The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide ele...The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide electricity users in carbon reduction and promote power industry low-carbon transformation.Fundamentally,calculating indirect carbon emissions involves allocating direct carbon emission data from the power source side,indicating that accurate indirect emission results rely on the precise measurement of power source emissions.However,existing research on indirect carbon emissions in large-scale power systems rarely accounts for variations in carbon emission characteristics under different operating conditions of power sources,such as rated/non-rated operating conditions and ramping up/down conditions,making it difficult to reflect source-side and load-side carbon emission information variation during providing ancillary services.Quadratic and exponential functions are proposed to characterize the energy consumption profiles of coal-fired and gas-fired power generation,respectively,to construct a refined carbon emission model for power sources.By leveraging the theory of power system carbon flow,we analyze how variable operating conditions of power sources impact indirect carbon emissions.Case studies demonstrate that changes in power source emissions under variable conditions have a significant effect on the indirect carbon emissions of power grids.展开更多
The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading ...The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot.展开更多
The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource a...The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.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。
To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hyb...To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.展开更多
New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed s...New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.展开更多
Space-Based Solar Power(SBSP) presents a promising solution for achieving carbon neutrality and Renewable Electricity 100%(RE100) goals by offering a stable and continuous energy supply. However, its commercialization...Space-Based Solar Power(SBSP) presents a promising solution for achieving carbon neutrality and Renewable Electricity 100%(RE100) goals by offering a stable and continuous energy supply. However, its commercialization faces significant obstacles due to the technical challenges of long-distance microwave Wireless Power Transmission(WPT) from geostationary orbit. Even ground-based kilometer-scale WPT experiments remain difficult because of limited testing infrastructure, high costs, and strict electromagnetic wave regulations. Since the 1975 NASA-Raytheon experiment, which successfully recovered 30 kW of power over 1.55 km, there has been little progress in extending the transmission distance or increasing the retrieved power. This study proposes a cost-effective methodology for conducting long-range WPT experiments in constrained environments by utilizing existing infrastructure. A deep space antenna operating at 2.08 GHz with an output power of 2.3 kW and a gain of 55.3 dBi was used as the transmitter. Two test configurations were implemented: a 1.81 km ground-to-air test using an aerostat to elevate the receiver and a 1.82 km ground-to-ground test using a ladder truck positioned on a plateau. The rectenna consists of a lightweight 3×3 patch antenna array(0.9 m × 0.9 m), accompanied by a steering device and LED indicators to verify power reception. The aerostat-based test achieved a power density of 154.6 mW/m2, which corresponds to approximately 6.2% of the theoretical maximum. The performance gap is primarily attributed to near-field interference, detuning of the patch antenna, rectifier mismatch, and alignment issues. These limitations are expected to be mitigated through improved patch antenna fabrication, a transition from GaN to GaAs rectifiers optimized for lower input power, and the implementation of an automated alignment system. With these enhancements, the recovered power is expected to improve by approximately four to five times. The results demonstrate a practical and scalable framework for long-range WPT experiments under constrained conditions and provide key insights for advancing SBSP technology.展开更多
The new energy power generation is becoming increasingly important in the power system.Such as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,...The new energy power generation is becoming increasingly important in the power system.Such as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,photovoltaic power generation is unstable and random,resulting in a low utilization rate and directly affecting the stability of the power grid.To solve this problem,this paper proposes a coordinated control strategy for a newenergy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit.Firstly,the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal,which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage system.Secondly,the fuzzy control algorithm is introduced to balance the power between energy storage.In this paper,the actual data is used for simulation,and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit,effectively avoiding problems such as overshoot and over-discharge,and can significantly improve the stability of the photovoltaic power generation systemand the stability of the Direct Current bus.It is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units.展开更多
In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm ...In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm co-design framework:the T-type three-level bidirectional converter(100 kHz switching frequency)based on silicon carbide(SiC)MOSFET is deeply integrated with fuzzy model predictive control(Fuzzy-MPC).At the hardware level,the switching trajectory and resonance suppression circuit(attenuation resonance peak 18 dB)are optimized,and the total loss is reduced by 23%compared with the traditional silicon-based IGBT.At the algorithm level,the adaptive parameter update mechanism and multi-objective rolling optimization are adopted,and the 5 ms level dynamic power allocation is realized by relying on edge computing.Experiments on 800 V DC microgrid(including 600 kW photovoltaic and 150 A·h energy storage)built based on MATLAB/Simulink hardware-in-the-loop(HIL)platform show that the system shortens the battery charging time from 42 to 28 min(the charging speed is increased by 33%).Through the 78%valley power utilization rate,the power purchase cost of high-priced power grids was significantly reduced,and the levelized electricity price decreased by 10.3%;Under the irradiation fluctuation,the renewable energy consumption rate increases by 10.1%,and the DC bus voltage fluctuation is stable within±10 V when the load step is±30%.The co-design provides an economically feasible and dynamically robust solution for the efficient integration of PV-ESG-EV in the smart grid.展开更多
