The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact vario...The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact various aspects of the system,including renewable-energy resource potential,power-system planning and operation,and electricity markets.The Intergovernmental Panel on Climate Change(IPCC)has pointed out that as climate change accelerates,extreme weather events will continue to become more frequent and severe.展开更多
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
Herein,we report a simple self-charging hybrid power system(SCHPS)based on binder-free zinc copper selenide nanostructures(ZnCuSe_(2) NSs)deposited carbon fabric(CF)(i.e.,ZnCuSe_(2)/CF),which is used as an active mate...Herein,we report a simple self-charging hybrid power system(SCHPS)based on binder-free zinc copper selenide nanostructures(ZnCuSe_(2) NSs)deposited carbon fabric(CF)(i.e.,ZnCuSe_(2)/CF),which is used as an active material in the fabrication of supercapacitor(SC)and triboelectric nanogenerator(TENG).At first,a binder-free ZnCuSe_(2)/CF was synthesized via a simple and facial hydrothermal synthesis approach,and the electrochemical properties of the obtained ZnCuSe_(2)/CF were evaluated by fabricating a symmetric quasi-solid-state SC(SQSSC).The ZCS-2(Zn:Cu ratio of 2:1)material deposited CF-based SQSSC exhibited good electrochemical properties,and the obtained maximum energy and power densities were 7.5 Wh kg^(-1)and 683.3 W kg^(-1),respectively with 97.6%capacitance retention after 30,000 cycles.Furthermore,the ZnCuSe_(2)/CF was coated with silicone rubber elastomer using a doctor blade technique,which is used as a negative triboelectric material in the fabrication of the multiple TENG(M-TENG).The fabricated M-TENG exhibited excellent electrical output performance,and the robustness and mechanical stability of the device were studied systematically.The practicality and applicability of the proposed M-TENG and SQSSC were systematically investigated by powering various low-power portable electronic components.Finally,the SQSSC was combined with the M-TENG to construct a SCHPS.The fabricated SCHPS provides a feasible solution for sustainable power supply,and it shows great potential in self-powered portable electronic device applications.展开更多
Grid-forming(GFM)control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation(CIG),including wind power,photovoltaic,and battery energy...Grid-forming(GFM)control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation(CIG),including wind power,photovoltaic,and battery energy storage.In this paper,we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy.The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation,denoted as the frequency-fixed zone.With the proposed strategy,the system frequency is almost always fixed at its rated value,achieving system active power balance independent of frequency,and intentional power flow adjustments are implemented through direct phase angle control.This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations.Furthermore,synchronization dynamics are significantly diminished,and synchronization stability is enhanced.The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.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
Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for eac...Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches,namely accuracy,training time,and model management complexity,a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First,the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently,K-means clustering was conducted on each line based on the similarity of their electrical features,employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally,a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover,this approach allows for more flexible adjustments to line topology changes.展开更多
The continuously increasing renewable energy sources(RES)and demand response(DR)are becoming crucial sources of system flexibility.Consequently,decision-dependent uncertainties(DDUs),inter-changeably referred to as en...The continuously increasing renewable energy sources(RES)and demand response(DR)are becoming crucial sources of system flexibility.Consequently,decision-dependent uncertainties(DDUs),inter-changeably referred to as endogenous uncertainties,impose new characteristics on power system dis-patch.The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR,while reserve providers encounter DDUs from the uncertain reserve deployment.Thus,a systematic framework was established in this study to address robust dispatch problems with DDUs.The main contributions are drawn as follows.①The robust characterization of DDUs was unfolded with a dependency decomposition structure.②A generic DDU coping mechanism was manifested as the bilateral matching between uncertainty and flexibility.③The influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems was analyzed.④Generic solution algorithms adaptive for DDUs were proposed.Under this framework,the inherent distinctions and correlations between DDUs and decision-independent uncertainties(DIUs)were revealed,laying a fundamental theoretical foundation for the economic and reliable operation of RES-dominated power systems.Illustrative applications in the source and demand sides are provided to show the significance of considering DDUs and demonstrate the proposed theoretical results.展开更多
In this paper,a strength-constrained unit commitment(UC)model incorporating system strength constraints based on the weighted short-circuit ratio(WSCR)is proposed.This model facilitates the comprehensive assessment of...In this paper,a strength-constrained unit commitment(UC)model incorporating system strength constraints based on the weighted short-circuit ratio(WSCR)is proposed.This model facilitates the comprehensive assessment of area-wide system strength in power systems with high inverter-based resource(IBR)penetration,thereby contributing to the mitigation of weak grid issues.Unlike traditional models,this approach considers the interactions among multiple IBRs.The UC problem is initially formulated as a mixed-integer nonlinear programming(MINLP)model,reflecting WSCR and bus impedance matrix modification constraints.To enhance computational tractability,the model is transformed into a mixed-integer linear programming(MILP)form.The effectiveness of the proposed approach is validated through simulations on the IEEE 5-bus,IEEE 39-bus,and a modified Korean power system,demonstrating the ability of the proposed UC model enhancing system strength compared to the conventional methodologies.展开更多
