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
Combined heat and electricity operation with variable mass flow rates promotes flexibility,economy,and sustainability through synergies between electric power systems(EPSs)and district heating systems(DHSs).Such combi...Combined heat and electricity operation with variable mass flow rates promotes flexibility,economy,and sustainability through synergies between electric power systems(EPSs)and district heating systems(DHSs).Such combined operation presents a highly nonlinear and nonconvex optimization problem,mainly due to the bilinear terms in the heat flow model—that is,the product of the mass flow rate and the nodal temperature.Existing methods,such as nonlinear optimization,generalized Benders decomposition,and convex relaxation,still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency.To resolve this problem,we herein first reformulate the district heating network model through an equivalent transformation and variable substitution.The reformulated model has only one set of nonconvex constraints with reduced bilinear terms,and the remaining constraints are linear.Such a reformulation not only ensures optimality,but also accelerates the solving process.To relax the remaining bilinear constraints,we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model.To improve the quality of the McCormick relaxation,we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables.We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution.Case studies show that,compared with the interior point method and the method implemented in a global bilinear solver,the proposed tightening McCormick method quickly solves the heat–electricity operation problem with an acceptable feasibility check and optimality.展开更多
During the hot summer season,using electricity systems increases the local anthropogenic heat emission,further increasing the temperature.Regarding anthropogenic heat sources,electric energy consumption,heat generatio...During the hot summer season,using electricity systems increases the local anthropogenic heat emission,further increasing the temperature.Regarding anthropogenic heat sources,electric energy consumption,heat generation,indoor and outdoor heat transfer,and exchange in buildings play a critical role in the change in the urban thermal environment.Therefore,the Weather Research and Forecasting(WRF)Model was applied in this study to investigate the heat generation from an indoor electricity system and its influence on the outdoor thermal environment.Through the building effect parameterization(BEP)of a multistorey urban canopy scheme,a building energy model(BEM)to increase the influence of indoor air conditioning on the electricity consumption system was proposed.In other words,the BEP+BEM urban canopy parameterization scheme was set.High temperatures and a summer heat wave were simulated as the background weather.The results show that using the BEP+BEM parameterization scheme of indoor and outdoor energy exchange in the WRF model can better simulate the air temperature near the surface layer on a sunny summer.During the day,the turning on the air conditioning and other electrical systems have no obvious effect on the air temperature near the surface layer in the city,whereas at night,the air temperature generally increases by 0.6℃,especially in densely populated areas,with a maximum temperature rise of approximately 1.2℃from 22:00 to 23:00.When the indoor air conditioning target temperature is adjusted to 25-27℃,the total energy release of the air conditioning system is reduced by 12.66%,and the temperature drops the most from 13:00 to 16:00,with an average of approximately 1℃.Further,the denser the building is,the greater the temperature drop.展开更多
Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading fau...Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.展开更多
National Grid is the electricity system operator in Great Britain and has an unique feature in so far as it is one of the world’s few for-profit system operators. In addition, the commercially orientation of the Brit...National Grid is the electricity system operator in Great Britain and has an unique feature in so far as it is one of the world’s few for-profit system operators. In addition, the commercially orientation of the British market rules means that nearly every action taken by National Grid to operate the system has a cost associated to it. Based on those factors and in order to encourage National Grid to seek continuous improvements and drive for efficient and economic system operation, the regulator (Ofgem) offers an incentive scheme, whereby a target is agreed annually and any savings in relation to this target are shared between consumers and National Grid in the form of a profit. It is in National Grid’s best interest to have mechanisms to mitigate the impacts of volatility in the costs it faces as system operator so that it can implement cost saving actions without the risk of windfall losses (or gains) arising from sudden changes in uncontrollable drivers. The purpose of this paper is to share the experiences of National Grid in the operation of Great Britain's electricity system, with a special interest on the mechanisms created to manage the associated costs in response to the incentive scheme. It does so by describing the market operation in Great Britain and the costs drivers impacting National Grid’s system operation and illustrating the steps recently taken by National Grid to propose volatility mitigation mechanisms. It concludes with the rationale and expected results from the latest proposals as consulted with the industry for introduction in the incentive scheme starting on 1st April 2011. It is worth noting that with this work, the authors wish to both share the experience with other system operators and regulators in the world, as well as give British market participants an insight on the inner workings of National Grid.展开更多
