Maximum Energy Research Articles

US federal resource allocations are inconsistent with concentrations of energy poverty.

Recent data from the US Energy Information Administration reveals that nearly one in three households in the United States report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We implement a LASSO-based machine learning approach using sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. We then compare the allocation to states from the Low Income Home Energy Assistance Program to an optimized allocation. We allocate funds to the most burdened households, providing them with enough assistance to reduce their energy expenditures so that their household energy burden is equal to a new maximum allowable energy burden. This markedly shifts funds from the northern cold-weather states to the southern warm-weather states.

An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D

This paper addresses the green permutation flow shop scheduling problem (GPFSP) with energy consumption consideration, aiming to minimize the maximum completion time and total energy consumption as optimization objectives, and proposes a new method that integrates end-to-end deep reinforcement learning (DRL) with the multi-objective evolutionary algorithm based on decomposition (MOEA/D), termed GDRL-MOEA/D. To improve the quality of solutions, the study first employs DRL to model the PFSP as a sequence-to-sequence model (DRL-PFSP) to obtain relatively better solutions. Subsequently, the solutions generated by the DRL-PFSP model are used as the initial population for the MOEA/D, and the proposed job postponement energy-saving strategy is incorporated to enhance the solution effectiveness of the MOEA/D. Finally, by comparing the GDRL-MOEA/D with the MOEA/D, NSGA-II, the marine predators algorithm (MPA), the sparrow search algorithm (SSA), the artificial hummingbird algorithm (AHA), and the seagull optimization algorithm (SOA) through experimental tests, the results demonstrate that the GDRL-MOEA/D has a significant advantage in terms of solution quality.

Enhanced energy storage efficiency of an innovative three-dimensional nickel cobalt metal organic framework nanocubes with molybdenum disulphide electrode material as a battery-like supercapacitor

Recently, metal-organic frameworks (MOFs) with transition metal sulfides have gained much attention as electrode materials for supercapacitors (SCs). In this work, 3D-NCMOF@MS NCs, incorporating two-dimensional molybdenum disulphide (2D-MS) nanosheets and three-dimensional nickel cobalt-MOF nanocubes (3D-NCMOF NCs) are synthesized by a solvothermal and precipitation process. Owing to their enhanced electrical conductivity and huge surface area, the 3D-NCMOF@MS NCs produce superior electrochemical responses in SCs compared to 2D-MS and 3D-NCMOF NCs. The results demonstrate that the synergistic 3D-NCMOF@MS NCs increased specific capacitance up to 1048 Fg−1 at 1 Ag−1, revealing outstanding cycle stability with a capacitance retention of 99.06 %. Besides, the exposed high-power density of 3D-NCMOF@MS NCs reached 400 W kg−1 as well as an outstanding maximum energy density of 188.64 Wh kg−1. Such synergistic effects of the multivalent states of Ni, Co, and Mo promote electrochemical characteristics, implying that the 3D-NCMOF@MS NCs could potentially be used in energy storage systems, boosting electrochemical performance.

Optimal scheduling of battery energy storage system operations under load uncertainty

This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.

Energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization

Aiming at the problems of large number of data transmission node deaths and large transmission energy consumption output in energy-saving clustering routing communication of wireless sensor networks, an energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization is proposed. Firstly, a network communication energy consumption model is constructed, and the network node distribution is optimized in time with the goal of reducing energy consumption combined with differential bee colony algorithm; then, based on the network node distribution optimization results, an energy-saving clustering method for heterogeneous wireless sensor networks is formulated, and the cluster head is determined by using the energy iteration cluster selection method, and the cluster head radius is obtained to complete the energy-saving clustering of communication nodes in heterogeneous wireless sensor networks; finally, the distance between the communication cluster head node and the base station is set, the routing level of the node communication is determined, and the energy-saving routing communication of the heterogeneous wireless sensor network is realized by combining the multi-hop routing communication mode. The experimental results show that when the method is used for network energy-saving clustering routing communication, the number of data transmission nodes that die is at most 22, and the maximum energy consumption of node transmission is 21 nJ/bit , indicating that the node communication energy-saving effect of this method is good.

