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
The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt ...The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.展开更多
Simultaneous uranium recovery,organic pollutant degradation,and electricity generation were achieved by employing a self-driven photoelectrochemical(PEC)system equipped with a modified carbon felt(MCF)cathode for the ...Simultaneous uranium recovery,organic pollutant degradation,and electricity generation were achieved by employing a self-driven photoelectrochemical(PEC)system equipped with a modified carbon felt(MCF)cathode for the treatment of complex radioactive wastewater.The MCF cathode was synthesized via a facile hydrothermal method,which modified the surface functional groups on carbon felt(CF)with enhanced active site availability and facilitated interfacial charge transfer,thus improving its UO_(2)^(2+)adsorption and reduction capacities.The self-driven PEC system with the MCF cathode demonstrated remarkable removal efficiencies and rate constants(k)for UO_(2)^(2+)(98.8%and 0.111 min^(−1))and chlortetracycline hydrochloride(CTC)(92.9%and 0.028 min^(−1))within 40 min and 90 min,respectively,coupled with an excellent power output of 1.41 mW/cm^(2).Additionally,the system with the MCF cathode exhibited superior removal performance for UO_(2)^(2+)and CTC in treating model complex wastewater under wide conditions.Even under natural sunlight,the system achieved over 80%removal efficiency for both UO_(2)^(2+)and CTC.Moreover,the uranium immobilized on the MCF cathode was mainly reduced to U(Ⅳ)species(90.51%),and performance remained robust over ten operational cycles.The cathode surface modification strategy and its application in the system provide a cost-effective,multi-functional and high-efficiency approach to controlling nuclides and organic pollutants in complex radioactive wastewater.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
Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat...Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.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
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
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
Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited ...Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited by the need for wound exposure and by increased pain.Our preliminary study demonstrated the innovative application of remote NMES(rNMES)to the skeletal muscle of the distal calf,which showed the potential to accelerate wound healing in remote areas.rNMES was effective in human clinical trials in our previous work,although the underlying mechanisms remain unclear.As rNMES is often used to stimulate muscle contraction in long-term bedridden patients,we analyzed data from the Gene Expression Omnibus(GEO)database and found that exercise promotes midkine(MDK)expression in muscle.MDK is a small secreted heparin-binding protein that interacts with multiple cell surface receptors to promote growth.In the present study,we found that MDK significantly enhanced macrophage efferocytosis in a low-density lipoprotein receptor-related protein 1(LRP1)-dependent manner.Our findings demonstrate that rNMES upregulates MDK expression in skeletal muscles through the AMPK-ERK axis,facilitating its delivery to wounds through the circulatory system and promoting LRP1-mediated efferocytosis of apoptotic cells,thereby expediting wound healing.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 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 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
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 NARI Relays Electric Co.,Ltd.under the Project“Research on Evaluation of Clearing Results and Switching Criteria for Primary-Backup Systems in Electricity SpotMarkets”(Project No.CGSQ240800443).
文摘The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.
基金supported by the National Natural Science Foundation of China(Nos.52170083 and 12305352)the Science and Technology Innovation Program of Hunan Province(No.2022RC1125).
文摘Simultaneous uranium recovery,organic pollutant degradation,and electricity generation were achieved by employing a self-driven photoelectrochemical(PEC)system equipped with a modified carbon felt(MCF)cathode for the treatment of complex radioactive wastewater.The MCF cathode was synthesized via a facile hydrothermal method,which modified the surface functional groups on carbon felt(CF)with enhanced active site availability and facilitated interfacial charge transfer,thus improving its UO_(2)^(2+)adsorption and reduction capacities.The self-driven PEC system with the MCF cathode demonstrated remarkable removal efficiencies and rate constants(k)for UO_(2)^(2+)(98.8%and 0.111 min^(−1))and chlortetracycline hydrochloride(CTC)(92.9%and 0.028 min^(−1))within 40 min and 90 min,respectively,coupled with an excellent power output of 1.41 mW/cm^(2).Additionally,the system with the MCF cathode exhibited superior removal performance for UO_(2)^(2+)and CTC in treating model complex wastewater under wide conditions.Even under natural sunlight,the system achieved over 80%removal efficiency for both UO_(2)^(2+)and CTC.Moreover,the uranium immobilized on the MCF cathode was mainly reduced to U(Ⅳ)species(90.51%),and performance remained robust over ten operational cycles.The cathode surface modification strategy and its application in the system provide a cost-effective,multi-functional and high-efficiency approach to controlling nuclides and organic pollutants in complex radioactive wastewater.
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supported in part by the National Natural Science Foundation of China(No.52407115)State Key Laboratory of Power System Operation and Control(61011000223).
文摘Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.
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.
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supported by the National Natural Science Foundation of China(Grant No.82271252 to W.L.,No.8217091029 to T.W.and No.82204542 to L.H.)the Key Medical Research Projects of Jiangsu Health and Health Commission(Grant No.K2023066 to L.Z.)the Taishan Industrial Talent Project(Grant No.2020-371722-73-03-097290 to W.L.).
文摘Neuromuscular electrical stimulation(NMES)is a well-established therapeutic approach for chronic wounds.Conventionally,NMES involves direct electrode contact with wounds or adjacent healthy skin;however,it is limited by the need for wound exposure and by increased pain.Our preliminary study demonstrated the innovative application of remote NMES(rNMES)to the skeletal muscle of the distal calf,which showed the potential to accelerate wound healing in remote areas.rNMES was effective in human clinical trials in our previous work,although the underlying mechanisms remain unclear.As rNMES is often used to stimulate muscle contraction in long-term bedridden patients,we analyzed data from the Gene Expression Omnibus(GEO)database and found that exercise promotes midkine(MDK)expression in muscle.MDK is a small secreted heparin-binding protein that interacts with multiple cell surface receptors to promote growth.In the present study,we found that MDK significantly enhanced macrophage efferocytosis in a low-density lipoprotein receptor-related protein 1(LRP1)-dependent manner.Our findings demonstrate that rNMES upregulates MDK expression in skeletal muscles through the AMPK-ERK axis,facilitating its delivery to wounds through the circulatory system and promoting LRP1-mediated efferocytosis of apoptotic cells,thereby expediting wound healing.
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 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.