The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbi...The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbine,developed and installed by China Three Gorges Corp.(CTG),is located in the Phase II Liuao offshore wind farm,more than 30 km off the coast of Fujian in waters deeper than 40 metres.The 20-mw unit successfully completed commissioning and started operation on 5 February,CTG announced.展开更多
In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along...In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.展开更多
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
To address the issues of high costs and low component utilization caused by the independent configuration of hybrid DC circuit breakers(HCBs)and DC power flow controllers(DCPFCs)at each port in existing DC distributio...To address the issues of high costs and low component utilization caused by the independent configuration of hybrid DC circuit breakers(HCBs)and DC power flow controllers(DCPFCs)at each port in existing DC distribution networks,this paper adopts a component sharing mechanism to propose a composite multi-port hybrid DC circuit breaker(CM-HCB)with DC power flow and fault current limitation abilities,as well as reduced component costs.The proposed CM-HCB topology enables the sharing of the main breaker branch(MB)and the energy dissipation branch,while the load commutation switches(LCSs)in the main branch are reused as power flow control components,enabling flexible regulation of power flow in multiple lines.Meanwhile,by reconstructing the current path during the fault process,the proposed CM-HCB can utilize the internal coupled inductor to limit the current rise rate at the initial stage of the fault,significantly reducing the requirement for breaking current.A detailed study on the topological structure,steady-state power flow regulation mechanism,transient fault isolation mechanism,control strategy and characteristic analysis of the proposed CM-HCB is presented.Then,a Matlab/Simulink-based meshed three-terminal DC grid simulation platform with the proposed CM-HCB is built.The results indicate that the proposed CM-HCB can not only achieve flexible power flow control during steady-state operation,but also obtain current rise limitation and fault isolation abilities under short-circuit fault conditions,verifying its correctness and effectiveness.Finally,a comparative economic analysis is conducted between the proposed CM-HCB and the other two existing solutions,confirming that its component sharing mechanism can significantly reduce the number of components,lower system costs,and improve component utilization.展开更多
Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationship...Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra,PSD,and random signal power spectra.The relationship was derived,validated through experiments and simulations,and subsequently applied to multi-objective optimization.Various optimization algorithms were compared to achieve optimal system power quality.The findings revealed that the relationships between power quality indices and PSD were influenced by variations in the order of the power spectral estimation model.An increase in the order of the AR model resulted in a 36%improvement in the number of optimal solutions.Regarding optimal solution distribution,NSGA-II demonstrated superior diversity,while MOEA/D exhibited better convergence.However,practical applications showed that while MOEA/D had higher convergence,NSGA-II produced superior optimal solutions,achieving the best power quality indices(THDi at 4.62%,d%at 3.51%,and cosφat 96%).These results suggest that the proposed method holds significant potential for optimizing power quality in practical applications.展开更多
Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency devia...Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.展开更多
Background:Low relative sit-to-stand(STS)power has emerged as a critical predictor of adverse health outcomes,such as frailty and disability,in older adults.However,its impact on falls,fractures,hospitalizations,and a...Background:Low relative sit-to-stand(STS)power has emerged as a critical predictor of adverse health outcomes,such as frailty and disability,in older adults.However,its impact on falls,fractures,hospitalizations,and all-cause mortality remains unclear.Therefore,this longitudinal study aimed to investigate the potential associations between low relative STS power and these adverse health outcomes in older adults.Methods:A total of 1876 older adults(aged≥65 years,56.4%women)were included from the Toledo Study for Healthy Aging.Relative STS power was assessed using the 30-s STS test and the Alcazar equation.Participants were categorized as having low relative STS power based on previously established cut-off points(2.53 W/kg for men and 2.01 W/kg for women).Falls and fractures(hip and all-type)within the previous year were recorded.Hospitalizations and all-cause mortality were obtained during a follow-up of 6.8±3.1 years(mean±SD;median=7.8 years;interquartile range:3.9-10.1 years)and 9.7±3.5 years(median=10.9 years;interquartile range:8.2-12.5 years),respectively.Generalized linear mixed models,binary logistic regression,and proportional hazards regression adjusted for age,educational level,and comorbidities were used.Results:In men,low relative STS power was significantly associated with an increased likelihood of history of falls(odds ratio(OR)=1.73,95%confidence interval(95%CI):1.08-2.75,p=0.022)and all-type fractures(OR=1.86,95%CI:1.21-2.84,p=0.004)in the previous year.In women,low relative STS power was associated with a higher probability of hip fractures within the previous year(OR=3.25,95%CI:1.07-9.86,p=0.038).Low relative STS power predicted hospitalizations in women(hazard ratio(HR)=1.29,95%CI:1.06-1.58,p=0.012)and longer hospital stays in both men(p=0.020)and women(p=0.033).Low relative STS power significantly increased all-cause mortality in both men(HR=1.57,95%CI:1.26-1.97,p<0.001)and women(HR=2.04,95%CI:1.51-2.74,p<0.001).Conclusion:Low relative STS power was associated with history of hip fractures in women,whereas in men it was associated with history of falls and all-type fractures.Low relative STS power predicted hospitalizations in women but not in men.In both men and women,low relative STS power was associated with longer hospital stays and increased risk of all-cause mortality.展开更多
Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven...Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.展开更多
基金supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd.(ZBKTM20232244)the Project of National Natural of Science Foundation of China(52477103).