Ag-decorated ZnCo2S4/SiO2 nanocomposite as a high-performance supercapacitor electrode

Ag-decorated ZnCo2S4/SiO2 nanocomposite was utilized in this study as a potential supercapacitor electrode. Ag nanoparticles demonstrate a low level of pseudo-capacitance and their capacity to improve charge transfer. The specific capacitances of the ZnCo2S4, ZnCo2S4/SiO2, and Ag@ZnCo2S4/SiO2 electrodes were 935, 1066, and 1623 Fg−1, respectively, at 1 Ag−1 current density. The Ag@ZnCo2S4/SiO2 electrode exhibits remarkable cycle stability (100 % coulombic efficiency and capacitance retention of approximately 91.7 % at 10 A g−1 after 10,000 cycles). Furthermore, the symmetric supercapacitor (Ag@ZnCo2S4/SiO2//Ag@ZnCo2S4/SiO2) possesses a broad potential window of 1.5 V, a maximal energy density of 68.6 Whkg−1, and a specific power of 23.88 kWkg−1. Furthermore, the hybrid supercapacitor device (Ag@ZnCo2S4/SiO2//Ag@ZnCo2S4/SiO2) demonstrated a coulombic efficiency of 100 % and a capacitance retention of 92.3 %. Consequently, the Ag@ZnCo2S4/SiO2 composite electrode was suitable for high-energy supercapacitors due to their high capacitance, remarkable cycle stability, and high energy density.

Energy Maximisation and Power Management for a Wave-to-Wire Model of a Vibro-Impact Wave Energy Converter Array

This paper develops a wave-to-wire model of a vibro-impact wave energy converter array for stand-alone offshore applications. Nonlinear model predictive control is proposed for maximising the wave power capture of the array, and implemented by AC/DC converters and the space vector pulse width modulation technique. A hybrid energy storage system, consisting of batteries and supercapacitors, is placed parallel to the DC bus via buck-boost DC/DC converters to smooth the array power output, and a Lyapunov-based power management strategy is utilised to control the DC/DC converters for stabilising the DC bus voltage. Intensive numerical simulations are conducted; the results show that the proposed wave-to-wire model is capable to evaluate the performance of the vibro-impact wave energy converter array in various scenarios, and the proposed energy maximisation control and power management strategy can enhance wave power capture and stabilise the power output simultaneously.

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Investigation on the microstructure and compressive behavior of open-cell copper- Al2O3 composite foams

The objective of this study is to evaluate the mechanical behavior and energy absorption characteristics of open-cell copper- Al2O3 composite foams fabricated using polyurethane foam with an average pore size of 1.2 mm and a combination of electroless and electrodeposition methods. During electroplating process, the composite foams were fabricated with varying amounts of Al2O3 particles (0.1, 0.6, 0.8, and 1 g/l) in electrolyte solution. To assess mechanical properties of the fabricated foams, uniaxial compression test was conducted to determine the maximum compressive strength, energy absorption density, efficiency, and specific energy absorption. The content of Al2O3 in the fabricated composite foams was evaluated using energy dispersive X-ray (EDX) analysis, and the microstructure was examined using scanning electron microscopy (SEM). The results indicate that the incorporation of Al2O3 particles significantly improves the mechanical properties of the copper-Al2O3 composite foam. Specifically, in the composite foam with 9.34 vol% of Al2O3 particles, the maximum compressive strength, total energy absorption, specific energy absorption, and energy absorption efficiency reach 1.03 MPa, 6.48 MJ m−3, 1.9 J g−1, and 75%, respectively. This amount of alumina particles enhances the maximum compressive strength and total energy absorption of open-cell copper foam by approximately 30% and 100%, respectively. However, the mentioned parameters slightly decreased with an increasing amount of Al2O3 particles up to 16.32 vol%. These significant improvements in the mechanical properties of these foams make them well-suited for high-strength applications.

Subsurface hardening of Al irradiated with ultrafast infrared laser

The effect of femtosecond laser shock peening on a model Al-0.3Mn alloy was investigated experimentally and numerically by molecular dynamics. Micro-diffraction experiments performed at synchrotron source revealed the depth profiles of the residual stress and the stored energy of dislocations, a measure of local plasticity. The depth of the maximum compressive stress did not coincide with that of the maximum dislocation energy, which was found at the surface. The interaction between the laser and the metal was simulated with LAMMPS using a two-temperature molecular dynamics package. The model accurately described the equation of state of aluminum and showed nearly equal resolved shear stresses on all slip systems at the wavefront. The dislocation density at a depth of 1 μm, predicted by the Meyers' model [1], was higher than the experimental data, suggesting possible recovery due to the increased temperature of the sample after repeated shock loading.