This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with t...This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with the cases: opening the generator side and load side simultaneously. The problem of real-time congestion management is transformed to a nonlinear programming problem. While the transmission congestion is maximum, the adjustment cost is minimum based on the ant colony algorithm, and the global optimal solu-tion is obtained. Simulation results show that the improved optimal model can obviously reduce the adjust-ment cost and the designed algorithm is safe and easy to implement.展开更多
Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to p...Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.展开更多
Estimating CO2 emission factor of the electricity system is a key aspect in the calculation of the baseline emissions for projects certified as CDM （Clean Development Mechanism）, which replace energy from the grid. ...Estimating CO2 emission factor of the electricity system is a key aspect in the calculation of the baseline emissions for projects certified as CDM （Clean Development Mechanism）, which replace energy from the grid. Currently, Uruguay is driving the expansion of the electricity system based on domestic renewable energies, in addition to replacing oil-based fuels for others with lower emission factors. This implies a substantial change of the generation park in the next decade and of the associated CO2 emissions. In this paper, a calculation methodology of the baseline emissions is adapted for its incorporation in the software SimSEE （Electric Energy Systems Simulator）, which is used for modeling the Uruguayan electric system, and therefore, allows modeling the current energy generator park and the future one. Using this tool, the CO2 emission factor＇s evolution is evaluated in the 2012-2020 period. The 2020 scenario is based on an optimal expansion of the electric system. The results indicate a strong reduction of the emission factor between 2012 and 2020, going from average values （for 100 simulations） around 0.60 tCO2/MWh to 0.15 tCO2/MWh. In this possible future scenario, CDM certification will probably not act as a strong incentive in Uruguay for the development of projects based on non-traditional renewable energies.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
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
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
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
This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality:the electric vehicles(EVs)adoption and the renewable energy(RE)electricity generation.Focusing on a Renewable Ener...This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality:the electric vehicles(EVs)adoption and the renewable energy(RE)electricity generation.Focusing on a Renewable Energy-Dominated(RED)electricity system,the research examines the interdependence between these interventions and their collective impact on economic dispatch.The study's objective is to determine optimal economic dispatch strategies that meet hourly electricity demand,considering two distinct supply scenarios across eight supply options.The first scenario assesses the maximum possible supply,while the second contemplates the minimum possible supply from each option.Additionally,the study delves into the influence of social cost of emissions on these economic dispatches.Employing an experimental design,the study generates representative load curves that incorporate EV charging demands for varied levels of EV penetration,alongside regular electricity demand.Data from Karnataka's RED electricity system provides a basis for the supply-side analysis.The economic dispatch for each supply scenario is formulated as a Mixed Integer Linear Program(MILP),aiming to minimize both costs for generation and social costs of emissions,while adhering to operational constraints of the supply options.Key findings from this approach,highlight several critical insights:the significant role of incorporating social costs in economic dispatch decisions,the tangible impact of EV demand on supply shortages,and the importance of maintaining supply capacity to minimize these shortages.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
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
Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating c...Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating current(AC)(direct current(DC))voltage control.In fact,faster and more stable active and reactive power in the presence of frequency and voltage sags and swells is needed.Power electronics-controlled variable speed generators do not have enough energy storage(inertia)for the scope(static synchronous compensators(STATCOMs)included).This is because power electronics tends to decouple the generator from the power system.While virtual inertia control in doubly fed induction generators(DFIGs)offers a partial solution to these problems,a more robust and comprehensive framework is required for advanced grid support.This is how,by extending the dual-excitation principles,the dualaxis excited electric synchronous generators(DE-SG)provide superior flexibility in two variants summarized here:as a multifunctional DFIG and dual-axis vs.single-axis excited synchronous generator(SG),and as a synchronous condenser(SC),with dual DC and AC excitation(as a no-load DFIG with inertia wheel),where variable speed is used to accelerate/decelerate the SC and thus provide additional assistance in frequency stabilization.These solutions,good for short-time transients,are not meant,however,to replace the large bidirectional energy storage systems(pump-hydro,hydrogen,batteries,etc.)which are crucial for the daily inherent variations of output energy in modern power systems with multiple power sources.The present paper offers a summary of techniques used in the dual-axis excited vs.single-axis excited SGs(SE-SGs),and SCs topologies,modeling,and control for better stability in modern multiple-source energy systems.This survey includes multiple case studies to shed light on prominent methods.展开更多