文摘The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide electricity users in carbon reduction and promote power industry low-carbon transformation.Fundamentally,calculating indirect carbon emissions involves allocating direct carbon emission data from the power source side,indicating that accurate indirect emission results rely on the precise measurement of power source emissions.However,existing research on indirect carbon emissions in large-scale power systems rarely accounts for variations in carbon emission characteristics under different operating conditions of power sources,such as rated/non-rated operating conditions and ramping up/down conditions,making it difficult to reflect source-side and load-side carbon emission information variation during providing ancillary services.Quadratic and exponential functions are proposed to characterize the energy consumption profiles of coal-fired and gas-fired power generation,respectively,to construct a refined carbon emission model for power sources.By leveraging the theory of power system carbon flow,we analyze how variable operating conditions of power sources impact indirect carbon emissions.Case studies demonstrate that changes in power source emissions under variable conditions have a significant effect on the indirect carbon emissions of power grids.
文摘The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot.
基金supported by the Science and Technology Project of the State Grid Corporation of China“Research on Comprehensive Value Evaluation Method of Flexible Adjusting Resources under Carbon-electricity-certificate Market Coupling Environment”(No.5108-202455038A-1-1-ZN).
文摘The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
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 Science and Technology Project of the headquarters of the State Grid Corporation of China(No.5500-202324492A-3-2-ZN).
文摘To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.
基金supported by the science and technology project of State Grid Shanghai Municipal Electric Power Company(No.52094023003L).
文摘New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.
文摘Space-Based Solar Power(SBSP) presents a promising solution for achieving carbon neutrality and Renewable Electricity 100%(RE100) goals by offering a stable and continuous energy supply. However, its commercialization faces significant obstacles due to the technical challenges of long-distance microwave Wireless Power Transmission(WPT) from geostationary orbit. Even ground-based kilometer-scale WPT experiments remain difficult because of limited testing infrastructure, high costs, and strict electromagnetic wave regulations. Since the 1975 NASA-Raytheon experiment, which successfully recovered 30 kW of power over 1.55 km, there has been little progress in extending the transmission distance or increasing the retrieved power. This study proposes a cost-effective methodology for conducting long-range WPT experiments in constrained environments by utilizing existing infrastructure. A deep space antenna operating at 2.08 GHz with an output power of 2.3 kW and a gain of 55.3 dBi was used as the transmitter. Two test configurations were implemented: a 1.81 km ground-to-air test using an aerostat to elevate the receiver and a 1.82 km ground-to-ground test using a ladder truck positioned on a plateau. The rectenna consists of a lightweight 3×3 patch antenna array(0.9 m × 0.9 m), accompanied by a steering device and LED indicators to verify power reception. The aerostat-based test achieved a power density of 154.6 mW/m2, which corresponds to approximately 6.2% of the theoretical maximum. The performance gap is primarily attributed to near-field interference, detuning of the patch antenna, rectifier mismatch, and alignment issues. These limitations are expected to be mitigated through improved patch antenna fabrication, a transition from GaN to GaAs rectifiers optimized for lower input power, and the implementation of an automated alignment system. With these enhancements, the recovered power is expected to improve by approximately four to five times. The results demonstrate a practical and scalable framework for long-range WPT experiments under constrained conditions and provide key insights for advancing SBSP technology.
基金supported by the State Grid Corporation of China Science and Technology Project,grant number 52270723000900K.
