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Abstract: In this paper, we present a facile coating technique to fabricate the light diffusing film with hemispherical surface convex micro-structure. The coating was prepared by different ratio of light-diffusing particles (LDP)/polyacrylates composites via in situ radical polymerization, with the H2SO4 and vinyl triethoxysilane (A-151) pretreatment made the LDP better dispersed and incorporated with polyacrylate polymer chains. When the mass ratio (LDP/polyacrylate) was 0.5, the film obtained the highest light-diffusing effect and more than 90% transmittance due to the formation of hemispherical surface convex micro-structure. The light diffusing films have excellent anti-glare property if applied to LED light system. [Copyright &y& Elsevier]

Potential induced degradation (PID) leads to power degradation, and reduces durability and reliability of solar modules. However, this problem has not been thoroughly solved so far. The results from interlaboratory and field study show contradictory fault phenomenon for PID. In this paper, PID of crystalline silicon photovoltaic power plants distributed in various climate conditions was investigated. These photovoltaic power plants consist of two types of crystalline silicon solar modules, which cover almost all kinds of front glass, ethyl vinyl acetate (EVA) and backsheet available commercially. It was found that only a few of power plants were affected by PID. By measuring current voltage characteristics of PID-affected solar modules, the real faults phenomenon was uncovered and classified into regular and irregular power degradation in a module string. The results obtained in this work show that the negative potential caused by high system voltage and stacking faults are necessary and sufficient conditions for PID occurrence for the first time. The anomalous power degradation is related to the stacking fault, which appears randomly during the crystal growth. [ABSTRACT FROM AUTHOR]

The model parameter identification based on real operation data is a means to accurately determine the simulation parameters of the microgrid, but the real operation data cannot guarantee the exact agreement with the required data for parameter identification, which has become an important restriction factor in the accurate simulation and analysis of the dynamics of the microgrid. This paper provides a method of modeling of microgrid based on data testing and parameter identification. In this paper, the method of parameter trajectory sensitivity is first introduced. Then, the data testing scheme for parameter identification is presented, and the parameter identification flow chart is given. Thirdly, a microgrid demonstration system in China is taken as an example, the important parameters of the distributed photovoltaic, direct-drive wind turbine and energy storage unit in the system are obtained by data testing and parameter identification, and in the end, the accuracy of the model is verified through the comparison of the simulation data and the test data of the microgrid during grid-connection/island switching process. The obtained microgrid model provides a base model for the analysis of the overall characteristics, such as the transient stability, as well as power quality of the microgrid. [ABSTRACT FROM AUTHOR]

In this study, the notch effect of different types of 304 stainless steel rod notch is studied because of the problem of difficulty in measuring the notch root strain of 304 rod stainless steel, and the parameters of the annular notch root are analyzed. The notch angle, notch depth, and notch root radius are the main parameters of the stress field affecting the annular notch, and the mathematical expression of the strain rate concentration factor is derived. In order to verify the accuracy of the theory, the mechanical model of 304 stainless steel bar is established by finite element and numerical simulation calculation. The results show that the theoretical and finite elements have a high degree of agreement. When the notch angle increases, both theoretical and finite elements show a downward trend. When the notch depth of 304 stainless steel bar increases, both theoretical and numerical simulations show an increasing trend. The notch root radius of 304 stainless steel bar increases with decreasing numerical simulation. [ABSTRACT FROM AUTHOR]

The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users' electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion. [ABSTRACT FROM AUTHOR]

