Rational design of natural leather-based water evaporator for electricity generation and functional applications

In recent years,water evaporation-induced electricity has attracted a great deal of attention as an emerging green and renewable energy harvesting technology.Although abundant materials have been developed to fabricate hydrovoltaic devices,the limitations of high costs,inconvenient storage and transport,low environmental benefits,and unadaptable shape have restricted their wide applications.Here,an electricity generator driven by water evaporation has been engineered based on natural biomass leather with inherent properties of good moisture permeability,excellent wettability,physicochemical stability,flexibility,and biocompatibility.Including numerous nano/microchannels together with rich oxygen-bearing functional groups,the natural leather-based water evaporator,Leather_(Emblic-NPs-SA/CB),could continuously produce electricity even staying outside,achieving a maximum output voltage of∼3 V with six-series connection.Furthermore,the leather-based water evaporator has enormous potential for use as a flexible self-powered electronic floor and seawater demineralizer due to its sensitive pressure sensing ability as well as its excellent photothermal conversion efficiency(96.3%)and thus fast water evaporation rate(2.65 kg m^(−2)h^(−1)).This work offers a new and functional material for the construction of hydrovoltaic devices to harvest the sustained green energy from water evaporation in arbitrary ambient environments,which shows great promise in their widespread applications.

Harnessing overlapped temperature-salinity gradient in solar-driven interfacial seawater evaporation for efficient steam and electricity generation

Solar-driven interfacial water evaporation(SIWE)offers a superb way to leverage concentrated solar heat to minimize energy dissipation during seawater desalination.It also engenders overlapped temperaturesalinity gradient(TSG)between water-air interface and adjacent seawater,affording opportunities of harnessing electricity.However,the efficiency of conventional SIWE technologies is limited by significant challenges,including salt passivation to hinder evaporation and difficulties in exploiting overlapped TSG simultaneously.Herein,we report self-sustaining hybrid SIWE for not only sustainable seawater desalination but also efficient electricity generation from TSG.It enables spontaneous circulation of salt flux upon seawater evaporation,inducing a self-cleaning evaporative interface without salt passivation for stable steam generation.Meanwhile,this design enables spatial separation and simultaneous utilization of overlapped TSG to enhance electricity generation.These benefits render a remarkable efficiency of90.8%in solar energy utilization,manifesting in co-generation of solar steam at a fast rate of 2.01 kg m^(-2)-h^(-1)and electricity power of 1.91 W m^(-2)with high voltage.Directly interfacing the hybrid SIWE with seawater electrolyzer constructs a system for water-electricity-hydrogen co-generation without external electricity supply.It produces hydrogen at a rapid rate of 1.29 L h^(-1)m^(-2)and freshwater with 22 times lower Na+concentration than the World Health Organization(WHO)threshold.

Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Analyzing the Wind-Dominant Electricity Market under Coexistence of Regulated and Deregulated Power Trading

Currently,both regulated and deregulated power trading exist in China’s power system,which has caused imbalanced funds in the electricity market.In this paper,a simulation analysis of the electricity market with wind energy resources is conducted,and the calculation methods of unbalanced funds are investigated systematically.In detail,the calculation formulas of unbalanced funds are illustrated based on their definition,and a two-track electricity market clearing model is established.Firstly,the concept of the dual-track system is explained,and the specific calculation formulas of various types of unbalanced funds are provided.Next,considering the renewable energy consumption,the market clearing model based on DC power flow is constructed and solved;by combining fitting methods of mid-and long-term curves,the unbalanced funds are calculated based on clearing results and formulas.

Analysis of Electricity Consumption Pattern Clustering and Electricity Consumption Behavior

Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.

Energy Storage Operation Modes in Typical Electricity Market and Their Implications for China

As the Chinese government proposes ambitious plans to promote low-carbon transition,energy storage will play a pivotal role in China’s future power system.However,due to the lack of a mature electricity market environment and corresponding mechanisms,current energy storage in China faces problems such as unclear operational models,insufficient cost recovery mechanisms,and a single investment entity,making it difficult to support the rapid development of the energy storage industry.In contrast,European and American countries have already embarked on certain practices in energy storage operation models.Through exploration of key issues such as investment entities,market participation forms,and cost recovery channels in both front and back markets,a wealth of mature experiences has been accumulated.Therefore,this paper first summarizes the existing practices of energy storage operation models in North America,Europe,and Australia’s electricity markets separately from front and back markets,finding that perfect market mechanisms and reasonable subsidy policies are among the main drivers for promoting the rapid development of energy storage markets.Subsequently,combined with the actual development of China’s electricity market,it explores three key issues affecting the construction of costsharing mechanisms for energy storage under market conditions:Market participation forms,investment and operation modes,and cost recovery mechanisms.Finally,in line with the development expectations of China’s future electricitymarket,suggestions are proposed fromfour aspects:Market environment construction,electricity price formation mechanism,cost sharing path,and policy subsidy mechanism,to promote the healthy and rapid development of China’s energy storage industry.