The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always...The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always been the discussion focus to ensure spacecraft reliability.In this paper,a few-shot unsupervised fault diagnosis method based on the improved Newman community division algorithm is proposed,to approach the scarcity of fault data samples and the inconspicuous characteristics of abnormal data.Firstly,aiming to capture the overall relevance of the fault dataset,a complex network model is built by adopting the K-Dynamic time warping distance Adjacent Nodes(KDAN)method.Based on the complex network model,the Newman community divisions algorithm is improved by using the Quantum-behaved Particle Swarm Optimization(QPSO).Subsequently,in order to evaluate the feasibility of the proposed method,experimental validation was conducted using an open-source dataset.The results indicate that the average accuracy can reach 96.43% for fault data diagnosis,and an F1_score of 97.76%with only 17.65%of the dataset used for training.The proposed method can accurately classify abnormal data by identifying the community structure in the data network,significantly improve the efficiency of the community divisions algorithm and reduce its complexity,and provide a new solution for fault diagnosis in large-scale complex systems.展开更多
Each morning at Yangluo Port in Wuhan,Hubei Province,the all-electric cargo vessel Huahang Xinneng No.1 completes a battery swap in under 10 minutes before returning to service with nearly 8,000 kWh of power onboard。
The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one...The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.展开更多
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.
基金This work was supported by the Science and Technology Program of State Grid Corporation of China(522300190008).
文摘Combined heat and electricity operation with variable mass flow rates promotes flexibility,economy,and sustainability through synergies between electric power systems(EPSs)and district heating systems(DHSs).Such combined operation presents a highly nonlinear and nonconvex optimization problem,mainly due to the bilinear terms in the heat flow model—that is,the product of the mass flow rate and the nodal temperature.Existing methods,such as nonlinear optimization,generalized Benders decomposition,and convex relaxation,still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency.To resolve this problem,we herein first reformulate the district heating network model through an equivalent transformation and variable substitution.The reformulated model has only one set of nonconvex constraints with reduced bilinear terms,and the remaining constraints are linear.Such a reformulation not only ensures optimality,but also accelerates the solving process.To relax the remaining bilinear constraints,we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model.To improve the quality of the McCormick relaxation,we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables.We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution.Case studies show that,compared with the interior point method and the method implemented in a global bilinear solver,the proposed tightening McCormick method quickly solves the heat–electricity operation problem with an acceptable feasibility check and optimality.
基金supported by Incubation Project of State Grid Jiangsu Electric Power Company“Research and application of key technology of intelligent forecasting and warning for electric power meteorological public service platform”(JF2021045).
文摘During the hot summer season,using electricity systems increases the local anthropogenic heat emission,further increasing the temperature.Regarding anthropogenic heat sources,electric energy consumption,heat generation,indoor and outdoor heat transfer,and exchange in buildings play a critical role in the change in the urban thermal environment.Therefore,the Weather Research and Forecasting(WRF)Model was applied in this study to investigate the heat generation from an indoor electricity system and its influence on the outdoor thermal environment.Through the building effect parameterization(BEP)of a multistorey urban canopy scheme,a building energy model(BEM)to increase the influence of indoor air conditioning on the electricity consumption system was proposed.In other words,the BEP+BEM urban canopy parameterization scheme was set.High temperatures and a summer heat wave were simulated as the background weather.The results show that using the BEP+BEM parameterization scheme of indoor and outdoor energy exchange in the WRF model can better simulate the air temperature near the surface layer on a sunny summer.During the day,the turning on the air conditioning and other electrical systems have no obvious effect on the air temperature near the surface layer in the city,whereas at night,the air temperature generally increases by 0.6℃,especially in densely populated areas,with a maximum temperature rise of approximately 1.2℃from 22:00 to 23:00.When the indoor air conditioning target temperature is adjusted to 25-27℃,the total energy release of the air conditioning system is reduced by 12.66%,and the temperature drops the most from 13:00 to 16:00,with an average of approximately 1℃.Further,the denser the building is,the greater the temperature drop.