The regular performance deterioration of P-type crystalline silicon solar modules and module strings caused by potential-induced degradation in a photovoltaic power plant was found in the field. The PID-affected solar modules dismounted from the photovoltaic power plant were further investigated systematically in the laboratory. For the first time, we found that the neutral point of voltage in a module string moved forward to the positive pole for a PID-affected module string as time goes on. Even if low positive voltage is applied to a PID-prone module, it could cause PID. The thermographic and electroluminescence (EL) images of a PID-affected module string also exhibit a regular degradation pattern. This is in good agreement with the measured power loss of the dismounted solar modules under standard test conditions. The results obtained in this paper show that the maximum power degradation rate of solar modules was as high as 53.26% after only one year of operation because of PID in the field. Due to the vast amount of solar modules and incomplete recovery, this is a terrible catastrophe for the owner of a power plant and module producer. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is a crucial aspect of building a sustainable smart city and, more broadly, relevant for improving environmental, economic, and social well-being. Non-intrusive load monitoring (NILM) is a computing technique that estimates energy consumption in real-time and helps raise energy awareness among users to facilitate energy management. Most NILM solutions are still a single machine approach and do not fit well in smart cities. This work proposes a model-agnostic hybrid federated learning framework to collaboratively train NILM models for city-wide energy-saving applications. The framework supports both centralised and decentralised training modes to provide a cluster-based, customisable and optimal learning solution for users. The proposed framework is evaluated on a real-world energy disaggregation dataset. The results show that all NILM models trained in our proposed framework outperform the locally trained ones in accuracy. The results also suggest that the NILM models trained in our framework are resistant to privacy leakage. [ABSTRACT FROM AUTHOR]

Electrochemical Impedance Spectroscopy (EIS) plays a crucial role in characterizing the internal electrochemical states of lithium-ion batteries and proves to be effective for estimating battery states. Traditional EIS measurement, however, requires expensive electrochemical workstations with time-consuming signal injection, especially in low-frequency regions, thus limiting its practical applications. Here we show that applying our proposed pulse-like Binary Multi-Frequency Signals (BMFS) as the excitation signal in the EIS measurement, which simultaneously possesses numerous frequency components and maintains high energy at each frequency component, will significantly improve test speed while retaining accuracy. The applicability of the BMFS under various cathode material types, including nickel cobalt manganese (NCM), lithium cobalt oxide (LCO), and lithium iron phosphate (LFP) is demonstrated. The robustness of the signal is experimentally verified through varying C-rates and measurement window lengths. The BMFS, requiring only 30 s per test, can achieve test results with an amplitude error of 1% and a phase error of 1° as compared with those obtained from traditional EIS tests. Moreover, BMFS can also be applied in online EIS measurement scenarios, favorable for real-world applications. This work enables accurate and rapid acquisition of EIS results, which is currently expensive and time-consuming to obtain, ensuring a faster and more nuanced characterization of the internal states of many battery systems in an affordable and accessible manner, especially in data-driven and machine-learning approaches. • A rapid EIS method using high-energy, wide-frequency signals is proposed. • EIS test time is reduced from 240 s to 30s, compared to electrochemical workstations. • Applicable for batteries with various cathode materials and impedance levels. • MAPE of this method is reduced by 58% compared to the MAPE of existing methods. • An application example of online EIS measurement during battery discharge is provided. [ABSTRACT FROM AUTHOR]

As the data-rate of the optical fiber communication system evolves to 400 Gb/s or even 1 Tb/s, higher requirements are put forward for transmission rate and repeater distance. To meet the requirements, further decreasing the optical fiber attenuation coefficient is one of the effective methods. In this paper, we did research on the relationship between the distribution of elements and frozen-in stress in the core layer and the cladding. Subsequently, their effect on optical fiber attenuation is also investigated. The results show that the co-doping of germanium, fluorine, and chlorine in the core layer can improve the stress matching between the core layer and the optical cladding. In addition, based on our research, the outer cladding with a high softening point can restrain the stress mismatch between the core layer and the optical cladding. Furthermore, the thickness of the second cladding will also affect the stress mismatch. Eventually, ultra-low loss fibers with the optimal attenuation of 0.156 dB/km at 1550 nm were prepared successfully by analyzing the stress distribution inside the optical fiber and adjusting the doping amount of germanium, fluorine, chlorine and other elements in the core layer, optical cladding, and outer cladding to maintain that the core layer and the optical cladding share the same stress. • Researched the effect of frozen-in stress on optical fiber attenuation and proposed a method to get lower attenuation. • Propose a scheme of Ge,F and Cl CO-dropped,suitable for mass production of ultra-low loss fiber core rods by VAD. • Reducing the stress difference between the fiber core layer and the optical cladding can reduce fiber attenuation. • Providing fiber support for the evolution of a single channel to a higher rate of optical fiber communication system. [ABSTRACT FROM AUTHOR]

