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
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
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
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
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
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
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
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
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
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
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
We theoretically investigate the electricity storage/generation in a reversible solid oxide cell stack. The system heat is for the first time tentatively stored in a phase-change metal when the stack is operated to ge...We theoretically investigate the electricity storage/generation in a reversible solid oxide cell stack. The system heat is for the first time tentatively stored in a phase-change metal when the stack is operated to generate electricity in a fuel cell mode and then reused to store electricity in an electrolysis mode. The state of charge （H2 frication in cathode） effectively enhances the open circuit voltages （OCVs） while the system gas pressure in electrodes also increases the OCVs. On the other hand, a higher system pressure facilitates the species diffusion in electrodes that therefore accordingly improve electrode polarizations. With the aid of recycled system heat, the roundtrip efficiency reaches as high as 92% for the repeated electricity storage and generation.展开更多
The rapid change in economic growth and human civilization has led to a dramatic increase in energy utilization and electricity demand. That faces nations with the challenge of maintaining cost-effective and clean pow...The rapid change in economic growth and human civilization has led to a dramatic increase in energy utilization and electricity demand. That faces nations with the challenge of maintaining cost-effective and clean power energy production. This paper aims to present Afghanistan electricity sector development and energy resources exploitation in a broad context. With the global growing interest in Afghanistan rehabilitation, this paper presents energy development trends in Afghanistan. Besides, the future outlook up to 2032 and the challenges that face electricity sector are highlighted. This study tries to emerge the historical development, current status, and future direction of Afghanistan electricity sector till 2032, in a precise collection.展开更多
This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period...This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period should satisfy the requirements of power industry restructuring.Therefore, it is necessary to set up an appropriate pricing mechanism and system including thelinks of sales price to network, transmission and distribution price (T&D price) and sales price.In the light of various factors influencing increase and decrease in price, a forecast of electricitytariff is given in the five years to come.[展开更多
Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban ...Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.展开更多
A membrane-less constructed wetland microbial fuel cell （CW-MFC） is constructed and operated under continuous flow with a hydraulic retention time （HRT） of 2 d. Fed with glucose, the CW-MFC generates a stable curr...A membrane-less constructed wetland microbial fuel cell （CW-MFC） is constructed and operated under continuous flow with a hydraulic retention time （HRT） of 2 d. Fed with glucose, the CW-MFC generates a stable current density of over 2 A/m3 with a resistor of 1 kΩ and has a chemical oxygen demand （COD） removal efficiency of more than 90% after the startup of 2 to 3 d. A series of systems with the electrode spacings of 10, 20, 30 and 40 cm are compared. It is found that the container with the electrode spacing of 20 cm gains the highest voltage of 560 mV, the highest power density of 0. 149 W/m 3, and the highest Coulombic efficiency of 0.313%. It also has the highest COD removal efficiency of 94. 9%. In addition, the dissolved oxygen （DO） concentrations are observed as the lowest level in the middle of all the CW-MFC reactors. The results show that the more COD is removed, the greater power is generated, and the relatively higher Coulombic efficiency will be achieved. The present study indicates that the CW-MFC process can be used as a cost-effective and environmentally friendly wastewater treatment with simultaneous power generation.展开更多
Based on the Ramsey theory-the foundation for the tiered electricity pricing mechanism, this article analyzes the effects of the tiered electricity pricing system on social justice, eff iciency and commodity prices, a...Based on the Ramsey theory-the foundation for the tiered electricity pricing mechanism, this article analyzes the effects of the tiered electricity pricing system on social justice, eff iciency and commodity prices, and concludes that the system would direct subsidies to flow into low income groups, and promote energy conservation and emissions reduction by restricting over-consumption of high income groups, without enormous effect on commodity prices. The key to designing the tiered system is how to estimate the amount of electricity use for each tier, so as to avoid excluding low income groups from the subsidy or including high income groups in the subsidy.展开更多
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.
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.
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.
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.
文摘We theoretically investigate the electricity storage/generation in a reversible solid oxide cell stack. The system heat is for the first time tentatively stored in a phase-change metal when the stack is operated to generate electricity in a fuel cell mode and then reused to store electricity in an electrolysis mode. The state of charge （H2 frication in cathode） effectively enhances the open circuit voltages （OCVs） while the system gas pressure in electrodes also increases the OCVs. On the other hand, a higher system pressure facilitates the species diffusion in electrodes that therefore accordingly improve electrode polarizations. With the aid of recycled system heat, the roundtrip efficiency reaches as high as 92% for the repeated electricity storage and generation.
文摘The rapid change in economic growth and human civilization has led to a dramatic increase in energy utilization and electricity demand. That faces nations with the challenge of maintaining cost-effective and clean power energy production. This paper aims to present Afghanistan electricity sector development and energy resources exploitation in a broad context. With the global growing interest in Afghanistan rehabilitation, this paper presents energy development trends in Afghanistan. Besides, the future outlook up to 2032 and the challenges that face electricity sector are highlighted. This study tries to emerge the historical development, current status, and future direction of Afghanistan electricity sector till 2032, in a precise collection.
文摘This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period should satisfy the requirements of power industry restructuring.Therefore, it is necessary to set up an appropriate pricing mechanism and system including thelinks of sales price to network, transmission and distribution price (T&D price) and sales price.In the light of various factors influencing increase and decrease in price, a forecast of electricitytariff is given in the five years to come.[
基金Project(NRF-20110030631) supported by the National Research Foundation of Korea Grant funded by the Korean Government
文摘Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.
基金The Fundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China (No.51109038)
文摘A membrane-less constructed wetland microbial fuel cell （CW-MFC） is constructed and operated under continuous flow with a hydraulic retention time （HRT） of 2 d. Fed with glucose, the CW-MFC generates a stable current density of over 2 A/m3 with a resistor of 1 kΩ and has a chemical oxygen demand （COD） removal efficiency of more than 90% after the startup of 2 to 3 d. A series of systems with the electrode spacings of 10, 20, 30 and 40 cm are compared. It is found that the container with the electrode spacing of 20 cm gains the highest voltage of 560 mV, the highest power density of 0. 149 W/m 3, and the highest Coulombic efficiency of 0.313%. It also has the highest COD removal efficiency of 94. 9%. In addition, the dissolved oxygen （DO） concentrations are observed as the lowest level in the middle of all the CW-MFC reactors. The results show that the more COD is removed, the greater power is generated, and the relatively higher Coulombic efficiency will be achieved. The present study indicates that the CW-MFC process can be used as a cost-effective and environmentally friendly wastewater treatment with simultaneous power generation.
文摘Based on the Ramsey theory-the foundation for the tiered electricity pricing mechanism, this article analyzes the effects of the tiered electricity pricing system on social justice, eff iciency and commodity prices, and concludes that the system would direct subsidies to flow into low income groups, and promote energy conservation and emissions reduction by restricting over-consumption of high income groups, without enormous effect on commodity prices. The key to designing the tiered system is how to estimate the amount of electricity use for each tier, so as to avoid excluding low income groups from the subsidy or including high income groups in the subsidy.
