In September 2024,Constellation Energy of Baltimore,MD,USA,owner and operator of the Three Mile Island(TMI)nuclear power plant near Middletown,PA,USA,announced that it would reopen the plant’s recently shuttered Unit...In September 2024,Constellation Energy of Baltimore,MD,USA,owner and operator of the Three Mile Island(TMI)nuclear power plant near Middletown,PA,USA,announced that it would reopen the plant’s recently shuttered Unit 1 reactor to provide electricity for data centers owned by tech giant Microsoft(Redmond,WA,USA)[1-3].展开更多
Combining water electrolysis and rechargeable battery technologies into a single system holds great promise for the co-production of hydrogen (H_(2)) and electricity.However,the design and development of such systems ...Combining water electrolysis and rechargeable battery technologies into a single system holds great promise for the co-production of hydrogen (H_(2)) and electricity.However,the design and development of such systems is still in its infancy.Herein,an integrated hydrogen-oxygen (O_(2))-electricity co-production system featuring a bipolar membrane-assisted decoupled electrolyzer and a Na-Zn ion battery was established with sodium nickelhexacyanoferrate (NaNiHCF) and Zn^(2+)/Zn as dual redox electrodes.The decoupled electrolyzer enables to produce H_(2)and O_(2)in different time and space with almost 100%Faradaic efficiency at 100 mA cm^(-2).Then,the charged NaNiHCF and Zn electrodes after the electrolysis processes formed a Na-Zn ion battery,which can generate electricity with an average cell voltage of 1.75 V at 10 m A cm^(-2).By connecting Si photovoltaics with the modular electrochemical device,a well-matched solar driven system was built to convert the intermittent solar energy into hydrogen and electric energy with a solar to hydrogen-electricity efficiency of 16.7%,demonstrating the flexible storage and conversion of renewables.展开更多
Electricity theft significantly impacts the reliability and sustainability of electricity services,particularly in developing regions.However,the socio-economic,infrastructural,and institutional drivers of theft remai...Electricity theft significantly impacts the reliability and sustainability of electricity services,particularly in developing regions.However,the socio-economic,infrastructural,and institutional drivers of theft remain inadequately explored.Here we examine electricity theft in Lubumbashi,Democratic Republic of Congo,focusing on its patterns,causes,and impacts on service quality.Theft rates exceeded 75%in peripheral municipalities like Katuba and Kampemba,driven by poverty,weak law enforcement,and poor infrastructure dominated by above-ground networks.In contrast,central areas like Kamalondo and Lubumbashi reported lower theft rates due to better urban planning and underground systems.We found that electricity theft directly correlates with frequent voltage fluctuations,prolonged outages,and grid overloads.Socio-economic factors,including high connection fees and poverty,emerged as primary drivers,while institutional weaknesses such as corruption and ineffective enforcement perpetuate theft.Addressing theft requires a holistic approach integrating infrastructure modernization,socio-economic reforms,and institutional strengthening.Transitioning to underground networks,providing affordable electricity access,and adopting advanced metering systems are crucial.Overall,this study highlights the systemic nature of electricity theft and provides actionable insights for improving electricity service delivery and equity in urban settings.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for th...The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.展开更多
The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastr...The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.展开更多
The present teaching content of the power electronics course is insufficient to cover the power electronics technology used in building electrical engineering.This paper analyzes the relationship between building elec...The present teaching content of the power electronics course is insufficient to cover the power electronics technology used in building electrical engineering.This paper analyzes the relationship between building electrical engineering and power electronics technology,investigates the main power electronics technology used in building electrical engineering,introduces the teaching content of current power electronics course,analyzes the insufficiency of current teaching content related to the practice of electrical engineering,and proposes the principles and directions for the reformation and innovation of the teaching content of the course of power electronics for the major of building electricity and intelligence.展开更多
Accurate forecasting of electricity spot prices is crucial for market participants in formulating bidding strategies.However,the extreme volatility of electricity spot prices,influenced by various factors,poses signif...Accurate forecasting of electricity spot prices is crucial for market participants in formulating bidding strategies.However,the extreme volatility of electricity spot prices,influenced by various factors,poses significant challenges for forecasting.To address the data uncertainty of electricity prices and effectively mitigate gradient issues,overfitting,and computational challenges associated with using a single model during forecasting,this paper proposes a framework for forecasting spot market electricity prices by integrating wavelet packet decomposition(WPD)with a hybrid deep neural network.By ensuring accurate data decomposition,the WPD algorithm aids in detecting fluctuating patterns and isolating random noise.The hybrid model integrates temporal convolutional networks(TCN)and long short-term memory(LSTM)networks to enhance feature extraction and improve forecasting performance.Compared to other techniques,it significantly reduces average errors,decreasing mean absolute error(MAE)by 27.3%,root mean square error(RMSE)by 66.9%,and mean absolute percentage error(MAPE)by 22.8%.This framework effectively captures the intricate fluctuations present in the time series,resulting in more accurate and reliable predictions.展开更多
