International Journal of Energy Research最新文献

With the growing presence of renewable energy sources (RESs), the necessity for adaptive and robust control strategies becomes more pronounced. This article proposes a self-adaptive bonobo optimizer (SABO)-based proportional integral one plus double derivative (PI-(1+DD)) controller that offers a novel solution to the load frequency control (LFC). It draws inspiration from the reproductive strategies of bonobos, employing unique mating behaviors to enhance optimization processes. This innovative approach introduces memory capabilities, repulsion-based learning, and diverse-mating strategies. It is developed to tune the PI-(1+DD) controller for handling the LFC in a two-area power system involving a thermal plant and RESs of a wind farm. The proposed SABO algorithm is applied in a comparative manner to the standard bonobo optimization algorithm (BOA), Coot algorithm, particle swarm optimizer (PSO), and Pelican optimization approach (POA). Also, the SABO-based PI-(1+DD) controller is contrasted to PI and PIDn controllers. The simulation findings distinguish the proposed SABO-based PI-(1+DD) controller as a versatile and adaptive controller offering a more resilient and efficient approach to tackle the complexities introduced by the evolving energy landscape. It demonstrates its potential to significantly improve the dynamic response of power systems, particularly in the face of step load changes and random fluctuations. The proposed SABO-based PI-(1+DD) controller shows significant enhancement compared to BOA, Coot, POA, and PSO with 38.81%, 46.27%, 16.79%, and 37.40%, respectively. Also, it demonstrates an impressive percentage improvement of 97.1% compared to the PIDn controller and 74.88% over the PI controller considering random consecutive fluctuations in the system.

The paper proposes a bipolar coil arrangement method (BCAM) to identify a new anti-misalignment positioning of overlapping (OV) coils in a bipolar pad (BP) for achieving high-power transmission in a wireless electric vehicle (EV) charging system. Six different magnetic couplers with identical geometric dimensions, such as circular pad (CP), rectangular pad (RP), double-D pad (DDP), DD quadrature pad (DDQP), BP, and four-coil pad, are compared to identify a better performance charging pad. The performance evaluation for all charging pads is done by considering a vertical airgap (ΔZ) of 60–100 mm between the transmitter and receiver with and without ferrite (Fe) core and aluminum (Al) shield using ANSYS Maxwell software. In addition, the lateral misalignment (LTM) distance (ΔY) of 40–60 mm is also examined in all charging pads. The measurable quantities, such as coupling coefficient (k), the magnetic field strength (B), and mutual inductance (M), are evaluated for the above-mentioned charging pads with different misalignment conditions. The proposed coil arrangement in the BP provides better mutual inductance by facilitating omnidirectional flux distribution with ΔY of −60 to 60 mm. It also achieved the maximum DC–DC efficiency of 94.5% at ΔZ of 100 mm between charging pads by incorporating the inductor–capacitor–capacitor-series (LCC-S) compensation circuit for a 4.75 kW inductive power transfer (IPT) charging system. Finally, a small-scale laboratory-based prototype is designed for all charging pads to verify the feasibility of the proposed method. Both simulation and experimental validation ensure the improvement of DC–DC efficiency irrespective of LTMs of the proposed inward OV BP coil position.

This paper presents a sophisticated deep-learning framework designed for predicting rate of penetration (ROP) by assimilating well-log data, litho-facies classifications, and parameters of onshore production wells drilling operations in Central Asia. The evolution in bit technology and relevant drilling operation underscores the necessity for enhancing the traditional empirically derived predictions. Distinctively, our approach integrates transfer learning into a conventional deep-neural-network, employing two important techniques. One is data quality control by Kalman filter to make machine learning applicable to in situ data which have significant noises. The other is K-means clustering to reflect litho-facies attributes as input features of deep-learning predictive model. The developed scheme was applied to the in situ drilling data which have 12 kinds of data types: measured depth; two drilling operation variables, namely weight on bit (WOB) and rotary speed (RPM [revolutions per minute]); six well-log measurements including density (RHOZ), neutron porosity (TNPH), resistivity (RT), sonic (DT), gamma ray (GR), and photoelectric factor (PEFZ); alongside three clusters delineating litho-facies data. The developed schemes are tested by being applied to the in situ well’s ROP prediction based on the training and validation of four wells’ data. All in-situ data are in the interval of 7-in. casing which ranges from about 800 to 3100 m. By adding the well-log-data-driven litho-facies and the transfer learning on the base model, ROP prediction performances are improved as follows: R² value up to 49% (from 0.49 to 0.73), mean absolute error up to 23% (from 6.79 to 8.82 m/h), and the dynamic time warping up to 24% (from 361 to 473 h), respectively. As a result of deriving a drilling operation strategy that allocates WOB from 1 to 6 tons for each 100 m section and optimizes ROP, it is expected to reduce drilling time by about 16.5% compared to actual drilling. The developed method can evaluate ROP with high reliability from the comparison between ROPs predicted and measured in actual drilling operation. It is expected that the developed scheme can be applied for an extension to real-time ROP optimization, a kind of inverse modeling, to find the optimum parameter conditions for ROP maximization, as a forward model.