The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-domin...The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.展开更多
In recent years,renewable energy(RE)penetration has become an important target in power systems.However,RE power is affected by climate change and has strong randomness and volatility.Adequate transmission capacity an...In recent years,renewable energy(RE)penetration has become an important target in power systems.However,RE power is affected by climate change and has strong randomness and volatility.Adequate transmission capacity and energy storage systems(ESSs)are conducive to the integration of RE.Therefore,coordinated transmission renewable–storage expansion planning(TRSEP)is an effective decision-making approach to cope with the impacts of climate change and achieve the development tar-get of RE penetration.Electricity trading between different systems is common;therefore,in addition to the penetration of RE into the internal loads of the system,the proportion of RE generation in tie lines is gaining attention,making analyses of the RE transmission path necessary.Referring to the flow of carbon emissions,this paper defines the RE power flow density to track the transmission path of RE.Next,a TRSEP model is proposed that can clearly distinguish the RE transmission path into internal loads,exter-nal loads,and energy losses.To address the presence of bilinear terms in the proposed model,the McCormick method is applied,and a customized feasibility correction strategy is designed to obtain a good feasible solution.Numerical results from case studies are provided to verify the rationality and effectiveness of the approach proposed in this paper.展开更多
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
In wind and solar renewable-dominant hybrid alternating current/direct current(AC/DC)power systems,the active power of high-voltage direct current(HVDC)system is significantly limited by the security and stability eve...In wind and solar renewable-dominant hybrid alternating current/direct current(AC/DC)power systems,the active power of high-voltage direct current(HVDC)system is significantly limited by the security and stability events caused by cascading failures.To identify critical lines in cascading failures,a rapid risk assessment method is proposed based on the gradient boosting decision tree(GBDT)and frequent pat-tern growth(FP-Growth)algorithms.First,security and stability events triggered by cascading failures are analyzed to explain the impact of cascading failures on the maximum DC power.Then,a cascading failure risk index is defined,focusing on the DC power being limited.To handle the strong nonlinear relationship between the maximum DC power and cascading failures,a GBDT with an update strategy is utilized to rapidly predict the maximum DC power under uncertain operating conditions.Finally,the FP-Growth algorithm is improved to mine frequent patterns in cascading failures.The importance index for each fault in a frequent pattern is defined by evaluating its impact on cascading failures,enabling the identification of critical lines.Simulation results of a modified Ningxia–Shandong hybrid AC/DC system in China demonstrate that the proposed method can rapidly assess the risk of cascading failures and effectively identify critical lines.展开更多
This study considers the state estimation problem of the circuit breakers(CBs),solving for randomabrupt changes that occurred in power systems.With the abrupt changes randomly occurring,it is represented in a Markov c...This study considers the state estimation problem of the circuit breakers(CBs),solving for randomabrupt changes that occurred in power systems.With the abrupt changes randomly occurring,it is represented in a Markov chain,and then the CBs can be considered as a Markov jump system(MJS).In these MJSs,the transition probabilities are obtained from historical statistical data of the random abrupt changes when the faults occurred.Considering that the traditional Kalman filter(KF)frameworks based on MJS only depend on the subsystem of MJS,but neglect the stochastic jump between different subsystems.This study utilized the derandomization technique which transforms the stochastic MJS to a deterministic system to introduce the stochastic mode jumping in MJS,in which the state is still in the same norm,and the Lyapunov function is derived to show the stability condition of the systems,which proved that the transformed deterministic system is more conservative than the original MJS mathematically.After that,the Kalman filter algorithm is designed for estimating the state of the CBs depending on the transformed deterministic system.With the help of the Kalman filter,the estimation performance is derived by the recursive state estimation algorithm for the CBs.Furthermore,a single machine infinite-bus(SMIB)power system and a three-bus large scale system are proposed as practical examples to validate the effectiveness of the proposed algorithm.展开更多
With the continuous improvement of current science and technology and residents’electricity demand,the power system relay protection course has become a key component of the curriculum for students majoring in power ...With the continuous improvement of current science and technology and residents’electricity demand,the power system relay protection course has become a key component of the curriculum for students majoring in power engineering.In this context,the teaching model of power system relay protection faces both new challenges and opportunities.By integrating the Outcome-Based Education(OBE)concept,teachers can reconstruct curriculum objectives and teaching frameworks by clearly defining learning outcomes,thereby enhancing students’practical competencies and innovative thinking.Based on the core connotation of the OBE concept,this paper analyzes the significance of incorporating OBE into Power System Relay Protection teaching and explores effective implementation strategies.The findings aim to offer practical insights for teaching reform and to strengthen the alignment between academic training and industry requirements.展开更多