Passively mode-locking fiber laser based on Cr2Si2Te6 saturable absorber

This paper reports on the generation of third-order harmonic mode-locking, double-pulse phenomena, and conventional solitons in an erbium-doped fiber laser using Cr2Si2Te6 as a saturable absorber (SA). The double-balance probing method was employed to examine the nonlinear characteristics of Cr2Si2Te6-SA. Its modulation depth is 2.35 %, and its saturation intensity is 29.74 MW/cm2. We discovered conventional soliton functioning with a center wavelength of 1561.8 nm at a pump power of 34.76 mW. It has a repetition frequency of 6.76 MHz and a signal-to-noise ratio of 45 dB. As the pump power increased, the traditional soliton was able to maintain its existence in the range of 34.76 mW to 80.96 mW. The maximum output power reaches 1.1 mW when the pump power is 80.96 mW, and the maximum single-pulse energy of the conventional soliton is 0.16 nJ. Furthermore, we observed third-order harmonic mode-locking and double-pulse phenomena at pump outputs of 53.65 mW and 71.16 mW. The experimental findings demonstrate the excellent nonlinear effect of the SA based on Cr2Si2Te6. In ultrashort-pulse fiber lasers, Cr2Si2Te6 nanosheets can be employed as an efficient saturable absorption material.

Tunning Pr to achieve ultra-high magnetic properties of anisotropic nanocrystalline (Sm,Pr)Co5 magnets

In this study, we reported on the bulk anisotropic nanocrystalline Sm1-xPrxCo5 (x = 0.2–0.6) magnets aimed at achieving excellent energy products. Through a combination of advanced processing techniques, including melt spinning, hot pressing, and hot deformation process, bulk magnets with a fine nanocrystalline structure were successfully synthesized. The results revealed that the substitution of Pr strengthened the c-axis texture and the saturation magnetization, facilitating the enhancement of remanence (Mr) and maximum magnetic energy product [(BH)max]. However, Pr substitution decreased the coercivity (Hci) due to the reduced magnetocrystalline anisotropy field. Excellent magnetic properties are obtained for Sm0.4Pr0.6Co5 magnets with the (BH)max of 23.2 MGOe and Hci of 11.8 kOe. The prepared (Sm,Pr)Co5 magnets with different Pr contents have essentially the same microstructure with RECo5 main phase grains of about 400 nm in diameter and fine dispersed rare earth-rich nanoprecipitates. Our findings can provide reliable reference for manufacturing nanocrystalline permanent magnet materials and promote the development of rare earth permanent magnet new materials.

Designing nano-heterostructured nickel doped tin sulfide/tin oxide as binder free electrode material for supercapattery

New generation of electrochemical energy storage devices (EESD) such as supercapattery is being intensively studied as it merges the ideal energy density of batteries and optimal power density of supercapacitors in a single device. A multitude of parameters such as the method of electrodes preparation can affect the performance of supercapattery. In this research, nickel doped tin sulfide /tin oxide (SnS@Ni/SnO2) heterostructures were grown directly on the Ni foam and subjected to different calcination temperatures to study their effect on formation, properties, and electrochemical performance through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and electrochemical tests. The optimized SnS@Ni/SnO2 electrode achieved a maximum specific capacity of 319 C g− 1 while activated carbon based capacitive electrode exhibited maximum specific capacitance of 381.19 Fg− 1. Besides, capacitive electrodes for the supercapattery were optimized by incorporating different conductive materials such as acetylene black (AB), carbon nanotubes (CNT) and graphene (GR). Assembling these optimized electrodes with the aid of charge balancing equation, the assembled supercapattery was able to achieve outstanding maximum energy density and power density of 36.04 Wh kg− 1 and 12.48 kW kg− 1 with capacity retention of 91% over 4,000 charge/discharge cycles.