文摘The new energy power generation is becoming increasingly important in the power system.Such as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,photovoltaic power generation is unstable and random,resulting in a low utilization rate and directly affecting the stability of the power grid.To solve this problem,this paper proposes a coordinated control strategy for a newenergy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit.Firstly,the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal,which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage system.Secondly,the fuzzy control algorithm is introduced to balance the power between energy storage.In this paper,the actual data is used for simulation,and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit,effectively avoiding problems such as overshoot and over-discharge,and can significantly improve the stability of the photovoltaic power generation systemand the stability of the Direct Current bus.It is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units.
基金Jiangsu Provincial College Student Innovation and Entrepreneurship Program(Grant No.SJCX25_2184)—“Multi-energy Complementary Optimization and Vehicle-Storage Bidirectional Interaction Technology Driven by Novel 5E Framework”(Principal Investigator:Yuan-Yuan ShiFunding Agency:Jiangsu Provincial Education Department)+3 种基金Huaian Natural Science Research Project(Grant No.HAB2024046)—“Optimal Control of Flexible Cold-Heat-Power Integrated System with Source-Grid-Load-Storage Coordination”(Principal Investigator:Jie JiFunding Agency:Huaian Science and Technology Bureau)Huaiyin Institute of TechnologyUniversity-funded Project(GrantNo.HGYK202511)—“Data-driven CooperativeOptimization Dispatch for Source-Grid-Load Systems”(Principal Investigator:Chu-Tong ZhangFunding Agency:Huaiyin Institute of Technology).
文摘In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm co-design framework:the T-type three-level bidirectional converter(100 kHz switching frequency)based on silicon carbide(SiC)MOSFET is deeply integrated with fuzzy model predictive control(Fuzzy-MPC).At the hardware level,the switching trajectory and resonance suppression circuit(attenuation resonance peak 18 dB)are optimized,and the total loss is reduced by 23%compared with the traditional silicon-based IGBT.At the algorithm level,the adaptive parameter update mechanism and multi-objective rolling optimization are adopted,and the 5 ms level dynamic power allocation is realized by relying on edge computing.Experiments on 800 V DC microgrid(including 600 kW photovoltaic and 150 A·h energy storage)built based on MATLAB/Simulink hardware-in-the-loop(HIL)platform show that the system shortens the battery charging time from 42 to 28 min(the charging speed is increased by 33%).Through the 78%valley power utilization rate,the power purchase cost of high-priced power grids was significantly reduced,and the levelized electricity price decreased by 10.3%;Under the irradiation fluctuation,the renewable energy consumption rate increases by 10.1%,and the DC bus voltage fluctuation is stable within±10 V when the load step is±30%.The co-design provides an economically feasible and dynamically robust solution for the efficient integration of PV-ESG-EV in the smart grid.
文摘The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbine,developed and installed by China Three Gorges Corp.(CTG),is located in the Phase II Liuao offshore wind farm,more than 30 km off the coast of Fujian in waters deeper than 40 metres.The 20-mw unit successfully completed commissioning and started operation on 5 February,CTG announced.
文摘In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金funded by Youth Talent Growth Project of Guizhou Provincial Department of Education(No.Qianjiaoji[2024]21)National Natural Science Foundation of China(No.62461008 and No.52507211)Guizhou Provincial Key Technology R&D Program(No.[2024]General 049).
文摘To address the issues of high costs and low component utilization caused by the independent configuration of hybrid DC circuit breakers(HCBs)and DC power flow controllers(DCPFCs)at each port in existing DC distribution networks,this paper adopts a component sharing mechanism to propose a composite multi-port hybrid DC circuit breaker(CM-HCB)with DC power flow and fault current limitation abilities,as well as reduced component costs.The proposed CM-HCB topology enables the sharing of the main breaker branch(MB)and the energy dissipation branch,while the load commutation switches(LCSs)in the main branch are reused as power flow control components,enabling flexible regulation of power flow in multiple lines.Meanwhile,by reconstructing the current path during the fault process,the proposed CM-HCB can utilize the internal coupled inductor to limit the current rise rate at the initial stage of the fault,significantly reducing the requirement for breaking current.A detailed study on the topological structure,steady-state power flow regulation mechanism,transient fault isolation mechanism,control strategy and characteristic analysis of the proposed CM-HCB is presented.Then,a Matlab/Simulink-based meshed three-terminal DC grid simulation platform with the proposed CM-HCB is built.The results indicate that the proposed CM-HCB can not only achieve flexible power flow control during steady-state operation,but also obtain current rise limitation and fault isolation abilities under short-circuit fault conditions,verifying its correctness and effectiveness.Finally,a comparative economic analysis is conducted between the proposed CM-HCB and the other two existing solutions,confirming that its component sharing mechanism can significantly reduce the number of components,lower system costs,and improve component utilization.