基金supported by Shanghai Rising-Star Program(No.22QA1403900)the National Natural Science Foundation of China(No.71804106)the Noncarbon Energy Conversion and Utilization Institute under the Shanghai Class IV Peak Disciplinary Development Program.
文摘Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.
文摘National Grid is the electricity system operator in Great Britain and has an unique feature in so far as it is one of the world’s few for-profit system operators. In addition, the commercially orientation of the British market rules means that nearly every action taken by National Grid to operate the system has a cost associated to it. Based on those factors and in order to encourage National Grid to seek continuous improvements and drive for efficient and economic system operation, the regulator (Ofgem) offers an incentive scheme, whereby a target is agreed annually and any savings in relation to this target are shared between consumers and National Grid in the form of a profit. It is in National Grid’s best interest to have mechanisms to mitigate the impacts of volatility in the costs it faces as system operator so that it can implement cost saving actions without the risk of windfall losses (or gains) arising from sudden changes in uncontrollable drivers. The purpose of this paper is to share the experiences of National Grid in the operation of Great Britain's electricity system, with a special interest on the mechanisms created to manage the associated costs in response to the incentive scheme. It does so by describing the market operation in Great Britain and the costs drivers impacting National Grid’s system operation and illustrating the steps recently taken by National Grid to propose volatility mitigation mechanisms. It concludes with the rationale and expected results from the latest proposals as consulted with the industry for introduction in the incentive scheme starting on 1st April 2011. It is worth noting that with this work, the authors wish to both share the experience with other system operators and regulators in the world, as well as give British market participants an insight on the inner workings of National Grid.
文摘This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with the cases: opening the generator side and load side simultaneously. The problem of real-time congestion management is transformed to a nonlinear programming problem. While the transmission congestion is maximum, the adjustment cost is minimum based on the ant colony algorithm, and the global optimal solu-tion is obtained. Simulation results show that the improved optimal model can obviously reduce the adjust-ment cost and the designed algorithm is safe and easy to implement.
文摘Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.
文摘Estimating CO2 emission factor of the electricity system is a key aspect in the calculation of the baseline emissions for projects certified as CDM （Clean Development Mechanism）, which replace energy from the grid. Currently, Uruguay is driving the expansion of the electricity system based on domestic renewable energies, in addition to replacing oil-based fuels for others with lower emission factors. This implies a substantial change of the generation park in the next decade and of the associated CO2 emissions. In this paper, a calculation methodology of the baseline emissions is adapted for its incorporation in the software SimSEE （Electric Energy Systems Simulator）, which is used for modeling the Uruguayan electric system, and therefore, allows modeling the current energy generator park and the future one. Using this tool, the CO2 emission factor＇s evolution is evaluated in the 2012-2020 period. The 2020 scenario is based on an optimal expansion of the electric system. The results indicate a strong reduction of the emission factor between 2012 and 2020, going from average values （for 100 simulations） around 0.60 tCO2/MWh to 0.15 tCO2/MWh. In this possible future scenario, CDM certification will probably not act as a strong incentive in Uruguay for the development of projects based on non-traditional renewable energies.
