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 increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack...The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.展开更多
Sustainable water,energy and food(WEF)supplies are the bedrock upon which human society depends.Solar-driven interfacial evaporation,combined with electricity generation and cultivation,is a promising approach to miti...Sustainable water,energy and food(WEF)supplies are the bedrock upon which human society depends.Solar-driven interfacial evaporation,combined with electricity generation and cultivation,is a promising approach to mitigate the freshwater,energy and food crises.However,the performance of solar-driven systems decreases significantly during operation due to uncontrollable weather.This study proposes an integrated water/electricity cogeneration-cultivation system with superior thermal management.The energy storage evaporator,consisting of energy storage microcapsules/hydrogel composites,is optimally designed for sustainable desalination,achieving an evaporation rate of around 1.91 kg m^(-2)h^(-1).In the dark,heat released from the phase-change layer supported an evaporation rate of around 0.54kg m^(-2)h^(-1).Reverse electrodialysis harnessed the salinity-gradient energy enhanced during desalination,enabling the long-running WEC system to achieve a power output of~0.3 W m^(-2),which was almost three times higher than that of conventional seawater/surface water mixing.Additionally,an integrated crop irrigation platform utilized system drainage for real-time,on-demand wheat cultivation without secondary contaminants,facilitating seamless WEF integration.This work presents a novel approach to all-day solar water production,electricity generation and crop irrigation,offering a solution and blueprint for the sustainable development of WEF.展开更多
提出一种改进的ELECTRE(elimination et choix traduisant la réalité)动态模糊多属性决策方法。首先,对动态决策矩阵运用熵权法获得客观的时间权重;其次,提出改进的ELECTRE方法,针对直觉模糊数不可直接比较的问题,根据隶属...提出一种改进的ELECTRE(elimination et choix traduisant la réalité)动态模糊多属性决策方法。首先,对动态决策矩阵运用熵权法获得客观的时间权重;其次,提出改进的ELECTRE方法,针对直觉模糊数不可直接比较的问题,根据隶属度、非隶属度值不同的物理含义,分别构造其级别优先的一致性和矛盾性动态指标函数,再融合为各属性的一致性和矛盾性动态指标;随后,根据其相反的赋值意义,利用时间权重分别进行集成,获得综合各时间段的一致性和矛盾性指标并进行耦合,从而得到各方案的赋值级别优先关系,最终完成方案排序。实验数据验证了方法的有效性与可行性。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
Forecasting energy demand is essential for optimizing energy generation and effectively predicting power system needs.Recently,many researchers have developed various models on tabular datasets to enhance the effectiv...Forecasting energy demand is essential for optimizing energy generation and effectively predicting power system needs.Recently,many researchers have developed various models on tabular datasets to enhance the effectiveness of demand prediction,including neural networks,machine learning,deep learning,and advanced architectures such as CNN and LSTM.However,research on the CNN models has struggled to provide reliable outcomes due to insufficient dataset sizes,repeated investigations,and inappropriate baseline selection.To address these challenges,we propose a Tabular data-based Lightweight Convolutional Neural Network(TLCNN)model for predicting energy demand.It frames the problem as a regression task that effectively captures complex data trends for accurate forecasting.The BanE-16 dataset is preprocessed using normalization techniques for categorical and numerical data before training the model.The proposed approach dynamically selects relevant features through a two-dimensional convolutional structure that improves adaptability.The model’s performance is evaluated using MSE,MAE,and Accuracy metrics.Experimental results show that TLCNN achieves a 10.89%lower MSE than traditional ML algorithms,demonstrating superior predictive capability.Additionally,TLCNN’s lightweight structure enhances generalization while reducing computational costs,making it suitable for real-world energy forecasting tasks.This study contributes to energy informatics by introducing an optimized deep-learning framework that improves demand prediction by ensuring robustness and adaptability for tabular data.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
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
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
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
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
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 dual system capable of solar-driven interfacial steam production and all-weather hydropower generation is emerging as a potential way to alleviate freshwater shortage and energy crisis.However,the intrinsic mechan...The dual system capable of solar-driven interfacial steam production and all-weather hydropower generation is emerging as a potential way to alleviate freshwater shortage and energy crisis.However,the intrinsic mechanism of hydroelectricity generation powered by the interaction between seawater and material structure is vague,and it remains challenging to develop dual-functional evaporators with high photothermal conversion efficiency and ionic selectivity.Herein,an all-weather dual-function evaporator based on porous carbon fiber-like(PCF)is acquired through the pyrolysis of barium-based metal-organic framework(Ba-BTEC),which is originated from waste polyimide.The PCF-based evaporator/device exhibits a high steam generation rate of 2.93 kg m^(-2)h^(-1)in seawater under 1 kW m^(-2)irradiation,along with the notable opencircuit voltage of 0.32 V,owing to the good light absorption ability,optimal wettability,and suitable aperture size.Moreover,molecular dynamics simulation result reveals that Na+tends to migrate rapidly within the nanoporous channels of PCF,owing to a strong affinity between oxygen-containing functional group and water molecules.This work not only proposes an eco-friendly strategy for constructing low-cost fulltime freshwater-hydroelectric co-generation device,but also contributes to the understanding of evaporation-driven energy harvesting technology.展开更多