• Method of Defect Compensation was proposed to reduce the content of ODC, E' center and NBOHC. • The glass has high transmittance with internal transmittance of 99.9%@ 193nm, low stress birefringence with 0.9nm/cm @632.8nm and good optical homogeneity with 0.78ppm @ 632.8nm. Synthetic quartz materials with high ultraviolet transmittance are widely used in lenses and other optical devices. However, the relatively insufficient transmittance limits the application. In this paper, method of Defect Compensation is proposed to reduce the content of ODC, E'center and NBOHC and synthetic quartz glass with UV high transmittance was prepared successfully. The glass has high transmittance with internal transmittance of 99.9%@193nm, low stress birefringence with 0.9nm/cm @632.8nm and good optical homogeneity with 0.78ppm @ 632.8nm. [ABSTRACT FROM AUTHOR]

The interaction between pyromellitic and 1,5-diaminonaphthalene forms nanoparticle modules that self-assemble into nanotentacle-constructed superstructures via solute–solvent interaction. A charge storage mechanism of alternate uptake of Zn2+/CF 3 SO 3 − at zincophilic-sites and multielectron redox response to form O−Zn−N bonds is proposed. The ion-compatible pore architectures render fast desolvation and low diffusion energy barrier. This unique electrochemistry harvests high-energy (145 Wh kg−1), large-current survivability (100 A/g) and ultralong-life (300,000 cycles) for Zn-ion storage. [Display omitted] • Modulating the solvent − precursor interaction gives tailor-made spherical carbon superstructures. • Carbon superstructure cathodes enable high-capacity, high-energy and ultralong-life Zn-ion capacitors. • An opposite charge-carrier co-storage mechanism within carbon superstructures is proposed. Designing well-structured carbon nanomaterials is crucial for promoting zinc-ion hybrid capacitors with high-kinetics and large-current Zn2+-storage viability. Herein we report the solvent-guided nanoarchitecturing of polyimide precursor to customize versatile heterodiatomic carbon superstructures (CS). Modulating the solvent-precursor interaction through a solubility parameter model and molecular dynamic simulation optimizes the thermodynamic solubilization (–2.14 eV) and growth kinetics (–9.22 eV) of polymeric intermediates with a minimum energy obstruction. The solvent-optimized CS exhibit well-defined spherical topology, ion-compatible pore channels and favorable dual-function motifs, affording more ample-exposed zincophilic platforms and high-speed ion transport routes. As a consequence, the assembled Zn||CS hybrid capacitor activates superior electrochemical activity and durability, including superior rate capacities (240 mAh g−1 at 0.5 A g−1, 108 mAh g−1 at 100 A g−1), high energy density (145 Wh kg−1) and ultralong lifespan (300,000 cycles at 50 A g−1). Marriage of experimental studies and theoretical calculations unravel the alternate storage of opposite charges in CS cathode, which involves high-kinetics physical Zn2+/CF 3 SO 3 − uptake at zincophilic sites and chemical redox of Zn2+ ions with carbonyl/pyridine motifs to initiate O−Zn−N bonds. Molecular dynamics simulations demonstrate that diffusion kinetics of Zn2+ ions is greatly facilitated by > 10 Å micropores with low energy barriers, but is blocked by < 7 Å micropores. This work provides new insights into the structural engineering of carbon superstructures for advanced energy storage. [ABSTRACT FROM AUTHOR]

