Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gat...Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption,thereby transforming the traditional power grid into a smart grid(SG).However,the openness of wireless communication channels introduces vulnerabilities,as it allows potential eavesdroppers to intercept sensitive information.This poses threats to the secure and efficient operation of the IoT-driven smart grid.To address these challenges,we propose a novel scenario that incorporates an Unmanned Aerial Vehicle(UAV)as a relay gateway for multiple authorized smart meters.This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface(RIS)technology,which dynamically adjusts the direction of information transmission.Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security(PLS)techniques.We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV,the passive beamforming of the RIS,and the UAV’s trajectory.To solve this complex optimization problem,we introduce the Twin Soft Actor-Critic(TSAC)algorithm.This algorithm employs a dual-agent framework,where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS,while Agent 2 concurrently searches for the optimal trajectory of the UAV.Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system,achieving faster convergence and higher rewards under the worst communication conditions.The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient(TDDPG)and Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithms.Furthermore,the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution,further validating its practical applicability and effectiveness in real-world scenarios.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and explorati...In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale...An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.展开更多
The integration of wind power and natural gas for hydrogen production forms a Green and Blue Hydrogen Integrated Energy System(GBH-IES),which is a promising cogeneration approach characterized by multienergy complemen...The integration of wind power and natural gas for hydrogen production forms a Green and Blue Hydrogen Integrated Energy System(GBH-IES),which is a promising cogeneration approach characterized by multienergy complementarity,flexible dispatch,and efficient utilization.This system can meet the demands for electricity,heat,and hydrogen while demonstrating significant performance in energy supply,energy conversion,economy,and environment(4E).To evaluate the GBH-IES system effectively,a comprehensive performance evaluation index system was constructed from the 4E dimensions.The fuzzy DEMATEL method was used to quantify the causal relationships between indicators,establishing a scientific input-output assessment system.The DEA model was then employed for preliminary performance evaluation of the hydrogen storage system,followed by the entropy weight TOPSIS method to enhance the accuracy and reliability of the assessment results.The study also conducted a comprehensive benefit evaluation and sensitivity analysis for different cases involving blue hydrogen,green hydrogen,and their synergistic effects under varying carbon emission factors(CEFs)and hydrogen blending ratios(HBRs).The results indicate that combining green and blue hydrogen can achieve higher comprehensive benefits for the hydrogen storage system,providing valuable insights for hydrogen storage development and demonstrating the effectiveness of themulti-criteria decision-making methods used.展开更多
The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,dist...The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.展开更多
As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-int...As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addi...Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.展开更多
With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization p...With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization problem inmulti-microgrid cooperation,a cooperative game strategy based on the Nash bargainingmodel is proposed,aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization.First,each microgrid is regarded as a game participant,and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed,targeting the minimization of total operational cost under constraints such as power balance and energy storage limits.Second,the Nash bargaining solution is introduced as the benefit allocation scheme to ensure individual rationality and coalition stability.Finally,theAlternating Direction Method of Multipliers(ADMM)is employed to decompose the centralized optimization problem into distributed subproblems for iterative solution,thereby reducing communication burden and protecting privacy.Case studies reveal that the operational costs of the threemicrogrids are reduced by 26.28%,19.00%,and 17.19%,respectively,and the overall renewable energy consumption rate is improved by approximately 66.11%.展开更多
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.展开更多
The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllabl...The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.展开更多
Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of cha...Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.展开更多
The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastr...The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.展开更多
In China,the primary energy bases and the energyrequirement areas are mismatched,which presents theremote and reverse distribution characteristics,and thelarge-scale power flow is from the west to the cast andfrom the...In China,the primary energy bases and the energyrequirement areas are mismatched,which presents theremote and reverse distribution characteristics,and thelarge-scale power flow is from the west to the cast andfrom the north to the south.In this paper,according tothe above characteristics and the power flow pattern,thelarge-scale,high-efficiency and long-distance UHVACand UHVDC combined transmission scheme is investi-gated and proposed,and the development mode ofChina's power grid and the planning scheme of‘NorthChina-Central China-East China'synchronous UHV gridwhich can satisfy the healthy and sustainabledevelopment of China's economy and society is proposedand optimized.展开更多
The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage...The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.展开更多