Co1-xS/N, S-codoped carbon loaded porous carbon cloth as flexible electrode for high performance supercapacitors

The capacitive performances of flexible electrodes highly depend on their architectures and redox activity. In this work, Co1-xS/N, S-codoped carbon (NSC) loaded porous carbon cloth (p-CC) was prepared through an etching procedure and a followed repeated-sulfurization process. Since the porous CC could accommodate more Co1-xS nanoparticles and the phase structure of Co1-xS was well controlled by the repeated-sulfurization process, the obtained p-CC/Co1-xS/NSC electrode with optimized composition and architecture exhibits high areal capacitances of 1263.8 mF cm−2 at 1 mA cm−2 and 858.6 mF cm−2 at 50 mA cm−2. The deposited NSC on Co1-xS could effectively prevent their slipping and agglomeration, and thus make the electrode exhibit high capacitance retention of 99.7 % after 20,000 charge–discharge tests. The all-solid-state symmetric supercapacitor assembled by the formed electrodes and ionic liquid/gel electrolyte shows a maximum energy density of 1.34 mWh cm−3 at the power density of 9.55 mW cm−3. These results present a new strategy for the construction of cobalt-sulfide/carbon based flexible electrode with high capacitive performances.

An Adaptive Chirp Mode Decomposition-Based Method for Modal Identification of Time-Varying Structures

Modal parameters are inherent characteristics of civil structures. Due to the effect of environmental factors and ambient loads, the physical and modal characteristics of a structure tend to change over time. Therefore, the effective identification of time-varying modal parameters has become an essential topic. In this study, an instantaneous modal identification method based on an adaptive chirp mode decomposition (ACMD) technique was proposed. The ACMD technique is highly adaptable and can accurately estimate the instantaneous frequencies of a structure. However, it is important to highlight that an initial frequency value must be selected beforehand in ACMD. If the initial frequency is set incorrectly, the resulting instantaneous frequencies may lack accuracy. To address the aforementioned problem, the Welch power spectrum was initially developed to extract a high-resolution time–frequency distribution from the measured signals. Subsequently, the time–frequency ridge was identified based on the maximum energy position in the time–frequency distribution plot, with the frequencies associated with the time–frequency ridge serving as the initial frequencies. Based on the initial frequencies, the measured signals with multiple degrees of freedom could be decomposed into individual time-varying components with a single degree of freedom. Following that, the instantaneous frequencies of each time-varying component could be calculated directly. Subsequently, a sliding window principal component analysis (PCA) method was introduced to derive instantaneous mode shapes. Finally, vibration data collected under various operational scenarios were used to validate the proposed method. The results demonstrated the effective identification of time-varying modal parameters in real-world civil structures, without missing modes.

Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy

Conventional pulse-and-glide (PnG) strategies expose issues including poor coordination with energy management systems (EMSs), discomfort and low levels of automation, this paper innovatively addresses these challenges by the comprehensive considerations of energy economy, dynamic performance, and ride comfort, proposing an intelligent pulse-and-glide (I-PnG) strategy tailored for plug-in hybrid electric vehicle (PHEV) configurations. The I-PnG strategy is aimed at optimizing economic cruising in high-speed scenarios; however, it demonstrates applicability across diverse conditions without deterioration in performance. I-PnG is a data-driven autonomous driving solution developed through a deep Q network (DQN), featuring two distinct modes: ECO and SPORT, the former prioritizes energy economy, while the latter emphasizes dynamic performance enhancement. The results indicate that our proposed strategy significantly optimizes energy management compared to the baseline methods. Specifically, during variable-speed cruising, I-PnG ECO achieves a maximum energy efficiency improvement of 16.13 % and a cost reduction of 7.74 %, while I-PnG SPORT exhibits a maximum energy efficiency improvement of 13.37 % and a cost reduction of 3.92 %. Under the Highway Fuel Economy Test (HWFET), I-PnG ECO and I-PnG SPORT further improve energy efficiency by up to 22.94 % and 19.04 %, respectively, with corresponding cost savings of 18.47 % and 13.35 %.

Unprecedented Hole Concentration of NiSe2 Electrodes Leading to Hole Degeneration of Entire WSe2 Channels.