This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as ru...This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.展开更多
Traditional approaches to teaching the power system analysis course face challenges such as abstract and difficult-to-understand content,single teaching method,and limited practical links.In response,this paper explor...Traditional approaches to teaching the power system analysis course face challenges such as abstract and difficult-to-understand content,single teaching method,and limited practical links.In response,this paper explores in depth the significance and strategies of applying AI technology in the teaching of the course.The aim is to enhance students’ability to understand and apply knowledge,and to cultivate well-qualified technical professionals who can adapt to the intelligent development needs of the power industry.The proposed strategies include building an intelligent learning diagnosis platform,providing personalized learning guidance,developing an AI-integrated curriculum system,fostering a“dual-qualified and interdisciplinary”teaching team,and establishing a diversified assessment and evaluation system.展开更多
This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form,Quasi Oppositional Sine Cosine Algorithm,to automatic generation control of a multipl...This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form,Quasi Oppositional Sine Cosine Algorithm,to automatic generation control of a multiple-source-based interconnected power system that consists of thermal,gas,and hydro power plants.The Proportional-Integral-Derivative controller,which is utilized for automated generation control in an interconnected hybrid power systemwith aDClink connecting two regions,has been tuned using the proposed optimization technique.An Electric Vehicle is taken into consideration only as an electrical load.The Quasi Oppositional Sine Cosinemethod’s performance and efficacy have been compared to the Sine Cosine Algorithm and optimal output feedback controller tuning performance.Applying the QOSCA optimization technique,which has only been shown in this study in the context of an LFC research thus far,makes this paper unique.The main objective has been used to assess and compare the dynamic performances of the recommended controller along with QOSCA optimisation technic.The resilience of the controller is examined using two different system parameters:B(frequency bias parameter)and R(governor speed regulation).The sensitivity analysis results demonstrate the high reliability of the QOSCA algorithm-based controller.Once optimal controller gains are established for nominal conditions,step load perturbations up to±10%&±25%in the nominal values of the systemparameters and operational load condition do not require adjustment of the controller.Ultimately,a scenario is examined whereby EVs are used for area 1,and a single PID controller is used rather than three.展开更多
Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple ...Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.展开更多
1.Introduction Engineers,policymakers,and governments are currently facing the pressing global challenges of climate change and the energy crisis.To address the continuously increasing demand for energy and mitigate e...1.Introduction Engineers,policymakers,and governments are currently facing the pressing global challenges of climate change and the energy crisis.To address the continuously increasing demand for energy and mitigate environmental damage,energy conservation and emissions reduction have become strategic priorities for sustainable development[1].Nations worldwide have reached a consensus on reducing carbon emissions and have introduced various policies and actions,such as the carbon peak and carbon neutrality targets proposed by China[2,3].展开更多
Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors...Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors and operations are compromised,which can lead to big problems,disruptions,failures and blackouts.In response to this challenge,this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory(Bi-LSTM)networks and employs explanatory methods from Artificial Intelligence(AI).Not only does the suggested architecture detect potential fraud with high accuracy,but it also makes its decisions transparent,enabling operators to take appropriate action.Themethod developed here utilizesmodel-free,interpretable tools to identify essential input elements,thereby making predictions more understandable and usable.Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique(SMOTE)-based data balancing.Benchmark power system data confirms that the model functions correctly through detailed experiments.Experimental results showed that Bi-LSTM+Explainable AI(XAI)achieved an average accuracy of 94%,surpassing XGBoost(89%)and Bagging(84%),while ensuring explainability and a high level of robustness across various operating scenarios.By conducting an ablation study,we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability.Additionally,examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time.The research offers a practical and flexible approach for detecting FDI attacks,improving the security of cyber-physical systems,and facilitating the deployment of AI in energy infrastructure.展开更多
文摘The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact various aspects of the system,including renewable-energy resource potential,power-system planning and operation,and electricity markets.The Intergovernmental Panel on Climate Change(IPCC)has pointed out that as climate change accelerates,extreme weather events will continue to become more frequent and severe.