Triboelectric nanogenerators(TENGs)offer a selfsustaining power solution for marine regions abundant in resources but constrained by energy availability.Since their pioneering use in wave energy harvesting in 2014,nea...Triboelectric nanogenerators(TENGs)offer a selfsustaining power solution for marine regions abundant in resources but constrained by energy availability.Since their pioneering use in wave energy harvesting in 2014,nearly a decade of advancements has yielded nearly thousands of research articles in this domain.Researchers have developed various TENG device structures with diverse functionalities to facilitate their commercial deployment.Nonetheless,there is a gap in comprehensive summaries and performance evaluations of TENG structural designs.This paper delineates six innovative structural designs,focusing on enhancing internal device output and adapting to external environments:high space utilization,hybrid generator,mechanical gain,broadband response,multi-directional operation,and hybrid energy-harvesting systems.We summarize the prevailing trends in device structure design identified by the research community.Furthermore,we conduct a meticulous comparison of the electrical performance of these devices under motorized,simulated wave,and real marine conditions,while also assessing their sustainability in terms of device durability and mechanical robustness.In conclusion,the paper outlines future research avenues and discusses the obstacles encountered in the TENG field.This review aims to offer valuable perspectives for ongoing research and to advance the progress and application of TENG technology.展开更多
Metal-Supported Solid Oxide Fuel Cells(MS-SOFCs)hold significant potential for driving the energy transition.These electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell(SOFCs)and can ...Metal-Supported Solid Oxide Fuel Cells(MS-SOFCs)hold significant potential for driving the energy transition.These electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell(SOFCs)and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells(PEMFCs)due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication processes.This review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future opportunities.Techniques such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged activity.These concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries(TPBs).This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available fuels.Lastly,the End-of-Life(EoL),recycling,and life-cycle assessments(LCAs)of the Fuel Cell Hybrid Electric Vehicles(FCHEVs)were discussed.LCAs comparing Fuel Cell Electric Vehicles(FCEVs)equipped with(PEMFCs)and FCHEVs equipped with MS-SOFCs,both powered with hydrogen(H_(2))generated by different routes were compared.This review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.展开更多
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...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.展开更多
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-qual...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.展开更多
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 ...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.展开更多
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 fabricat...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.展开更多
Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets.This study considers multiple neighboring countries in the form ...Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets.This study considers multiple neighboring countries in the form of alliances,introduces neighboring countries’exchange rates into the cross-border multi-agent power-trading market and proposes a method to study each agent’s dynamic decision-making behavior based on evolutionary game theory.To this end,this study uses three national agents as examples,constructs a tripartite evolutionary game model,and analyzes the evolution process of the decision-making behavior of each agent member state under the initial willingness value,cost of payment,and additional revenue of the alliance.This research helps realize cross-border energy operations so that the transaction agent can achieve greater trade profits and provides a theoretical basis for cooperation and stability between multiple agents.展开更多
The phase transition of water molecules in nanochannels under varying external electric fields is studied by molecular dynamics simulations.It is found that the phase transition of water molecules in nanochannels occu...The phase transition of water molecules in nanochannels under varying external electric fields is studied by molecular dynamics simulations.It is found that the phase transition of water molecules in nanochannels occurs by changing the frequency of the varying electric field.Water molecules maintain the ice phase when the frequency of the varying electric field is less than 16 THz or greater than 30 THz,and they completely melt when the frequency of the varying electric field is 24 THz.This phenomenon is attributed to the breaking of hydrogen bonds when the frequency of the varying electric field is close to their inherent resonant frequency.Moreover,the study demonstrates that the critical frequency varies with the confinement situation.The new mechanism of regulating the phase transition of water molecules in nanochannels revealed in this study provides a perspective for further understanding of the phase transition of water molecules in nanochannels,and has great application potential in preventing icing and deicing.展开更多
