Daily rolling estimation of carbon emission cost of coal-fired units considering long-cycle interactive operation simulation of carbon-electricity market

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets.The carbon emission cost(CEC)of coal-fired units becomes part of the power generation cost through market coupling.The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market.Study of carbon–electricity market interaction and CEC calculations is still in its initial stages.This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets.A long-period interactive operation simulation mechanism for the carbon–electricity market is established,and operation and trading models of the carbon emissions trading market and electric power market are established.A daily rolling estimation method for the CEC of coal-fired units is proposed,along with the CEC per unit electric quantity of the coal-fired units.The feasibility and effectiveness of the proposed method are verified through an example simulation,and the factors influencing the CEC are analyzed.

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.

Understanding the combustion behavior of electric bicycle batteries and unveiling its relationship with fire extinguishing

In this study,a detailed analysis of the combustion behaviors of the lithium iron phosphate(LFP)and lithium manganese oxide(LMO)batteries used in electric bicycles was conducted.This research included quantitative measurements of the combustion duration,flame height,combustion temperature,heat release rate,and total heat release.The results indicated that LMO batteries exhibited higher combustion temperatures of 600–700°C,flame heights of 70–75 cm,a significantly higher heat release rate of40.1 k W(12 Ah),and a total heat release of 1.04 MJ(12 Ah)compared to LFP batteries with the same capacity.Based on these experimental results,a normalized total heat release(NORTHR)parameter was proposed,demonstrating good universality for batteries with different capacities.Utilizing this parameter,quantitative calculations and optimization of the extinguishing agent dosage were conducted for fires involving these two types of batteries,and the method was validated by extinguishing fires for these two types of battery packs with water-based extinguishing fluids.

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.

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.

The impacts of market reform on the market penetration of natural gas-fired electricity and renewable energy in China

Natural gas-fired electricity(NGFE) is expected to play a more important role in the future due to its characteristics of low pollution, high efficiency and flexibility. However, its development in China is impeded by its high regulation price compared with coal power. Market reform is therefore of vital importance to promote the penetration of NGFE. The objective of this study is to analyze the impacts of market reform and the renewable electricity(RE) subsidy policy on the promotion of NGFE and RE. A dynamic game-theoretic model is developed to analyze the interaction among the NG supplier, the power sector and the power grid. Three scenarios are proposed with different policies, including a fixed regulation price of NG and electricity, real-time pricing(RTP) of NG and electricity, and subsidy targeted at RE. The results show that:(1) market reform can sharply decrease the NG price and consequently promote the development of NGFE and RE;(2) subsidy targeted at RE not only promotes the penetration of NGFE and RE, but also increases the utilization ratio of renewables significantly;(3) market reform and the subsidy also enhance consumers’ welfare by reducing their power consumption expenditure.

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.

UK signed an agreement with China on near zero emissions coal-fired electricity

On Dec. 22, Environment Secretary Margaret Beckett welcomed a UK/China landmark agreement on the development of clean coal technology with carbon dioxide capture and storage, which aims to reduce significantly the climate change impact from coal-fired electricity generation.

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.

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.

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).

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.

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.

Quantifying social costs of coal-fired power plant generation

Coal has been dominating the electricity supply in Indonesia,especially in long-term power generation from fossil energy.This dominance is due to lower production costs in coal-fired power plant generation.However,this low price is only based on monetary costs and ignores the social costs.Therefore,this study aims to quantify the social costs of coal-fired generation.Using QUERI-AirPacts modeling,the present study quantifies the social costs resulting from the Tenayan Raya coal-fired generation in Riau,Indonesia.It includes the levelized cost of electricity and health costs into the generation costs.After that,this study calculates the net present value,internal rate return,and project payback period.The study found that as much as$50.22/MWh was the levelized cost of electricity.While$15.978/MWh or$0.015978/kWh was the social cost that was not included in the generating cost.At the electricity production level of 1,380,171.69 MWh per year,there is an expected extra cost of$22,052,383.30 uncounted when externalities are included.For instance,the net present value(NPV)is lower and even negative when external costs are included(-$24,062,274.19)compared to$176,108,091.52 when externalities are not considered.The internal rate of return(IRR)is much higher when the social costs are not considered.The payback period is also shorter when the social costs are excluded than when the externalities are included.This global number indicates that the inclusion of external costs would impact NPV,IRR,and the payback period.This result implies that the government should internalize the external cost to stimulate the electricity producers to conduct cost-benefit analyses.The cost-benefit analysis mechanism would lead the producers to be more efficient.

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.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

Inter-Provincial Transaction Model in Two-Level ElectricityMarket Considering Carbon Emission and Consumption Responsibility Weights

In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.