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIP)(No.2018R1A6A1A03025708)partly supported by the GRRC program of Gyeonggi province(GRRCKyungHee2023-B03).
文摘Herein,we report a simple self-charging hybrid power system(SCHPS)based on binder-free zinc copper selenide nanostructures(ZnCuSe_(2) NSs)deposited carbon fabric(CF)(i.e.,ZnCuSe_(2)/CF),which is used as an active material in the fabrication of supercapacitor(SC)and triboelectric nanogenerator(TENG).At first,a binder-free ZnCuSe_(2)/CF was synthesized via a simple and facial hydrothermal synthesis approach,and the electrochemical properties of the obtained ZnCuSe_(2)/CF were evaluated by fabricating a symmetric quasi-solid-state SC(SQSSC).The ZCS-2(Zn:Cu ratio of 2:1)material deposited CF-based SQSSC exhibited good electrochemical properties,and the obtained maximum energy and power densities were 7.5 Wh kg^(-1)and 683.3 W kg^(-1),respectively with 97.6%capacitance retention after 30,000 cycles.Furthermore,the ZnCuSe_(2)/CF was coated with silicone rubber elastomer using a doctor blade technique,which is used as a negative triboelectric material in the fabrication of the multiple TENG(M-TENG).The fabricated M-TENG exhibited excellent electrical output performance,and the robustness and mechanical stability of the device were studied systematically.The practicality and applicability of the proposed M-TENG and SQSSC were systematically investigated by powering various low-power portable electronic components.Finally,the SQSSC was combined with the M-TENG to construct a SCHPS.The fabricated SCHPS provides a feasible solution for sustainable power supply,and it shows great potential in self-powered portable electronic device applications.
基金supported by the National Key Research&Development Program of China under Grant 2024YFB2408900.
文摘Grid-forming(GFM)control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation(CIG),including wind power,photovoltaic,and battery energy storage.In this paper,we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy.The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation,denoted as the frequency-fixed zone.With the proposed strategy,the system frequency is almost always fixed at its rated value,achieving system active power balance independent of frequency,and intentional power flow adjustments are implemented through direct phase angle control.This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations.Furthermore,synchronization dynamics are significantly diminished,and synchronization stability is enhanced.The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.
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.
基金supported by the Science and Technology Project of SGCC(5100-202199558A-0-5-ZN).
文摘Transient stability assessment(TSA)based on artificial intelligence typically has two distinct model management approaches:a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches,namely accuracy,training time,and model management complexity,a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First,the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently,K-means clustering was conducted on each line based on the similarity of their electrical features,employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally,a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover,this approach allows for more flexible adjustments to line topology changes.
基金supported by the Joint Research Fund in Smart Grid(U1966601)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and State Grid Corporation of China.
文摘The continuously increasing renewable energy sources(RES)and demand response(DR)are becoming crucial sources of system flexibility.Consequently,decision-dependent uncertainties(DDUs),inter-changeably referred to as endogenous uncertainties,impose new characteristics on power system dis-patch.The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR,while reserve providers encounter DDUs from the uncertain reserve deployment.Thus,a systematic framework was established in this study to address robust dispatch problems with DDUs.The main contributions are drawn as follows.①The robust characterization of DDUs was unfolded with a dependency decomposition structure.②A generic DDU coping mechanism was manifested as the bilateral matching between uncertainty and flexibility.③The influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems was analyzed.④Generic solution algorithms adaptive for DDUs were proposed.Under this framework,the inherent distinctions and correlations between DDUs and decision-independent uncertainties(DIUs)were revealed,laying a fundamental theoretical foundation for the economic and reliable operation of RES-dominated power systems.Illustrative applications in the source and demand sides are provided to show the significance of considering DDUs and demonstrate the proposed theoretical results.
基金partially supported by Korea Electrotechnology Research Institute(KERI)Primary research program through the National Research Council of Science&Technology(NST)funded by the Ministry of Science and ICT(MSIT)(No.25A01038)partially supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00218377).