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 grad...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.展开更多
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 mar...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.展开更多
Because of the contradiction between the scale of new energy installations and the continuous load growth in the central and eastern regions of China,the balance problems of the electricity market are becoming increas...Because of the contradiction between the scale of new energy installations and the continuous load growth in the central and eastern regions of China,the balance problems of the electricity market are becoming increasingly prominent,and it is urgent to solve such problems through inter-provincial electricity spot markets.First,the development history and construction status of the inter-provincial electricity spot market are summarized;second,the mechanism design of the inter-provincial electricity spot market is sorted out in terms of the market operation framework,transaction declaration,and clearing methods;subsequently,the evaluation index system of the inter-provincial electricity spot market is constructed,including four themes of electricity mutual aid and support,new energy consumption,economic benefits of market-based allocation,and social benefits of market-based allocation;finally,the operation of the inter-provincial electricity spot market is comprehensively analyzed by the algorithm based on the market operation data of 2022,which proves the feasibility and practicality of the proposed index system.展开更多
The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and con...The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.展开更多
文摘In September 2024,Constellation Energy of Baltimore,MD,USA,owner and operator of the Three Mile Island(TMI)nuclear power plant near Middletown,PA,USA,announced that it would reopen the plant’s recently shuttered Unit 1 reactor to provide electricity for data centers owned by tech giant Microsoft(Redmond,WA,USA)[1-3].
基金National Natural Science Foundation of China (Nos. 52488201, 52076177, and 52476222)China National Key Research and Development Plan Project (No. 2021YFF0500503)+1 种基金Key Research and Development Program of Shaanxi (No. 2024GH-YBXM-02)China Fundamental Research Funds for the Central Universities。
文摘Combining water electrolysis and rechargeable battery technologies into a single system holds great promise for the co-production of hydrogen (H_(2)) and electricity.However,the design and development of such systems is still in its infancy.Herein,an integrated hydrogen-oxygen (O_(2))-electricity co-production system featuring a bipolar membrane-assisted decoupled electrolyzer and a Na-Zn ion battery was established with sodium nickelhexacyanoferrate (NaNiHCF) and Zn^(2+)/Zn as dual redox electrodes.The decoupled electrolyzer enables to produce H_(2)and O_(2)in different time and space with almost 100%Faradaic efficiency at 100 mA cm^(-2).Then,the charged NaNiHCF and Zn electrodes after the electrolysis processes formed a Na-Zn ion battery,which can generate electricity with an average cell voltage of 1.75 V at 10 m A cm^(-2).By connecting Si photovoltaics with the modular electrochemical device,a well-matched solar driven system was built to convert the intermittent solar energy into hydrogen and electric energy with a solar to hydrogen-electricity efficiency of 16.7%,demonstrating the flexible storage and conversion of renewables.
文摘Electricity theft significantly impacts the reliability and sustainability of electricity services,particularly in developing regions.However,the socio-economic,infrastructural,and institutional drivers of theft remain inadequately explored.Here we examine electricity theft in Lubumbashi,Democratic Republic of Congo,focusing on its patterns,causes,and impacts on service quality.Theft rates exceeded 75%in peripheral municipalities like Katuba and Kampemba,driven by poverty,weak law enforcement,and poor infrastructure dominated by above-ground networks.In contrast,central areas like Kamalondo and Lubumbashi reported lower theft rates due to better urban planning and underground systems.We found that electricity theft directly correlates with frequent voltage fluctuations,prolonged outages,and grid overloads.Socio-economic factors,including high connection fees and poverty,emerged as primary drivers,while institutional weaknesses such as corruption and ineffective enforcement perpetuate theft.Addressing theft requires a holistic approach integrating infrastructure modernization,socio-economic reforms,and institutional strengthening.Transitioning to underground networks,providing affordable electricity access,and adopting advanced metering systems are crucial.Overall,this study highlights the systemic nature of electricity theft and provides actionable insights for improving electricity service delivery and equity in urban settings.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金support by the Science and Technology Project of Guangdong Power Exchange Center Co.,Ltd.(No.GDKJXM20222599)National Natural Science Foundation of China(No.52207104)Natural Science Foundation of Guangdong Province(No.2024A1515010426).
文摘The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.