Multifunctional photothermal protective films are novel photothermal-conversion materials that efficiently harvest and utilize solar energy. They also require excellent stability and durability. However, the poor toughness, loose structure, and weak shielding performance of these films limit their applications in photothermal conversion and metal protection. Inspired by the delicate microstructure of nacre, which exhibits excellent strength and barrier protection performance, a coordination-crosslinking self-assembly strategy is proposed herein that realizes a bio-phytic acid–silane–graphene oxide (bio-PSGO) metal-protective film with nacre-mimetic structures. The self-assembled bio-PSGO film exhibits good flexibility, excellent photothermal-conversion efficiency (93.9 %), outstanding protective performance, and water-desalination ability under natural sunlight irradiation. The coordination-crosslinking self-assembly process is simple and spontaneous, and it quickly assembles a complete thin film in an aqueous system. Nacre-like structured bio-PSGO films are easily obtained on the surfaces of metal substrates, such as iron, copper, zinc, magnesium, aluminum, and nickel. Metal ions released from metal materials promote the self-assembly of films via coordination crosslinking with PSGO nanosheets; meanwhile, the thermally conductive metal materials quickly transfer the heat generated in the photothermal-conversion process, synergistically improving the photothermal efficiency of the system. The proposed strategy designs robust and flexible materials that simulate the lamellar microstructure of nacre, which can be applied in flexible wearable devices and sensors, energy supply, metal protection, seawater desalination, solar thermal de-icing, and aerospace technologies. • Nacre-imitated photothermal-protective film (Bio-PSGO) was fabricated by a coordination crosslinking self-assembly strategy. • Bio-PSGO films exhibit outstanding photothermal-conversion performance, mechanical properties, and protective properties. • Eco-friendly Bio-PSGO films can be self-assembled on different metal surfaces • Appling for seawater desalination, solar thermal de-icing, metal protection, and flexible wearable devices and sensors [ABSTRACT FROM AUTHOR]

Accurate capacity estimation is essential in the management of lithium-ion batteries, as it guarantees the safety and dependability of battery-powered systems. However, direct measurement of battery capacity is challenging due to the unpredictable working conditions and intricate electrochemical characteristics, which complicates the identification of battery degradation. In this work, through in-depth analysis of battery aging data, an incremental slope (IS) aided feature extraction method is proposed to obtain universal multidimensional features that adapt to different working conditions. With the extracted features, a simple multilayer perceptron (MLP) is used to achieve high-precision capacity estimation. Furthermore, a feature matching based transfer learning (FM-TL) method is proposed to automatically adapt the capacity estimation across different types of batteries that are cycled under various working conditions. 158 batteries covering five material types and 15 working conditions are used to validate the proposed method. Results suggest that the MLP model can provide an accurate capacity estimation, where the overall mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are limited to 1.22% and 1.61%, respectively. Furthermore, compared with the traditional fine-tuning method, the overall MAPE and RMSPE under various transfer learning application scenarios respectively decrease by up to 78.23% and 75.31%, indicating that the FM-TL method is promising to construct a reliable transfer learning path, which improves the accuracy and reliability of capacity estimation when applied to various target domains. [Display omitted] • A novel extraction ideal based on the aiding of incremental slope is proposed. • A novel feature matching method is proposed to construct reliable and accurate transfer learning path. • 142 batteries with different material types and cycled under various working conditions are used for verification. • The overall MAPE and RMSPE can respectively decrease by up to 78.23% and 75.31% with the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this work was to examine how harsh luminous conditions in a cockpit, such as lightning in a thunderstorm or direct sunlight immediately after an aircraft passes through clouds, may affect the visual performance of pilots, and how to improve it. Such lighting conditions can result in the temporary visual impairment of aviators, which may greatly increase the risk of accidents. Tests were carried out in a full-scale simulator cockpit in which two kinds of dynamic lighting scenes, namely pulse changed and step changed lighting, were used to represent harsh luminous conditions. Visual acuity (VA), reaction time (RT) and identification accuracy (IA) were recorded as dependent variables. Data analysis results indicate that standardized VA values decreased significantly in both pulsing and step conditions in comparison with the dark condition. Standardized RT values increased significantly in the step condition; on the contrary, less reaction time was observed in the pulsing condition. Such effects could be reduced by an ambient illumination provided by a fluorescent lamp in both conditions. The results are to be used as a principle for optimizing lighting design with a thunderstorm light. [Copyright &y& Elsevier]