Dye-sensitized solar cells (DSSCs) have been attracted as a real class of building-integrated photovoltaic (BIPV) owing to its natural controllable color transparency, working ability in diffuse light, and low-cost fabrication. The low photoconversion efficiency (PCE) is the main obstacle for BIPV market. The bilayered structure based on mesoporous TiO₂ nanoparticles (NPs) along with TiO₂ blocking layer was introduced to obtain high PCE by optimizing the dye adsorption, avoid recombination via direct electrolyte contact, and enhance light-harvesting ability by providing scattering centers. However, the bilayered structure based on mesoporous TiO₂ network offers inferior charge transfer, thus higher recombination and, consequently, low PCE. In our previous studies, we have developed graphene/TiO₂ blocking layer, graphene/TiO₂ transparent layer, and scattering layer and analyzed individually to improve the electron transport and reduce recombination. In the current work, we have demonstrated the integrated optimized photoelectrode-based DSSCs via the above-mentioned previously developed photoelectrode components with Pt and graphene/polyaniline (PANI) cost-effective counter electrode. Optical property analysis and electrochemical impedance spectroscopy (EIS) have shown that graphene-modified optimum components of photoelectrode have effectively improved the electron transport and light-harvesting ability. Electron lifetime, diffusion coefficient, and diffusion length have been increased by ~87%, ~20%, and ~11%, respectively, as compared to control DSSC based on commercial paste. Consequently, 5.94% of PCE was achieved, which is 20% higher than the DSSCs fabricated with commercial pastes. Moreover, DSSCs based on optimized photoelectrode with graphene/PANI counter electrode have shown 4.04% PCE, which is ~70% of the PCE that was achieved with Pt.

The Ordovician carbonates in the Tarim Basin’s central uplift zone are crucial for oil and gas exploration. A comprehensive analysis has been conducted utilizing 3D seismic imaging, logging, core samples, and production tests to explore its potential. The results indicate that dissolution within the Ordovician reservoir, attributed to an intricate fault system involving karstification and faulting, enhances hydrocarbon storage. Both structural and dissolution-induced fractures are vital for efficient hydrocarbon flow. NE-SW strike-slip faults significantly impact the distribution of carbonate reservoirs, particularly in early karst strata with higher porosity. Seismic sections reveal three distinct reflection patterns: full, single peak, and chaotic. The forward seismic model shows that oil/gas saturation influences seismic energy. High-yielding wells are located in formations with full-waveform patterns and high energy, indicating high porosity. A strong correlation exists between Class I seismic phases and high-porosity zones. Daily production in these zones ranges from 30 to 70 tons, validating the evaluation methods. The Yijianfang formation excels in oil storage due to its karst features and numerous unfilled tectonic and dissolution fractures, offering ample storage and permeability. High production is seen in strata with porosity >4.5%, but fractures alone do not ensure high production. Structural activity, occurring mainly during the meso-deep burial stage, allows acidic solutions to permeate structural fractures, leading to further dissolution. Karstification is confined to depths within 80 m of the Yijianfang formation top, indicating significant exploration potential.

The increasing integration of renewable energy sources into power grids necessitates efficient energy storage systems to balance supply and demand. Vanadium redox flow batteries (VRFBs) are becoming increasingly popular because of their long lifespan and flexible energy storage capabilities. Central to the effectiveness of VRFBs is the accurate estimation of future state of charge (SOC) levels. However, conventional SOC forecast frameworks suffer from poor generalization capabilities, which restrict their applicability in real-life energy systems. This research introduces a sequential forecast framework that combines multihead self-attention (MHA) with chained transfer learning (CTL) to estimate SOC sequences across multiple temporal horizons. The model performance is evaluated by forecasting SOC levels of the VRFB system operated under various charging and discharging current profiles. The results demonstrate that the change in the VRFB system’s operational dynamics significantly reduces the forecast accuracy of conventional frameworks, with the maximum MAE reaching 66%. Compared to the best-performing baseline trained on a linear current profile, the CTL-MHA-gated recurrent unit (GRU) decreased the maximum MAE from 28.7% to below 1.5%. The generalization capability of the proposed framework addresses a critical barrier to the integration of SOC forecast frameworks with smart energy storage systems.

