl number.

The development of high-efficiency, low-pollution, zero-emission, flexible-fuel, and low-cost gas turbine (GT) generator sets is a crucial strategy to achieve carbon peaking and carbon neutrality goals. This research utilizes the reheat GT cycle (RGTC) as the top cycle and constructs four RGTC-supercritical/transcritical carbon dioxide (s/tCO₂) combined cycle systems with progressively enhanced configurations of the CO₂ bottom cycle. Modeling and thermodynamic analysis are conducted using next-generation GT parameters. When the supercritical CO₂ (sCO₂) dual recuperated bottom cycle (sCO₂DRBC) is used as the bottom cycle, the high temperature at the CO₂ compressor outlet results in gas exhaust temperatures exceeding 100°C, indicating insufficient waste heat utilization. Consequently, the operating condition of the CO₂ bottom cycle is changed from supercritical to transcritical, utilizing the low-temperature CO₂ fluid at the pump outlet to further absorb waste heat from the gas. This modification results in a final gas exhaust temperature of ~70°C and a 2.84% increase in combined cycle energy efficiency. Additionally, considering the substantial latent heat of vaporization in the water within the gas, its effective utilization can further enhance cycle energy efficiency. Therefore, the transcritical CO₂ (tCO₂)DRBC is further refined by incorporating a medium-temperature turbine (MT), medium-temperature recuperator (MTR), and phase change heater (PH) to form the modified tCO₂ bottom cycle I (tCO₂MBC-I). The RGTC-tCO₂MBC-I combined cycle achieves a 69.56% combined cycle energy efficiency with the absorption of some gas latent heat, demonstrating a significant improvement in energy efficiency. Further analysis reveals that the flow rate of the bottom CO₂ cycle is increased by the enhanced design due to the need to absorb additional gas latent heat, exacerbating losses at the bottom cycle precooler. Consequently, an expander (positioned before the precooler) is integrated into the tCO₂MBC-I to form the modified tCO₂ bottom cycle II (tCO₂MBC-II). Calculated results indicate an energy efficiency of 69.91% for the RGTC-tCO₂MBC-II combined cycle. This paper presents three modifications to the previously studied sCO₂DRBC, with the final RGTC-tCO₂MBC-II combined cycle demonstrating considerable energy efficiency advantages. The findings suggest its potential to become the next generation of GT combined cycle units.

Lithium-ion batteries (LIBs) degrade through repeated charge and discharge, causing increased internal resistance and reduced maximum capacity. This affects their discharge performance, such as maximum power output and runtime, which in turn affects the safety and reliability of the system using the LIB. Therefore, identifying and predicting the state of the LIB is essential to ensure the safety and reliability of the system. This paper proposes a system for diagnosing the health state of LIBs using time-series discharge data. The system for diagnosing the health state of LIBs is constructed by utilizing a residual-deep neural network (R-DNN). DNN with residual connections can have a deeper and wider structure than conventional neural networks, which enables abundant feature extraction. The time-series discharge data are processed to form the input and output data for the proposed diagnostic system, upon which training is conducted. The output of the trained diagnostic system is then used to determine the health state of the LIB. Furthermore, to validate the proposed method, diagnosis was performed on data not used for model training, and the results were analyzed. Additionally, a comparison group model was trained to perform a comparative analysis with the proposed method.

In the evolving energy landscape, there is an increasing demand for efficient and reliable heat transfer methods to prevent overheating in renewable energy systems. Pool boiling presents viable solutions, and the surface orientation of the heated surface is a key parameter which affects its performance. This research investigates the effect of surface orientation on critical heat flux (CHF) in pool boiling using silicon (Si) and silicon dioxide (SiO₂) surfaces. Experiments were conducted across seven preset orientation angles ranging from 0° to 180°. The experimental results indicated that these conditions had a notable effect on heat transfer performance, with the highest CHF observed at a 60° orientation for both types of surfaces. At 180°, a significant reduction in CHF was exhibited at the SiO₂ surface, with CHF values less than 5% of those at 0°. Si surfaces exhibited larger bubble departure angles and smaller bubble sizes at higher orientation angles compared to SiO₂ surfaces. These findings, in which CHF peaks at 60°, challenge the predictions of many existing models that predict a steady decrease of CHF as the surface orientation increases. This research involves a detailed analysis of vapor bubble dynamics, and the interactions between bubbles and the heating surface across different surface orientations. Through the examination of bubble detachment, coalescence, and liquid-vapor interactions, this study aims to provide a clearer understanding of the mechanisms driving CHF variations.