Simulation and Analysis of Cascading Faults in Integrated Heat and Electricity Systems Considering Degradation Characteristics

Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.

Electricity as an Energy Vector: A Performance Comparison with Hydrogen and Biodiesel in Italy

This study presents a comparative analysis of electricity, hydrogen, and biodiesel as energy vectors, with a focus on powering an aluminum smelter in southern Italy. It evaluates these vectors in terms of efficiency, land requirements for carbon-neutral energy production, and capital expenditure, providing insights throughout the entire supply chain (upstream, midstream, and downstream) into their feasibility for industrial applications. The research reveals that biodiesel, despite being carbon neutral, is impractical due to extensive land requirements and lower efficiency if compared to other vectors. Hydrogen, downstream explored in two forms as thermal power generation and fuel cell technology, shows lower efficiency and higher capital expenditure compared to electricity. Additionally, green hydrogen production’s land requirements significantly exceed those of electricity-based systems. Electricity emerges as the most viable option, offering an overall higher efficiency, lower land requirements for its green production, and comparatively lower capital expenditure. The study’s findings highlight the importance of a holistic assessment of energy vectors, considering economic, environmental, and practical aspects along the entire energy supply chain, especially in industrial applications where the balance of these factors is crucial for long-term sustainability and feasibility. This comprehensive analysis provides valuable guidance for similar industrial applications, emphasizing the need for a balanced approach in the selection of energy vectors.

Valorization of Agricultural Residues for Hydrogen-Based Electricity Generation towards Circular Bioeconomy

Global crises, notably climate shocks, degraded ecosystems, and growing energy demand, enforce sustainable production and consumption pathways. A circular bioeconomy offers the opportunities to actualize resource and eco-efficiency enhancement, valorization of waste streams, reduction of fossil energy and greenhouse gas (GHG) emissions. Albeit biomass resources are a potential feedstock for bio-hydrogen (bio-H2) production, Ghana’s agricultural residues are not fully utilized. This paper examines the economic and environmental impact of bio-H2 electricity generation using agricultural residues in Ghana. The bio-H2 potential was determined based on biogas steam reforming (BSR). The research highlights that BSR could generate 2617 kt of bio-H2, corresponding to 2.78% of the global hydrogen demand. Yam and maize residues contribute 50.47% of the bio-H2 produced, while millet residues have the most negligible share. A tonne of residues could produce 16.59 kg of bio-H2 and 29.83 kWh of electricity. A total of 4,705.89 GWh of electricity produced could replace the consumption of 21.92% of Ghana’s electricity. The economic viability reveals that electricity cost is $0.174/kWh and has a positive net present value of $2135550609.45 with a benefit-to-cost ratio of 1.26. The fossil diesel displaced is 1421.09 ML, and 3862.55 kt CO2eq of carbon emissions decreased corresponding to an annual reduction potential of 386.26 kt CO2eq. This accounts for reducing 10.26% of Ghana’s GHG emissions. The study demonstrates that hydrogen-based electricity production as an energy transition is a strategic innovation pillar to advance the circular bioeconomy and achieve sustainable development goals.

Optimizing Household Wastes (Rice, Vegetables, and Fruit) as an Environmentally Friendly Electricity Generator

The high consumption of electricity and issues related to fossil energy have triggered an increase in energy prices and the scarcity of fossil resources.Consequently,many researchers are seeking alternative energy sources.One potential technology,the Microbial Fuel Cell(MFC)based on rice,vegetable,and fruit wastes,can convert chemical energy into electrical energy.This study aims to determine the potency of rice,vegetable,and fruit waste assisted by Cu/Mg electrodes as a generator of electricity.The method used was a laboratory experiment,including the following steps:electrode preparation,waste sample preparation,incubation of the waste samples,construction of a reactor using rice,vegetable,and fruit waste as a source of electricity,and testing.The tests included measuring electrical conductivity,electric current,voltage,current density,and power density.Based on the test results,the maximum current and voltage values for the fruit waste samples were 5.53 V and 11.5 mA,respectively,with a current density of 2.300 mA/cm^(2) and a power density of 12.719 mW/cm^(2).The results indicate the potential for a future development.The next step in development involves determining the optimum conditions for utilizing of rice,vegetable,and fruit waste.The results of the electrical conductivity test on rice,vegetable,and fruit waste samples were 1.51,2.88,and 3.98 mS,respectively,with the highest electrical conductivity value found in the fruit waste sample.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