基金Cloud Course of Beijing University of Civil Engineering and Architecture at Super Star Learning(YC240109)。
文摘The present teaching content of the power electronics course is insufficient to cover the power electronics technology used in building electrical engineering.This paper analyzes the relationship between building electrical engineering and power electronics technology,investigates the main power electronics technology used in building electrical engineering,introduces the teaching content of current power electronics course,analyzes the insufficiency of current teaching content related to the practice of electrical engineering,and proposes the principles and directions for the reformation and innovation of the teaching content of the course of power electronics for the major of building electricity and intelligence.
基金partially supported by projects funded by the National Key R&D Program of China(2022YFB2403000)the State Grid Corporation of China Science and Technology Project(522722230034).
文摘Accurate forecasting of electricity spot prices is crucial for market participants in formulating bidding strategies.However,the extreme volatility of electricity spot prices,influenced by various factors,poses significant challenges for forecasting.To address the data uncertainty of electricity prices and effectively mitigate gradient issues,overfitting,and computational challenges associated with using a single model during forecasting,this paper proposes a framework for forecasting spot market electricity prices by integrating wavelet packet decomposition(WPD)with a hybrid deep neural network.By ensuring accurate data decomposition,the WPD algorithm aids in detecting fluctuating patterns and isolating random noise.The hybrid model integrates temporal convolutional networks(TCN)and long short-term memory(LSTM)networks to enhance feature extraction and improve forecasting performance.Compared to other techniques,it significantly reduces average errors,decreasing mean absolute error(MAE)by 27.3%,root mean square error(RMSE)by 66.9%,and mean absolute percentage error(MAPE)by 22.8%.This framework effectively captures the intricate fluctuations present in the time series,resulting in more accurate and reliable predictions.
基金supported by the National Key R&D Project from Ministry of Science and Technology,China(2021YFA1201603)National Natural Science Foundation of China(52073032 and 52192611)the Fundamental Research Funds for the Central Universities.
文摘Triboelectric nanogenerators(TENGs)offer a selfsustaining power solution for marine regions abundant in resources but constrained by energy availability.Since their pioneering use in wave energy harvesting in 2014,nearly a decade of advancements has yielded nearly thousands of research articles in this domain.Researchers have developed various TENG device structures with diverse functionalities to facilitate their commercial deployment.Nonetheless,there is a gap in comprehensive summaries and performance evaluations of TENG structural designs.This paper delineates six innovative structural designs,focusing on enhancing internal device output and adapting to external environments:high space utilization,hybrid generator,mechanical gain,broadband response,multi-directional operation,and hybrid energy-harvesting systems.We summarize the prevailing trends in device structure design identified by the research community.Furthermore,we conduct a meticulous comparison of the electrical performance of these devices under motorized,simulated wave,and real marine conditions,while also assessing their sustainability in terms of device durability and mechanical robustness.In conclusion,the paper outlines future research avenues and discusses the obstacles encountered in the TENG field.This review aims to offer valuable perspectives for ongoing research and to advance the progress and application of TENG technology.
基金the Fundacao de Amparo à Pesquisa do Estado de Sao Paulo(FAPESP,2022/02235-4,2017/11958-1,2017/11986-5,2014/02163-7)Fundacao de Apoio da UFMG(FUNDEP,27192-36,01-P-38465/2023)Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq,405675/2022-4,56405643/2022-5,302180/2022-2,306870/2021-5)。
文摘Metal-Supported Solid Oxide Fuel Cells(MS-SOFCs)hold significant potential for driving the energy transition.These electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell(SOFCs)and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells(PEMFCs)due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication processes.This review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future opportunities.Techniques such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged activity.These concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries(TPBs).This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available fuels.Lastly,the End-of-Life(EoL),recycling,and life-cycle assessments(LCAs)of the Fuel Cell Hybrid Electric Vehicles(FCHEVs)were discussed.LCAs comparing Fuel Cell Electric Vehicles(FCEVs)equipped with(PEMFCs)and FCHEVs equipped with MS-SOFCs,both powered with hydrogen(H_(2))generated by different routes were compared.This review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.
基金supported by the National Natural Science Foundation of China(No.52207104)China Postdoctoral Science Foundation(No.2022M711202).
文摘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.
基金This research was funded by the National Nature Sciences Foundation of China(Grant No.42250410321).
文摘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.
基金supported by the Science and Technology Project of State Grid Jiangxi Electric Power Corporation Limited‘Research on Key Technologies for Non-Intrusive Load Identification for Typical Power Industry Users in Jiangxi Province’(521852220004)。
文摘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.