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 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(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This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality:the electric vehicles(EVs)adoption and the renewable energy(RE)electricity generation.Focusing on a Renewable Energy-Dominated(RED)electricity system,the research examines the interdependence between these interventions and their collective impact on economic dispatch.The study's objective is to determine optimal economic dispatch strategies that meet hourly electricity demand,considering two distinct supply scenarios across eight supply options.The first scenario assesses the maximum possible supply,while the second contemplates the minimum possible supply from each option.Additionally,the study delves into the influence of social cost of emissions on these economic dispatches.Employing an experimental design,the study generates representative load curves that incorporate EV charging demands for varied levels of EV penetration,alongside regular electricity demand.Data from Karnataka's RED electricity system provides a basis for the supply-side analysis.The economic dispatch for each supply scenario is formulated as a Mixed Integer Linear Program(MILP),aiming to minimize both costs for generation and social costs of emissions,while adhering to operational constraints of the supply options.Key findings from this approach,highlight several critical insights:the significant role of incorporating social costs in economic dispatch decisions,the tangible impact of EV demand on supply shortages,and the importance of maintaining supply capacity to minimize these shortages.
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.
文摘Modern/distributed electric energy systems,with ever larger penetration of renewable(photovoltaic,wind,wave,and hydro)energy sources and time-variable outputs,are in need of stronger/higher frequency and alternating current(AC)(direct current(DC))voltage control.In fact,faster and more stable active and reactive power in the presence of frequency and voltage sags and swells is needed.Power electronics-controlled variable speed generators do not have enough energy storage(inertia)for the scope(static synchronous compensators(STATCOMs)included).This is because power electronics tends to decouple the generator from the power system.While virtual inertia control in doubly fed induction generators(DFIGs)offers a partial solution to these problems,a more robust and comprehensive framework is required for advanced grid support.This is how,by extending the dual-excitation principles,the dualaxis excited electric synchronous generators(DE-SG)provide superior flexibility in two variants summarized here:as a multifunctional DFIG and dual-axis vs.single-axis excited synchronous generator(SG),and as a synchronous condenser(SC),with dual DC and AC excitation(as a no-load DFIG with inertia wheel),where variable speed is used to accelerate/decelerate the SC and thus provide additional assistance in frequency stabilization.These solutions,good for short-time transients,are not meant,however,to replace the large bidirectional energy storage systems(pump-hydro,hydrogen,batteries,etc.)which are crucial for the daily inherent variations of output energy in modern power systems with multiple power sources.The present paper offers a summary of techniques used in the dual-axis excited vs.single-axis excited SGs(SE-SGs),and SCs topologies,modeling,and control for better stability in modern multiple-source energy systems.This survey includes multiple case studies to shed light on prominent methods.
基金supported in part by the Natural Science Foundation of Shanghai,China(No.23ZR1432400)the Shanghai Pilot Program for Basic Research-Chinese Academy of Science(No.JCYJ-SHFY-2022-015).
文摘The Electrical Power System(EPS)is one of the spacecraft’s key subsystems,and its operational status directly affects the lifespan and performance of the entire spacecraft.The corresponding fault diagnosis has always been the discussion focus to ensure spacecraft reliability.In this paper,a few-shot unsupervised fault diagnosis method based on the improved Newman community division algorithm is proposed,to approach the scarcity of fault data samples and the inconspicuous characteristics of abnormal data.Firstly,aiming to capture the overall relevance of the fault dataset,a complex network model is built by adopting the K-Dynamic time warping distance Adjacent Nodes(KDAN)method.Based on the complex network model,the Newman community divisions algorithm is improved by using the Quantum-behaved Particle Swarm Optimization(QPSO).Subsequently,in order to evaluate the feasibility of the proposed method,experimental validation was conducted using an open-source dataset.The results indicate that the average accuracy can reach 96.43% for fault data diagnosis,and an F1_score of 97.76%with only 17.65%of the dataset used for training.The proposed method can accurately classify abnormal data by identifying the community structure in the data network,significantly improve the efficiency of the community divisions algorithm and reduce its complexity,and provide a new solution for fault diagnosis in large-scale complex systems.
文摘Each morning at Yangluo Port in Wuhan,Hubei Province,the all-electric cargo vessel Huahang Xinneng No.1 completes a battery swap in under 10 minutes before returning to service with nearly 8,000 kWh of power onboard。
基金supported by the National Natural Science Foundation of China under Grant 52022016China Postdoctoral Science Foundation under grant 2021M693711Fundamental Research Funds for the Central Universities under grant 2021CDJQY-037.
文摘The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.