• An improved multi-timescale coordinated control strategy is proposed to be used in an integrated energy system. • A hybrid energy storage system is introduced into the system. • Operating costs and carbon emissions are considered as the optimization objectives. • General rules for sizing hybrid energy storage system are proposed in this paper. In view of the complex energy coupling and fluctuation of renewable energy sources in the integrated energy system, this paper proposes an improved multi-timescale coordinated control strategy for an integrated energy system (IES) with a hybrid energy storage system (HESS). The power response characteristics of various devices in IES at different time scales are analyzed which shows that the multi-timescale coordinated control method can be well coupled with the IES including HESS. The advantages of HESS over single energy storage system in stabilizing power fluctuation and extending energy storage life are compared and analyzed while the control method of supercapacitor under multi-time scale coordinated control strategy is proposed. The primary energy consumption, operating costs, carbon dioxide emissions and load shedding costs are considered the optimization objectives in the day-ahead rolling optimization stage according to the day-ahead forecast renewable energy (RE) data. In the intraday rolling adjusting stage, the proposed method can reduce the effect of the RE day-ahead forecasting errors to achieve the intraday energy scheduling balance and ensure the safe operation of the devices of IES. Considering the background of IES with a high proportion of renewable energy, this paper makes innovative use of the advantages of HESS to improve the power response characteristic of the system. The high-frequency part of RE is balanced by HESS in the real-time coordinated control stage. A typical structure of IES is applied and the simulation results are analyzed to show the performance of the proposed method in this paper. Moreover, general rules for sizing of HESS are also proposed. The influence of the hybrid energy storage capacity allocation ratio on battery capacity loss and carbon emission reduction is discussed. The Pareto front including the configuration and cost of supercapacitor has been obtained. As shown in the case, the application of HESS in the IES can improve the power response characteristic, extend the lithium-ion battery (LiB) life and has more benefits in reducing carbon emissions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Regions where feed-in tariff higher than valley price should follow dual objective optimization. • The economy of the household PV-BES-EV system mainly depends on the purchase cost of EVs. • Decreasing electricity price will not undermine the effectiveness of the household PV-BES-EV system. • The energy bills and carbon emissions decrease significantly with the optimal scheduling schemes. The household PV-BES-EV system (photovoltaic - battery energy storage - electric vehicle system) connected to the grid can guarantee the electricity demand of the household and facilitate the flexible trade with the grid to achieve the goals of cutting energy bills and reducing carbon emissions. In related issues, past studies often assume that renewable energy is both economical and environmentally friendly, and thus regarded it as the primary power source. While in fact, in some regions where the feed-in tariff is higher than the sales price at a certain time, households may be driven by arbitrage opportunities to frequently purchase electricity from the grid at the certain time to gain benefits, resulting in far greater carbon emissions than expected within the household measurement boundary. To avoid the environmental burden caused by pure economic incentives and to provide practical and sustainable solutions consistent with various energy price policies, the optimal scheduling schemes of the household PV-BES-EV system that considers both economic and environmental incentives for different scenarios solved by the genetic algorithm is proposed in this paper. Household data in Shanghai, China and Hamburg, Germany were selected for modelling, representing the dual-objective optimization scenario with arbitrage opportunities and the single-objective optimization scenario with sales price higher than feed-in tariff, respectively. Results show that for urban and rural residents in Shanghai, the energy bill and carbon emission would decline about 105%, 48% and 88%, 62%, respectively. As for Hamburg, the accordingly results would be 69% and 64%. In addition, based on the cost-effectiveness of the system in long-term, this paper puts forward policy recommendations that align with local conditions to further improve the economy of the system. [ABSTRACT FROM AUTHOR]