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.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
The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate fo...The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.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
To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobje...To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter per...The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter persistent dendrite growth and parasitic reactions,driven by the inhomogeneous charge distribution and water-dominated environment within the EDL.Compounding this,classical EDL theory,rooted in meanfield approximations,further fails to resolve molecular-scale interfacial dynamics under battery-operating conditions,limiting mechanistic insights.Herein,we established a multiscale theoretical calculation framework from single molecular characteristics to interfacial ion distribution,revealing the EDL’s structure and interactions between different ions and molecules,which helps us understand the parasitic processes in depth.Simulations demonstrate that water dipole and sulfate ion adsorption at the inner Helmholtz plane drives severe hydrogen evolution and by-product formation.Guided by these insights,we engineered a“water-poor and anion-expelled”EDL using 4,1’,6’-trichlorogalactosucrose(TGS)as an electrolyte additive.As a result,Zn||Zn symmetric cells with TGS exhibited stable cycling for over 4700 h under a current density of 1 mA cm^(−2),while NaV_(3)O_(8)·1.5H_(2)O-based full cells kept 90.4%of the initial specific capacity after 800 cycles at 5 A g^(−1).This work highlights the power of multiscale theoretical frameworks to unravel EDL complexities and guide high-performance ARZB design through integrated theory-experiment approaches.展开更多
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funded by the Science and Technology Project of State Grid Corporation of China(5108-202355437A-3-2-ZN).
文摘The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.
基金supported by the National Natural Science Foundation of China(No.52070057)China Postdoctoral Science Foundation(No.2023M730855)Heilongjiang Postdoctoral Fund(No.LBH-Z22183)for financial support。
文摘Sustainable water,energy and food(WEF)supplies are the bedrock upon which human society depends.Solar-driven interfacial evaporation,combined with electricity generation and cultivation,is a promising approach to mitigate the freshwater,energy and food crises.However,the performance of solar-driven systems decreases significantly during operation due to uncontrollable weather.This study proposes an integrated water/electricity cogeneration-cultivation system with superior thermal management.The energy storage evaporator,consisting of energy storage microcapsules/hydrogel composites,is optimally designed for sustainable desalination,achieving an evaporation rate of around 1.91 kg m^(-2)h^(-1).In the dark,heat released from the phase-change layer supported an evaporation rate of around 0.54kg m^(-2)h^(-1).Reverse electrodialysis harnessed the salinity-gradient energy enhanced during desalination,enabling the long-running WEC system to achieve a power output of~0.3 W m^(-2),which was almost three times higher than that of conventional seawater/surface water mixing.Additionally,an integrated crop irrigation platform utilized system drainage for real-time,on-demand wheat cultivation without secondary contaminants,facilitating seamless WEF integration.This work presents a novel approach to all-day solar water production,electricity generation and crop irrigation,offering a solution and blueprint for the sustainable development of WEF.
文摘提出一种改进的ELECTRE(elimination et choix traduisant la réalité)动态模糊多属性决策方法。首先,对动态决策矩阵运用熵权法获得客观的时间权重;其次,提出改进的ELECTRE方法,针对直觉模糊数不可直接比较的问题,根据隶属度、非隶属度值不同的物理含义,分别构造其级别优先的一致性和矛盾性动态指标函数,再融合为各属性的一致性和矛盾性动态指标;随后,根据其相反的赋值意义,利用时间权重分别进行集成,获得综合各时间段的一致性和矛盾性指标并进行耦合,从而得到各方案的赋值级别优先关系,最终完成方案排序。实验数据验证了方法的有效性与可行性。
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.
文摘Forecasting energy demand is essential for optimizing energy generation and effectively predicting power system needs.Recently,many researchers have developed various models on tabular datasets to enhance the effectiveness of demand prediction,including neural networks,machine learning,deep learning,and advanced architectures such as CNN and LSTM.However,research on the CNN models has struggled to provide reliable outcomes due to insufficient dataset sizes,repeated investigations,and inappropriate baseline selection.To address these challenges,we propose a Tabular data-based Lightweight Convolutional Neural Network(TLCNN)model for predicting energy demand.It frames the problem as a regression task that effectively captures complex data trends for accurate forecasting.The BanE-16 dataset is preprocessed using normalization techniques for categorical and numerical data before training the model.The proposed approach dynamically selects relevant features through a two-dimensional convolutional structure that improves adaptability.The model’s performance is evaluated using MSE,MAE,and Accuracy metrics.Experimental results show that TLCNN achieves a 10.89%lower MSE than traditional ML algorithms,demonstrating superior predictive capability.Additionally,TLCNN’s lightweight structure enhances generalization while reducing computational costs,making it suitable for real-world energy forecasting tasks.This study contributes to energy informatics by introducing an optimized deep-learning framework that improves demand prediction by ensuring robustness and adaptability for tabular data.