Solar energy is a ubiquitous renewable resource for photovoltaic (PV) power generation; however, higher operating temperatures significantly reduce the efficiency of PV modules, impacting their electrical output and increasing the levelized cost of energy (LCOE). This study aims to enhance conventional PV systems’ electrical efficiency and annual energy recovery while reducing the LCOE through thermal management using microchannel heat sinks (MCHSs) under forced convection. A 600 W monocrystalline PV module was analyzed, recognizing an efficiency reduction of ~20% under actual operating conditions due to thermal effects, with the surface temperature reaching up to 63.76°C without cooling. In addition, analytical calculations were used to determine an incident solar irradiance of 957.33 W/m² for an industrial location in Lahore, Pakistan. Similarly, computational fluid dynamics (CFDs) simulations were conducted using single and dual-layer MCHSs configurations with water as the coolant at inlet velocities ranging from 0.01 to 1.0 m/s. The dual-layer MCHSs significantly reduced the PV module’s surface temperature from 63.76 to ~25.65°C at an inlet velocity of 1.0 m/s, achieving a temperature reduction of 38.11°C. This thermal management increased the electrical efficiency from 18.33% (without cooling) to 22.27%, an efficiency gain of ~4%. The annual energy recovery improved substantially; at 1.0 m/s, the dual-layer configuration increased the annual energy output by 227,954 kWh/year (about 21.89%) compared to the no-cooling scenario, reaching 1,269,131 kWh/year. Furthermore, the LCOE was reduced to as low as 6.27 PKR/kWh over a 30-year operational lifespan at lower velocities, demonstrating improved cost-effectiveness. Meanwhile, optimal velocity was identified between 0.2 and 0.5 m/s, balancing thermal performance and economic viability. Finally, this study concludes that thermal management using dual-layer MCHSs effectively enhances PV module efficiency, increases annual energy recovery, and reduces LCOE, contributing to sustainable and economical solar energy integration in industrial applications.

We present a series of organic–inorganic composite membranes containing graphene oxide (GO) and quaternized poly(fluorene-terphenyl piperidinium) (QPFTP) polymer to enhance ion conductivity and physicochemical properties. Utilizing the hydrophilic functional groups and robust support of GO, the composite membrane accomplishes improved ion exchange capacity (IEC), swelling ratio, water uptake, and electrochemical performance. The interaction between polymer chains and GO, facilitated by the interface between quaternized ammonium groups on the polymer and oxygen functional groups on the filler support, promotes hydrogen bond formation. Based on our experiments and results, it was proven that the introduction of GO improves the alkaline stability of the membrane, and the optimal GO content was confirmed to be 0.7 wt%. Consequently, the ion conductivity of QPFTP-GO-0.7 reaches 198.2 mS cm⁻¹, demonstrating superior performance compared to the pristine membrane (126.5 mS cm⁻¹). Furthermore, the single cell performance of QPFTP-GO-0.7 achieves a power density of 347.6 mW cm⁻² in an H₂/O₂ environment at 60°C. The findings from this research are expected to contribute to the advancement of anion exchange membrane (AEM) technology, offering insights into the design and development of next-generation membranes for sustainable energy applications.

In the trajectory of carbon dioxide (CO₂) neutrality, wind and solar energies will be the key for the energy transition in the electricity sector; however, a massive integration of solar and wind farms into the electricity grid by 2050 will be carried out. For this end, powers control and powers management of these two renewable energies have taken the attention of several researchers since the last decades. This article presents a reactive energy management strategy for a power grid linked to a wind farm utilizing doubly fed induction generators (DFIG) and enhanced by a static reactive power compensator (STATCOM). This management strategy improves an electrical grid capability in the event of a low-voltage ride through (LVRT) and aims to optimize the sizing of the STATCOM to be installed alongside the wind farm. The proposed oriented voltage control strategy (VOC) for the grid-side converter and STATCOM facilitates effective reactive current injection into the grid during symmetrical faults with significant voltage sags. A maximum power point tracking (MPPT) approach combined with stator flux-oriented control (FOC) applied to the rotor side converter enables effective control of the DFIG during an asymmetrical fault. Breaking down currents and voltages into positive and negative sequences expressed in the synchronous frame enhances DFIG and STATCOM control during grid voltage asymmetrical faults. The control algorithms are validated by simulation results using MATLAB-SIMULINK.

In this study, two distinct approaches were proposed for recovering heat from the exhaust gases of electric arc furnaces (EAFs)—the steam cycle and the organic Rankine cycle (ORC). These methods were evaluated based on various criteria, including energy and exergy efficiency, economic feasibility, and environmental impacts. To identify optimal performance parameters, the effects of different working fluids in the ORC were examined, revealing significant variations in cycle behavior depending on the fluid used. Consequently, the most effective operational conditions for each specific fluid were identified and recorded based on temperature and pressure fluctuations. The analysis led to the selection of acetone as the optimal working fluid due to its favorable performance despite its high flammability, characterized by its isentropic nature. The energy and exergy efficiencies of the cycle using this fluid reached 21% and 61%, respectively, with a power output of 597.4 kW under maximum conditions. Additionally, the study demonstrated that, given the high temperature of the heat source, the steam cycle is more justifiable than the combined steam and ORC with the proposed configuration. The exergy efficiency of the steam cycle reached a maximum of 57%, with a net power output of 2897 kW and a total cost rate of $0.041/s under these conditions. Finally, by optimizing the steam cycle using a genetic algorithm, the ideal values for exergy efficiency were slightly reduced to 53%, with a significant decrease in the total cost rate to $0.036/s.