A transparent and anti-corrosive coating is of great importance for the protection of valuable articles, historical relics, and metal. However, there is a significant challenge due to the trade-off between high transparency and pleased anti-corrosive performance. The barrier function of skin, derived from the "brick and mortar" microstructure where hydrophobic corneocytes are parallelly embedded in lipids of the skin stratum corneum, offers an admirable concept to solve the problem. Herein, a novel bio-inspired assembly strategy is implemented to fabricate transparent and high-performance anti-corrosive epoxy nanocomposite coatings (Bio-MFICs), cooperating with superhydrophobic magnetic responsive graphene oxide nanosheets (FSO-GO) parallelly aligned in epoxy resin via magnetic-field-induced assembly and bridging structure further forming during curing process. The structure of Bio-MFICs is highly consistent with the "brick and mortar" structure of the skin stratum corneum, where FSO-GO nanosheets act as "corneocytes" and epoxy matrix acts as "lipids". The subtle skin-mimetic structure contributes to the excellent anti-corrosive performance of Bio-MFICs because of the prolonged and blocked diffusion pathway of corrosive media. As a result, the resistance of Bio-MFIC- 3 (0.75 wt% filler content) remains 4.17×107 Ω·cm−2, which is 43 times that of the pure epoxy coating, and the logarithm of breaking point frequency is quite low as −1.05 after immersion in 3.5 wt% NaCl solution for 28 days. Moreover, the transparency of the Bio-MFIC- 3 is highly close to that of the pure epoxy coating. This work provides an effective method of constructing transparent protective materials to reduce the loss of materials and achieve the goal of energy-saving and greenhouse gas emissions reduction. • A novel skin-mimetic strategy is proposed to fabricate transparent coatings with excellent barrier performance. • Magnetic-field-induced assembly is applied to construct the parallelly-aligned structure of graphene oxide sheets in epoxy. • Superhydrophobic magnetic responsive graphene oxide is synthesized by an in-situ method. • The anti-corrosion is realized by the prolonged diffusion pathway and higher diffusion resistance. [ABSTRACT FROM AUTHOR]

To improve the estimation accuracy of spectral reflectance from the given trichromatic value, a new two-step k-nearest neighbors locally weighted linear regression method is proposed. The algorithm has good local learning ability and can take into account the similarity of colorimetric and spectral reflectance space. The simulated and practical imaging experiments were carried out with Munsell matte and glossy dataset, respectively. Experimental results show that the mean root mean square error values of the spectral reflectance estimated by our model in simulated RGB, practical imaging Adobe RGB. and raw RGB data experiments are 0.00731, 0.01519, and 0.01453, respectively, and the mean color difference values under CIE standard illuminant D65 are 0.380, 1.311, and 1.180, respectively. In addition, we showed the calculation time cost of various models in the practical experiment. The calculation time of one sample for the proposed method is 0.094 s. The proposed method is better than several state-of-the-art methods in terms of comprehensive estimation performance and running efficiency. [ABSTRACT FROM AUTHOR]

In this paper, an isolated series asymmetrical half-bridge converter (SA-HBC) is proposed to satisfy the high power and high input-voltage requirements. In the proposed converter, two half-bridge modules with series configuration are adopted in the primary side to reduce the switch voltage stress to half of the input voltage. Moreover, the series half-bridge cells share the same transformer and leakage inductance, which simplifies the circuit structure. Zero-voltage-switching transition is achieved for all the active switches by employing the asymmetrical control scheme to reduce the switching losses. Furthermore, the voltages of the input capacitors are automatically balanced without any additional components or complex control methods. In addition, a family of dc–dc converters with series half-bridge structure is explored to give a universal discussion and extensive applications of the proposed contributions for the high input-voltage systems. Finally, a 500–600-V input 48-V output 1-kW prototype operating with 100 kHz switching frequency is built and tested to demonstrate the effectiveness of the proposed SA-HBC converter. The efficiency of 95% at full load is realized. [ABSTRACT FROM AUTHOR]