基金funded by the Inner Mongolia Nature Foundation Project,Project number:2023JQ04.
文摘Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra,PSD,and random signal power spectra.The relationship was derived,validated through experiments and simulations,and subsequently applied to multi-objective optimization.Various optimization algorithms were compared to achieve optimal system power quality.The findings revealed that the relationships between power quality indices and PSD were influenced by variations in the order of the power spectral estimation model.An increase in the order of the AR model resulted in a 36%improvement in the number of optimal solutions.Regarding optimal solution distribution,NSGA-II demonstrated superior diversity,while MOEA/D exhibited better convergence.However,practical applications showed that while MOEA/D had higher convergence,NSGA-II produced superior optimal solutions,achieving the best power quality indices(THDi at 4.62%,d%at 3.51%,and cosφat 96%).These results suggest that the proposed method holds significant potential for optimizing power quality in practical applications.
基金the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,for funding this research work through the project number“NBU-FFR-2025-3623-11”.
文摘Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.
基金supported by Centro de Investigaci on Biom edica en Red Fragilidad y Envejecimiento Saludable(CIBERFES)(Grant Nos.CB16/10/00477,CB16/10/00456,and CB16/10/00464)Plan Propio de Investigaci on of the University of Castilla-La Mancha,and Fondo Europeo de Desarrollo Regional(FEDER)funds from the European Union(Grant No.2022-GRIN-34296)+3 种基金further funded by grants from the Instituto de Salud Carlos III(Grant Nos.PI031558,PI07/90637,PI07/90306,RD 06/0013,and PI18/00972)the Government of Castilla-La Mancha(Grant Nos.03031 and SBPLY/19/180501/000312)Red EXERNETRED DE EJERCICIO FISICO Y SALUD:RED2022-134800T from the Spanish Ministry of Innovation and Sciencesupported by a research grant from the University of Castilla-La Mancha(Programa Investigo,Grant No.2024INVGO-12359)。
文摘Background:Low relative sit-to-stand(STS)power has emerged as a critical predictor of adverse health outcomes,such as frailty and disability,in older adults.However,its impact on falls,fractures,hospitalizations,and all-cause mortality remains unclear.Therefore,this longitudinal study aimed to investigate the potential associations between low relative STS power and these adverse health outcomes in older adults.Methods:A total of 1876 older adults(aged≥65 years,56.4%women)were included from the Toledo Study for Healthy Aging.Relative STS power was assessed using the 30-s STS test and the Alcazar equation.Participants were categorized as having low relative STS power based on previously established cut-off points(2.53 W/kg for men and 2.01 W/kg for women).Falls and fractures(hip and all-type)within the previous year were recorded.Hospitalizations and all-cause mortality were obtained during a follow-up of 6.8±3.1 years(mean±SD;median=7.8 years;interquartile range:3.9-10.1 years)and 9.7±3.5 years(median=10.9 years;interquartile range:8.2-12.5 years),respectively.Generalized linear mixed models,binary logistic regression,and proportional hazards regression adjusted for age,educational level,and comorbidities were used.Results:In men,low relative STS power was significantly associated with an increased likelihood of history of falls(odds ratio(OR)=1.73,95%confidence interval(95%CI):1.08-2.75,p=0.022)and all-type fractures(OR=1.86,95%CI:1.21-2.84,p=0.004)in the previous year.In women,low relative STS power was associated with a higher probability of hip fractures within the previous year(OR=3.25,95%CI:1.07-9.86,p=0.038).Low relative STS power predicted hospitalizations in women(hazard ratio(HR)=1.29,95%CI:1.06-1.58,p=0.012)and longer hospital stays in both men(p=0.020)and women(p=0.033).Low relative STS power significantly increased all-cause mortality in both men(HR=1.57,95%CI:1.26-1.97,p<0.001)and women(HR=2.04,95%CI:1.51-2.74,p<0.001).Conclusion:Low relative STS power was associated with history of hip fractures in women,whereas in men it was associated with history of falls and all-type fractures.Low relative STS power predicted hospitalizations in women but not in men.In both men and women,low relative STS power was associated with longer hospital stays and increased risk of all-cause mortality.
基金support from the Ministry of Science and Technology of Taiwan(Contract Nos.113-2221-E-011-130-MY2 and 113-2218-E-011-002)the support from Intelligent Manufactur-ing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan.
文摘Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.