文摘In this paper,a strength-constrained unit commitment(UC)model incorporating system strength constraints based on the weighted short-circuit ratio(WSCR)is proposed.This model facilitates the comprehensive assessment of area-wide system strength in power systems with high inverter-based resource(IBR)penetration,thereby contributing to the mitigation of weak grid issues.Unlike traditional models,this approach considers the interactions among multiple IBRs.The UC problem is initially formulated as a mixed-integer nonlinear programming(MINLP)model,reflecting WSCR and bus impedance matrix modification constraints.To enhance computational tractability,the model is transformed into a mixed-integer linear programming(MILP)form.The effectiveness of the proposed approach is validated through simulations on the IEEE 5-bus,IEEE 39-bus,and a modified Korean power system,demonstrating the ability of the proposed UC model enhancing system strength compared to the conventional methodologies.
基金supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd under Grant 036000KC23090004(GDKJXM20231026).
文摘The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.
基金supported by State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22119).
文摘In recent years,renewable energy(RE)penetration has become an important target in power systems.However,RE power is affected by climate change and has strong randomness and volatility.Adequate transmission capacity and energy storage systems(ESSs)are conducive to the integration of RE.Therefore,coordinated transmission renewable–storage expansion planning(TRSEP)is an effective decision-making approach to cope with the impacts of climate change and achieve the development tar-get of RE penetration.Electricity trading between different systems is common;therefore,in addition to the penetration of RE into the internal loads of the system,the proportion of RE generation in tie lines is gaining attention,making analyses of the RE transmission path necessary.Referring to the flow of carbon emissions,this paper defines the RE power flow density to track the transmission path of RE.Next,a TRSEP model is proposed that can clearly distinguish the RE transmission path into internal loads,exter-nal loads,and energy losses.To address the presence of bilinear terms in the proposed model,the McCormick method is applied,and a customized feasibility correction strategy is designed to obtain a good feasible solution.Numerical results from case studies are provided to verify the rationality and effectiveness of the approach proposed in this paper.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
基金supported by the National Key Research and Development Program of China"Key technologies for system stability and HVDC transmission of large-scale renewable energy generation base without conventional power support(2022YFB2402700)"the project of the State Grid Corporation of China(52272222001J).
文摘In wind and solar renewable-dominant hybrid alternating current/direct current(AC/DC)power systems,the active power of high-voltage direct current(HVDC)system is significantly limited by the security and stability events caused by cascading failures.To identify critical lines in cascading failures,a rapid risk assessment method is proposed based on the gradient boosting decision tree(GBDT)and frequent pat-tern growth(FP-Growth)algorithms.First,security and stability events triggered by cascading failures are analyzed to explain the impact of cascading failures on the maximum DC power.Then,a cascading failure risk index is defined,focusing on the DC power being limited.To handle the strong nonlinear relationship between the maximum DC power and cascading failures,a GBDT with an update strategy is utilized to rapidly predict the maximum DC power under uncertain operating conditions.Finally,the FP-Growth algorithm is improved to mine frequent patterns in cascading failures.The importance index for each fault in a frequent pattern is defined by evaluating its impact on cascading failures,enabling the identification of critical lines.Simulation results of a modified Ningxia–Shandong hybrid AC/DC system in China demonstrate that the proposed method can rapidly assess the risk of cascading failures and effectively identify critical lines.
文摘This study considers the state estimation problem of the circuit breakers(CBs),solving for randomabrupt changes that occurred in power systems.With the abrupt changes randomly occurring,it is represented in a Markov chain,and then the CBs can be considered as a Markov jump system(MJS).In these MJSs,the transition probabilities are obtained from historical statistical data of the random abrupt changes when the faults occurred.Considering that the traditional Kalman filter(KF)frameworks based on MJS only depend on the subsystem of MJS,but neglect the stochastic jump between different subsystems.This study utilized the derandomization technique which transforms the stochastic MJS to a deterministic system to introduce the stochastic mode jumping in MJS,in which the state is still in the same norm,and the Lyapunov function is derived to show the stability condition of the systems,which proved that the transformed deterministic system is more conservative than the original MJS mathematically.After that,the Kalman filter algorithm is designed for estimating the state of the CBs depending on the transformed deterministic system.With the help of the Kalman filter,the estimation performance is derived by the recursive state estimation algorithm for the CBs.Furthermore,a single machine infinite-bus(SMIB)power system and a three-bus large scale system are proposed as practical examples to validate the effectiveness of the proposed algorithm.