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future. [ABSTRACT FROM AUTHOR]

Power systems optimization is generally subject to the compromise between performance and cost. The 2021 Texas grid outage illustrates the worldwide dangers for the regional-centralized power grid, with comparable advantages to safety and flexibility for the distributed energy system. The storage of household batteries helps balance grid load and increase system stability and flexibility. However, household storage battery is still not widely used today because of its high costs. Currently, research on increasing household battery storage applicability is focused largely on optimizing economic strategies, such as configuration, dispatching and subsidy policies, which rely substantially more on technologies and financial perspectives. Consumers are not 'rational' individuals, and non-economic incentives can affect their decisions without raising prices. This paper consequently proposes to encourage users to acquire household battery storage to increase efficiency of power dispatching and economic advantages based on behavioral economics. In this paper, an empirical research builds upon the utility model of behavioral economics incentives and purchase willingness. Moreover, the multi-objective genetic algorithm is utilized to optimize the dispatching of household battery storage by using grid variance and user revenues as optimizing goals. The results of this paper show that the behavioral economics incentive improves intention to buy the household battery energy storage by 10.7% without raising subsidies. By improving the energy dispatching strategy, peak-load shifting performance and user revenues are improved by 4.2% and 10.6%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the application of traceability systems in the food and drug industry has developed rapidly, but it is rarely used for wind turbines. From the aspects of low information transparency and information islands in the supply chain process for wind turbines, a reliable traceability system is essential. However, the existing traceability systems are not suitable to be directly applied to wind turbines. Consequently, according to the characteristics of the wind power industry, a semi-centralized traceability architecture based on Internet of Things technology was proposed. Furthermore, a traceability platform was constructed by analyzing the information collected in each stage related to various user needs of wind turbines, and various applications, including manufacturing management and spare parts management, were developed. Compared with the existing systems, the proposed platform was wind-turbine-oriented, effectively improved traceability efficiency and enterprises' information security, and extended the length of the traceability chain by integrating the after-sales information. The traceability of key components of wind turbines during their life cycle provides a useful reference for further improving the parts quality management system of the wind power industry. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is conducive to the change in the wind power industry management model and is beneficial to the green design of products. Nowadays, none of the LCA systems are for wind turbines and the concept of Internet of Things (IoT) in LCA is quite a new idea. In this paper, a four-layer LCA platform of wind turbines based on IoT architecture is designed and discussed. In the data transmission layer, intelligent sensing of wind turbines can be achieved and their status and location can be monitored. In the data transmission layer, the LCA platform can be effectively integrated with enterprise information systems through the object name service (ONS) and directory service (DS). In the platform layer, a model based on IMPACT 2002+ is developed, and four management modules are designed. In the application layer, different from other systems, energy payback time (EPBT) is selected as an important evaluation index for wind turbines. Compared with the existing LCA systems, the proposed system is specifically for wind turbines and can collect data in real-time, leading to improved accuracy and response time. [ABSTRACT FROM AUTHOR]

Despite the rapid development of photovoltaic (PV) industry, direct current (DC) fault arc remains a major threat to the safety of PV system and personnel. While extensive research on DC fault arc has been conducted, little attention has been paid to the long-time interactions between the PV system and DC arc. In this paper, a simulation system with an arc model and PV system model is built to overcome the inconvenience of the fault-arc experiments and understand the mechanism of these interactions. For this purpose, the characteristics of the series DC arc in a small grid-connected PV system are first investigated under uniform irradiance. Then, by comparing with different arc models, the Habedank model is selected to simulate the fault arc and a method to determine its parameters under DC arc condition is proposed. The trends of simulated arc waveforms are consistent with the measured data, whose fitting degree in adjusted R-squared is between 0.946 and 0.956. Finally, a phenomenon observed during the experiment, that the negative perturbation of the maximum power point tracking (MPPT) algorithm can reduce the arc current, is explained by the proposed model. [ABSTRACT FROM AUTHOR]