The integration of large-scale new energy and high-capacity DC transmission leads to a reduction in system inertia.Grid-forming renewable energy sources(GF-RES)has a significant improvement effect on system inertia.Co...The integration of large-scale new energy and high-capacity DC transmission leads to a reduction in system inertia.Grid-forming renewable energy sources(GF-RES)has a significant improvement effect on system inertia.Commutation failure faults may cause a short-term reactive power surplus at the sending end and trigger transient overvoltage,threatening the safe and stable operation of the power grid.However,there is a lack of research on the calculation method of transient overvoltage caused by commutation failure in high-voltage DC transmission systems with grid-forming renewable energy sources integration.Based on the existing equivalent model of highvoltage DC transmission systems at the sending end,this paper proposes to construct a model of the high-voltage DC transmission system at the sending end with grid-forming renewable energy sources.The paper first clarifies the mechanism of overvoltage generation,then considers the reactive power droop control characteristics of GF-RES,and derives the transient voltage calculation model of theDC transmission system with GF-RES integration.It also proposes a calculation method for transient overvoltage at the sending-end converter bus with GF-RES integration.Based on the PSCAD/EMTDC simulation platform,this paper builds an experimental simulation model.By constructing three different experimental scenarios,the accuracy and effectiveness of the proposed transient overvoltage calculation method are verified,with a calculation error within 5%.At the same time,this paper quantitatively analyzes the impact of grid strength,new energy proportion,and rated transmission power on transient overvoltage from three different perspectives.展开更多
Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations o...Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations of renewable energy resources(RESs).However,challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process.To address these issues,this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set(DcUS)derived from historical data patterns.The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness.Additionally,a fully parallel column and constraint generation algorithm is developed to solve the model efficiently.Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8%compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios.These results highlight the computational efficiency,robustness,and practicality of the proposed approach,making it a promising solution for modern power systems.展开更多
基金supported by State Grid Shanxi Electric Power Company’s Science and Technology Projects(No.52051C230102).
文摘Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption,thereby transforming the traditional power grid into a smart grid(SG).However,the openness of wireless communication channels introduces vulnerabilities,as it allows potential eavesdroppers to intercept sensitive information.This poses threats to the secure and efficient operation of the IoT-driven smart grid.To address these challenges,we propose a novel scenario that incorporates an Unmanned Aerial Vehicle(UAV)as a relay gateway for multiple authorized smart meters.This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface(RIS)technology,which dynamically adjusts the direction of information transmission.Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security(PLS)techniques.We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV,the passive beamforming of the RIS,and the UAV’s trajectory.To solve this complex optimization problem,we introduce the Twin Soft Actor-Critic(TSAC)algorithm.This algorithm employs a dual-agent framework,where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS,while Agent 2 concurrently searches for the optimal trajectory of the UAV.Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system,achieving faster convergence and higher rewards under the worst communication conditions.The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient(TDDPG)and Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithms.Furthermore,the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution,further validating its practical applicability and effectiveness in real-world scenarios.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
文摘In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金supported by Yunnan Power Grid Co.,Ltd.Science and Technology Project:Research and application of key technologies for graphical-based power grid accident reconstruction and simulation(YNKJXM20240333).
文摘An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
基金The Key Research andDevelopment Project of Xinjiang Uygur Autonomous Region,with the grant number 2024B04025The General Programof Natural Science Foundation of Xinjiang Uygur Autonomous Region,with the grant number 2022D01C366.
文摘The integration of wind power and natural gas for hydrogen production forms a Green and Blue Hydrogen Integrated Energy System(GBH-IES),which is a promising cogeneration approach characterized by multienergy complementarity,flexible dispatch,and efficient utilization.This system can meet the demands for electricity,heat,and hydrogen while demonstrating significant performance in energy supply,energy conversion,economy,and environment(4E).To evaluate the GBH-IES system effectively,a comprehensive performance evaluation index system was constructed from the 4E dimensions.The fuzzy DEMATEL method was used to quantify the causal relationships between indicators,establishing a scientific input-output assessment system.The DEA model was then employed for preliminary performance evaluation of the hydrogen storage system,followed by the entropy weight TOPSIS method to enhance the accuracy and reliability of the assessment results.The study also conducted a comprehensive benefit evaluation and sensitivity analysis for different cases involving blue hydrogen,green hydrogen,and their synergistic effects under varying carbon emission factors(CEFs)and hydrogen blending ratios(HBRs).The results indicate that combining green and blue hydrogen can achieve higher comprehensive benefits for the hydrogen storage system,providing valuable insights for hydrogen storage development and demonstrating the effectiveness of themulti-criteria decision-making methods used.