Semimetal electrodes for 2D semiconductors have been extensively studied; however, research on p-type semimetals has been limited due to the scarcity of materials that satisfy both high work functions and low resistances. In response, we investigated the behavior of NiSe2 as an electrode. Utilizing a novel co-evaporation method that suppresses oxidation and contamination, we synthesized NiSe2 demonstrating a high work function of 5.53 eV, a low resistance of 5.81 × 10-5 Ω cm, and a record hole concentration exceeding 1023 cm-3. We confirmed that when WSe2 comes into contact with NiSe2, the Fermi level of WSe2 falls below the work function of NiSe2, leading to complete hole degeneration of the channel. This unusual Fermi level alignment correlates with the high hole concentration in NiSe2 and the maximum energy level of its hole pockets. Our research provides new insights into how an electrode's hole concentration affects channel behavior.

Stoichiometry modulated tunable optoelectronic properties of Nb-doped WS2 synthesized by chemical vapor deposition method.

Significant progress has been made in the field of two-dimensional WS2, yet the precise control of the doping process to achieve desired properties and the interpretation of complex physical phenomena in these novel materials necessitate thorough research. In this study, Nb-doped WS2 is synthesized using chemical vapor deposition technology, leading to triangular flakes with a side length of 12-20μm. The X-ray photoelectron spectroscopy analysis indicates that the NbW substitution defect functions as an electron acceptor. The interlayer and transverse forces of the Nb-doped WS2 flake approach 1 nN and 150 pN, respectively. As the doping concentration increases, the valence band maximum energy of Nb-doped WS2 ranges from 4.06eV to 4.3eV. Furthermore, the performance of the photodetector is discussed.

One‐Step Electrodeposition of Iron Oxyhydroxide onto 3D Porous Graphene Substrates for On Chip Asymmetric Micro‐Supercapacitors

Electrochemical capacitors based on redox active materials can achieve greater capacitance values than traditional electric double layer composites. Herein, electrodeposition of iron oxyhydroxide from a mildly acidic acetate precursor is reported. The one‐step deposition resulted in a submicron film composed of FeOOH phase, which was confirmed via Raman and x‐ray photoelectron spectroscopy. The capacitance increased linearly with loading amount and achieved a maximum at 1600 mC deposition with 120 mF cm‐2 at 25 mV s‐1 after which the film became more resistive, limiting electrolyte access to the porous graphene substrate. The deposited FeOOH demonstrated promising rate capability and good cycling stability, without phase changes, retaining 82% of the initial capacitance after 5000 consecutive charge/discharge cycles. The charge storage mechanism of FeOOH was determined via in situ Raman spectroscopy, which followed reversible iron oxygen vibration changes upon cycling which become more intense upon reduction as a result of sodium ion intercalation. Furthermore, an asymmetric configuration full cell combining FeOOH/MnO2 allowed the working voltage to be extended to 2 V, maintaining an ideal capacitor behaviour, and achieving a maximum energy and power density of 21 microWh cm‐2 and 2.5 mW cm‐2 respectively.

Interpretation of possible biogas production capacity by investigating the effects of anaerobic digester tank geometry and angular velocity on flow characteristics.

Mixing performance in reactors producing biogas through anaerobic digestion is one of the parameters that directly affect biogas yield. The most commonly used mixing model for bioreactors in biogas-production processes is mechanical mixing. In the present study, we focus on the geometry of the tank, where the mechanical mixing actually takes place. In this context, by using the six-blade standard Rushton impeller in two different types of tank, flow patterns involving velocity, dead zone volume, turbulent kinetic energy, and turbulent eddy dissipation rate in the angular velocity range of 25-100rpm were observed, and the possible effects of the results on biogas production were interpreted. A new impeller design was proposed that maximizes the interface between the fluid inside the reactor tank and the impeller, which has the potential to reduce the dead zone volume to significantly lower levels. Our results showed that the lowest dead zone volume was achieved for a 60° slope reactor tank compared to the conventional 90° slope reactor tank at an angular velocity of 100rpm. The dead zone volume decreased to 0.000094 m3 at 100rpm in the 60° slope reactor tank with a total volume of 0.0305 m3, which by comparison was 0.000374 m3 in the 90° slope reactor tank. The magnitudes of both maximum turbulent kinetic energy and maximum turbulent eddy dissipation were higher in the 60° slope reactor tank at all angular velocities examined, which would be expected to enhance mixing performance. It is hoped that the reader will benefit from the results of this study; however, further studies should be conducted on the use of actual biowaste as the working fluid instead of water.