A Review of Atmospheric Electricity Research in China from 2019 to 2022

Atmospheric electricity is composed of a series of electric phenomena in the atmosphere.Significant advances in atmospheric electricity research conducted in China have been achieved in recent years.In this paper,the research progress on atmospheric electricity achieved in China during 2019-22 is reviewed focusing on the following aspects:(1)lightning detection and location techniques,(2)thunderstorm electricity,(3)lightning forecasting methods and techniques,(4)physical processes of lightning discharge,(5)high energy emissions and effects of thunderstorms on the upper atmosphere,and(6)the effect of aerosol on lightning.

Evolutionary game-based optimization of green certificate-carbon emission right-electricity joint market for thermal-wind-photovoltaic power system

With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-market trading process is proposed to study the market-based strategy for renewable energy.Considering the commodity characteristics of green certificates and carbon emission rights,the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models.Furthermore,considering the irrational bidding behavior of energy suppliers in the actual electricity market,an evolutionary game based multi-market bidding optimization model is presented.Subsequently,it is solved using a composite differential evolutionary algorithm.Finally,the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.

Promoting and controlling electron transfer of furfural oxidation efficiently harvest electricity,furoic acid,hydrogen gas and hydrogen peroxide

Conventional chemical oxidation of aldehydes such as furfural to corresponding acids by molecular oxygen usually needs high pressure to increase the solubility of oxygen in aqueous phase,while electrochemical oxidation needs input of external electric energy.Herein,we developed a liquid flow fuel cell(LFFC)system to achieve oxidation of furfural in anode for furoic acid production with co-production of hydrogen gas.By controlling the electron transfer in cathode for reduction of oxygen,efficient generation of electricity or production of H_(2)O_(2)were achieved.Metal oxides especially Ag_(2)O have been screened as the efficient catalyst to promote the oxidation of aldehydes,while liquid redox couples were used for promoting the kinetics of oxygen reduction.A novel alkaline-acidic asymmetric design was also used for anolyte and catholyte,respectively,to promote the efficiency of electron transfer.Such an LFFC system achieves efficient conversion of chemical energy of aldehyde oxidation to electric energy and makes full use the transferred electrons for high-value added products without input of external energy.With(VO_(2))_(2)SO_(4)as the electron carrier in catholyte for four-electron reduction of oxygen,the peak output power density(Pmax)at room temperature reached 261 mW/cm^(2)with furoic acid and H_(2)yields of 90%and 0.10 mol/mol furfural,respectively.With anthraquinone-2-sulfonate(AQS)as the cathodic electron carrier,Pmaxof 60 mW/cm^(2)and furoic acid,H_(2)and H_(2)O_(2)yields of 0.88,0.15 and 0.41 mol/mol furfural were achieved,respectively.A new reaction mechanism on furfural oxidation on Ag_(2)O anode was proposed,referring to one-electron and two-electron reaction pathways depending on the fate of adsorbed hydrogen atom transferred from furfural aldehyde group.

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results.In the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

Ferroelectricity induced by the absorption of water molecules on double helix SnIP

We study the ferroelectricity in a one-dimensional(1D)system composed of a double helix SnIP with absorbing water molecules.Our ab initio calculations reveal two factors that are critical to the electrical polarization.The first one is the orientation of polarized water molecules staying in the R2 region of SnIP.The second one is the displacement of I atom which roots from subtle interaction with absorbed water molecules.A reasonable scenario of polarization flipping is proposed in this study.In the scenario,the water molecule is rolling-up with keeping the magnitude of its electrical dipole and changing its direction,meanwhile,the displacement of I atoms is also reversed.Highly tunable polarization can be achieved by applying strain,with 26.5%of polarization enhancement by applying tensile strain,with only 4%degradation is observed with 4%compressive strain.Finally,the direct band gap is also found to be correlated with strain.