文摘The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.
基金funded by the Technology Project of State Grid Jibei Electric Power Supply Co.,Ltd.(Grant Number:52018F240001).
文摘As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金the Science and Technology Project of State Grid Fujian Electric Power Co.,Ltd.(Project No.B31300240001)with the project title“Research on Key Technologies for Load Forecasting and Regulation Capability Evaluation of Regional Power Grid Taking into AccountWide Area Distributed New Energy Access”.
文摘Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.
基金funded by StateGrid Beijing Electric PowerCompany Technology Project,grant number 520210230004.
文摘With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization problem inmulti-microgrid cooperation,a cooperative game strategy based on the Nash bargainingmodel is proposed,aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization.First,each microgrid is regarded as a game participant,and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed,targeting the minimization of total operational cost under constraints such as power balance and energy storage limits.Second,the Nash bargaining solution is introduced as the benefit allocation scheme to ensure individual rationality and coalition stability.Finally,theAlternating Direction Method of Multipliers(ADMM)is employed to decompose the centralized optimization problem into distributed subproblems for iterative solution,thereby reducing communication burden and protecting privacy.Case studies reveal that the operational costs of the threemicrogrids are reduced by 26.28%,19.00%,and 17.19%,respectively,and the overall renewable energy consumption rate is improved by approximately 66.11%.
文摘By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
基金supported in part by Science and Technology Project of State Grid Corporation of China(No.5400-202319829A-4-1-KJ).
文摘The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.
基金supported by National Key R&D Program of ChinaunderGrant,(2021YFB2601403).
文摘Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.
文摘In China,the primary energy bases and the energyrequirement areas are mismatched,which presents theremote and reverse distribution characteristics,and thelarge-scale power flow is from the west to the cast andfrom the north to the south.In this paper,according tothe above characteristics and the power flow pattern,thelarge-scale,high-efficiency and long-distance UHVACand UHVDC combined transmission scheme is investi-gated and proposed,and the development mode ofChina's power grid and the planning scheme of‘NorthChina-Central China-East China'synchronous UHV gridwhich can satisfy the healthy and sustainabledevelopment of China's economy and society is proposedand optimized.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China(21YJA790009)National Natural Science Foundation of China(72140001).
文摘The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.
基金supported by Key Natural Science Research Projects of Colleges and Universities in Anhui Province(2022AH051831).
文摘The integration of large-scale new energy and high-capacity DC transmission leads to a reduction in system inertia.Grid-forming renewable energy sources(GF-RES)has a significant improvement effect on system inertia.Commutation failure faults may cause a short-term reactive power surplus at the sending end and trigger transient overvoltage,threatening the safe and stable operation of the power grid.However,there is a lack of research on the calculation method of transient overvoltage caused by commutation failure in high-voltage DC transmission systems with grid-forming renewable energy sources integration.Based on the existing equivalent model of highvoltage DC transmission systems at the sending end,this paper proposes to construct a model of the high-voltage DC transmission system at the sending end with grid-forming renewable energy sources.The paper first clarifies the mechanism of overvoltage generation,then considers the reactive power droop control characteristics of GF-RES,and derives the transient voltage calculation model of theDC transmission system with GF-RES integration.It also proposes a calculation method for transient overvoltage at the sending-end converter bus with GF-RES integration.Based on the PSCAD/EMTDC simulation platform,this paper builds an experimental simulation model.By constructing three different experimental scenarios,the accuracy and effectiveness of the proposed transient overvoltage calculation method are verified,with a calculation error within 5%.At the same time,this paper quantitatively analyzes the impact of grid strength,new energy proportion,and rated transmission power on transient overvoltage from three different perspectives.
基金supported by the National Natural Science Foundation of China(72271213)the Shenzhen Science and Technology Program(JCYJ20220530143800001 and RCYX20221008092927070)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(2024A1515240024)the National Key Research and Development Program of China(2022YFB2403500).
文摘Interconnection planning involving bi-directional converters(BdCs)is crucial for enhancing the reliability and robustness of hybrid alternating current(AC)/direct current(DC)microgrid clusters with high penetrations of renewable energy resources(RESs).However,challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process.To address these issues,this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set(DcUS)derived from historical data patterns.The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness.Additionally,a fully parallel column and constraint generation algorithm is developed to solve the model efficiently.Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8%compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios.These results highlight the computational efficiency,robustness,and practicality of the proposed approach,making it a promising solution for modern power systems.