文摘With the continuous improvement of current science and technology and residents’electricity demand,the power system relay protection course has become a key component of the curriculum for students majoring in power engineering.In this context,the teaching model of power system relay protection faces both new challenges and opportunities.By integrating the Outcome-Based Education(OBE)concept,teachers can reconstruct curriculum objectives and teaching frameworks by clearly defining learning outcomes,thereby enhancing students’practical competencies and innovative thinking.Based on the core connotation of the OBE concept,this paper analyzes the significance of incorporating OBE into Power System Relay Protection teaching and explores effective implementation strategies.The findings aim to offer practical insights for teaching reform and to strengthen the alignment between academic training and industry requirements.
文摘This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.
文摘Traditional approaches to teaching the power system analysis course face challenges such as abstract and difficult-to-understand content,single teaching method,and limited practical links.In response,this paper explores in depth the significance and strategies of applying AI technology in the teaching of the course.The aim is to enhance students’ability to understand and apply knowledge,and to cultivate well-qualified technical professionals who can adapt to the intelligent development needs of the power industry.The proposed strategies include building an intelligent learning diagnosis platform,providing personalized learning guidance,developing an AI-integrated curriculum system,fostering a“dual-qualified and interdisciplinary”teaching team,and establishing a diversified assessment and evaluation system.
文摘This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form,Quasi Oppositional Sine Cosine Algorithm,to automatic generation control of a multiple-source-based interconnected power system that consists of thermal,gas,and hydro power plants.The Proportional-Integral-Derivative controller,which is utilized for automated generation control in an interconnected hybrid power systemwith aDClink connecting two regions,has been tuned using the proposed optimization technique.An Electric Vehicle is taken into consideration only as an electrical load.The Quasi Oppositional Sine Cosinemethod’s performance and efficacy have been compared to the Sine Cosine Algorithm and optimal output feedback controller tuning performance.Applying the QOSCA optimization technique,which has only been shown in this study in the context of an LFC research thus far,makes this paper unique.The main objective has been used to assess and compare the dynamic performances of the recommended controller along with QOSCA optimisation technic.The resilience of the controller is examined using two different system parameters:B(frequency bias parameter)and R(governor speed regulation).The sensitivity analysis results demonstrate the high reliability of the QOSCA algorithm-based controller.Once optimal controller gains are established for nominal conditions,step load perturbations up to±10%&±25%in the nominal values of the systemparameters and operational load condition do not require adjustment of the controller.Ultimately,a scenario is examined whereby EVs are used for area 1,and a single PID controller is used rather than three.
基金Financial support from the National Natural Science Foundation of China under Grant(22393954 and 22078358)is gratefully acknowledged.
文摘Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.
基金supported by the National Natural Science Foundation of China(62293500,62293502,and 62293504)the State Key Laboratory of Industrial Control Technology,China(ICT2024A22)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)(B17017).
文摘1.Introduction Engineers,policymakers,and governments are currently facing the pressing global challenges of climate change and the energy crisis.To address the continuously increasing demand for energy and mitigate environmental damage,energy conservation and emissions reduction have become strategic priorities for sustainable development[1].Nations worldwide have reached a consensus on reducing carbon emissions and have introduced various policies and actions,such as the carbon peak and carbon neutrality targets proposed by China[2,3].
基金the Deanship of Scientific Research and Libraries in Princess Nourah bint Abdulrahman University for funding this research work through the Research Group project,Grant No.(RG-1445-0064).
文摘Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors and operations are compromised,which can lead to big problems,disruptions,failures and blackouts.In response to this challenge,this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory(Bi-LSTM)networks and employs explanatory methods from Artificial Intelligence(AI).Not only does the suggested architecture detect potential fraud with high accuracy,but it also makes its decisions transparent,enabling operators to take appropriate action.Themethod developed here utilizesmodel-free,interpretable tools to identify essential input elements,thereby making predictions more understandable and usable.Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique(SMOTE)-based data balancing.Benchmark power system data confirms that the model functions correctly through detailed experiments.Experimental results showed that Bi-LSTM+Explainable AI(XAI)achieved an average accuracy of 94%,surpassing XGBoost(89%)and Bagging(84%),while ensuring explainability and a high level of robustness across various operating scenarios.By conducting an ablation study,we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability.Additionally,examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time.The research offers a practical and flexible approach for detecting FDI attacks,improving the security of cyber-physical systems,and facilitating the deployment of AI in energy infrastructure.