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.
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].
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.
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.
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supported by National Natural Science Foundation of China(No.52373099)the Innovation and Talent Recruitment Base of New Energy Chemistry and Device(No.B21003)。
文摘The dual system capable of solar-driven interfacial steam production and all-weather hydropower generation is emerging as a potential way to alleviate freshwater shortage and energy crisis.However,the intrinsic mechanism of hydroelectricity generation powered by the interaction between seawater and material structure is vague,and it remains challenging to develop dual-functional evaporators with high photothermal conversion efficiency and ionic selectivity.Herein,an all-weather dual-function evaporator based on porous carbon fiber-like(PCF)is acquired through the pyrolysis of barium-based metal-organic framework(Ba-BTEC),which is originated from waste polyimide.The PCF-based evaporator/device exhibits a high steam generation rate of 2.93 kg m^(-2)h^(-1)in seawater under 1 kW m^(-2)irradiation,along with the notable opencircuit voltage of 0.32 V,owing to the good light absorption ability,optimal wettability,and suitable aperture size.Moreover,molecular dynamics simulation result reveals that Na+tends to migrate rapidly within the nanoporous channels of PCF,owing to a strong affinity between oxygen-containing functional group and water molecules.This work not only proposes an eco-friendly strategy for constructing low-cost fulltime freshwater-hydroelectric co-generation device,but also contributes to the understanding of evaporation-driven energy harvesting technology.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
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funded under research grant from the Research,Development,andInnovation Authority(RDIA),Saudi Arabia,grant No.13010-Tabuk-2023-UT-R-3-1-SE.
文摘The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.
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 State Grid Corporation of China Science and Technology Project:Research on Key Technologies for Intelligent Carbon Metrology in Vehicle-to-Grid Interaction(Project Number:B3018524000Q).
文摘To achieve low-carbon regulation of electric vehicle(EV)charging loads under the“dual carbon”goals,this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multiobjective optimization.First,a dual-convolution enhanced improved Crossformer prediction model is constructed,which employs parallel 1×1 global and 3×3 local convolutionmodules(Integrated Convolution Block,ICB)formultiscale feature extraction,combinedwith anAdaptive Spectral Block(ASB)to enhance time-series fluctuationmodeling.Based on high-precision predictions,a carbon-electricity cost joint optimization model is further designed to balance economic,environmental,and grid-friendly objectives.The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid.Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models,achieving a 15.8%reduction in carbon emissions and a 5.2%reduction in economic costs,while still providing a substantial 22.2%reduction in the peak-valley difference.Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control(MPC)benchmark,highlighting the advantage of a global optimization approach.This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金supported by the National Natural Science Foundation of China(52471240)the Natural Science Foundation of Zhejiang Province(LZ23B030003)+2 种基金the Fundamental Research Funds for the Central Universities(226-2024-00075)support from the Engineering and Physical Sciences Research Council(EPSRC,UK)RiR grant-RIR18221018-1EU COST CA23155。
文摘The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter persistent dendrite growth and parasitic reactions,driven by the inhomogeneous charge distribution and water-dominated environment within the EDL.Compounding this,classical EDL theory,rooted in meanfield approximations,further fails to resolve molecular-scale interfacial dynamics under battery-operating conditions,limiting mechanistic insights.Herein,we established a multiscale theoretical calculation framework from single molecular characteristics to interfacial ion distribution,revealing the EDL’s structure and interactions between different ions and molecules,which helps us understand the parasitic processes in depth.Simulations demonstrate that water dipole and sulfate ion adsorption at the inner Helmholtz plane drives severe hydrogen evolution and by-product formation.Guided by these insights,we engineered a“water-poor and anion-expelled”EDL using 4,1’,6’-trichlorogalactosucrose(TGS)as an electrolyte additive.As a result,Zn||Zn symmetric cells with TGS exhibited stable cycling for over 4700 h under a current density of 1 mA cm^(−2),while NaV_(3)O_(8)·1.5H_(2)O-based full cells kept 90.4%of the initial specific capacity after 800 cycles at 5 A g^(−1).This work highlights the power of multiscale theoretical frameworks to unravel EDL complexities and guide high-performance ARZB design through integrated theory-experiment approaches.