基金supported by the National Natural Science Foundation of China(22308210)the Scientific Research Program Funded by Shaanxi Provincial Education Department(23JK0350)+3 种基金the Open Foundation of Key Laboratory of Auxiliary Chemistry and Technology for Chemical Industry,Ministry of Education,and Shaanxi Collaborative Innovation Center of Industrial Auxiliary Chemistry and Technology,Shaanxi University of Science and Technology(KFKT2021-12)the Opening Project of Key Laboratory of Leather Chemistry and Engineering(Sichuan University),Ministry of Education(2022)the RIKEN-MOST Project between the Ministry of Science and Technology of the People's Republic of China(MOST)and RIKEN,the China Scholarship Council(202108610127)the Natural Science Foundation of Shaanxi University of Science&Technology(2019BT-44).
文摘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.
基金National Key R&D Program of China(Grant No.2022YFB2703500)National Natural Science Foundation of China(Grant No.52277104)+2 种基金National Key R&D Program of Yunnan Province(202303AC100003)Applied Basic Research Foundation of Yunnan Province (202301AT070455, 202101AT070080)Revitalizing Talent Support Program of Yunnan Province (KKRD202204024).
文摘Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets.This study considers multiple neighboring countries in the form of alliances,introduces neighboring countries’exchange rates into the cross-border multi-agent power-trading market and proposes a method to study each agent’s dynamic decision-making behavior based on evolutionary game theory.To this end,this study uses three national agents as examples,constructs a tripartite evolutionary game model,and analyzes the evolution process of the decision-making behavior of each agent member state under the initial willingness value,cost of payment,and additional revenue of the alliance.This research helps realize cross-border energy operations so that the transaction agent can achieve greater trade profits and provides a theoretical basis for cooperation and stability between multiple agents.
基金partially supported by the National Natural Science Foundation of China(Nos.12172334 and 12274110)the Zhejiang Provincial Natural Science Foundation of China(No.LR21A020001)
文摘The phase transition of water molecules in nanochannels under varying external electric fields is studied by molecular dynamics simulations.It is found that the phase transition of water molecules in nanochannels occurs by changing the frequency of the varying electric field.Water molecules maintain the ice phase when the frequency of the varying electric field is less than 16 THz or greater than 30 THz,and they completely melt when the frequency of the varying electric field is 24 THz.This phenomenon is attributed to the breaking of hydrogen bonds when the frequency of the varying electric field is close to their inherent resonant frequency.Moreover,the study demonstrates that the critical frequency varies with the confinement situation.The new mechanism of regulating the phase transition of water molecules in nanochannels revealed in this study provides a perspective for further understanding of the phase transition of water molecules in nanochannels,and has great application potential in preventing icing and deicing.
基金This work was supported by the National Key Research and Development Program of China(2022YFB4101600,2022YFB4101605)the National Natural Science Foundation of China(52372175,51972040)+1 种基金the Innovation and Technology Fund of Dalian(N2023JJ12GX020,2022JJ12GX023)Liaoning Normal University 2022 Outstanding Research Achievements Cultivation Fund(No.22GDL002).The authors also acknowledge the assistance of the DUT Instrumental Analysis Center.
文摘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.
基金the National Fund Project No.62172337National Natural Science Foundation of China(No.61662069)China Postdoctoral Science Foundation(No.2017M610817).
文摘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.
基金State Grid Jibei Electric Power Company Limited(no.SGJBJY00GPJS2310051)Natural Science Foundation of Beijing Municipality(no.9242015).
文摘Because of the contradiction between the scale of new energy installations and the continuous load growth in the central and eastern regions of China,the balance problems of the electricity market are becoming increasingly prominent,and it is urgent to solve such problems through inter-provincial electricity spot markets.First,the development history and construction status of the inter-provincial electricity spot market are summarized;second,the mechanism design of the inter-provincial electricity spot market is sorted out in terms of the market operation framework,transaction declaration,and clearing methods;subsequently,the evaluation index system of the inter-provincial electricity spot market is constructed,including four themes of electricity mutual aid and support,new energy consumption,economic benefits of market-based allocation,and social benefits of market-based allocation;finally,the operation of the inter-provincial electricity spot market is comprehensively analyzed by the algorithm based on the market operation data of 2022,which proves the feasibility and practicality of the proposed index system.
文摘The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.
