This paper introduces a novel hybrid optimization algorithm,Adaptive Hybrid PSO-Embedded GA(AHPEGA),which dynamically adapts to optimization performance by integrating Particle Swarm Optimization(PSO)and Genetic Algor...This paper introduces a novel hybrid optimization algorithm,Adaptive Hybrid PSO-Embedded GA(AHPEGA),which dynamically adapts to optimization performance by integrating Particle Swarm Optimization(PSO)and Genetic Algorithms(GA).The primary objective is to enhance the neuroevolutionary training of multilayer perceptron-based controllers(MLPCs)through the joint optimization of model parameters and structural hyperparameters.Traditional training methods frequently encounter issues such as premature convergence and limited generalization.AHPEGA addresses these limitations through an adaptive training strategy that dynamically adjusts parameters during the evolutionary process,thereby improving convergence speed and solution quality.By effectively reducing entrapment in local minima and balancing exploration and exploitation,AHPEGA improves the quality of neural controller design.The algorithm’s performance is evaluated against conventional optimization methods,demonstrating significant improvements in accuracy,convergence speed,and consistency across multiple runs.The practical applicability of the proposed method is demonstrated through simulation in the context of a VSC-based islanded microgrid(MG),where ensuring reliable and effective control under variable operating conditions is critical.This highlights AHPEGA’s capability to optimize intelligent control strategies in MG systems,particularly under dynamic and uncertain conditions,reinforcing its practical value in real-world energy environments.展开更多
Islanded microgrids(IMGs)offer a viable and efficient energy self-sustaining solution for distributed resources in remote areas.While without utility grid support,the frequency of IMG is susceptible to mismatches betw...Islanded microgrids(IMGs)offer a viable and efficient energy self-sustaining solution for distributed resources in remote areas.While without utility grid support,the frequency of IMG is susceptible to mismatches between demand and generation.Moreover,IMGs encounter uncertain and nonlinear load disturbances together with system parameter perturbation,which further compromises frequency stability.To this aim,this paper proposes a robust multi-virtual synchronous generators(multi-VSGs)coordinated control strategy for distributed secondary frequency regulation(DSFR)in IMGs,which exhibits minimal model dependency and avoids reliance on global information.Two critical methods are developed:(1)a robust VSG control framework that incorporates the linear active disturbance rejection control(LADRC)technique,which enables the estimation and effective elimination of uncertain load disturbances and system's parameter perturbations;(2)a novel secondorder consensus algorithm-based control law for robust secondary frequency regulation,which is featured with proper power sharing among different participants,suppressed power oscillation caused by response disparities,and reduced reliance on complex communication system.Building on methods(1)and(2),a novel multi-VSGs coordinated control strategy is proposed,providing a robust solution for IMG's frequency restoration,and its dynamic characteristics are explored in detail.The correctness and effectiveness of the proposal are verified by both simulation and the hardware-in-the-loop(HIL)experiment results across typical scenarios.展开更多
This survey paper provides a critical overview of optimization formulations for planning and operation of islanded microgrids,including optimization objectives,constraints,and control variables.The optimization approa...This survey paper provides a critical overview of optimization formulations for planning and operation of islanded microgrids,including optimization objectives,constraints,and control variables.The optimization approaches reviewed address methods both for increasing the resiliency of advanced distribution systems and electrification of remote communities.This paper examines over 120 individual optimization studies and discovers that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions,14 constraints,and 13 control variables.Each of the objectives,constraints,and variables are discussed exhaustively both from the perspective of their importance to islanded microgrids and chronological trends in their popularity.展开更多
The high proportion of nonlinear and unbalanced loads results in power quality issues in islanded microgrids.This paper presents a novel control strategy for harmonic and unbalanced power allocation among distributed ...The high proportion of nonlinear and unbalanced loads results in power quality issues in islanded microgrids.This paper presents a novel control strategy for harmonic and unbalanced power allocation among distributed genera-tors(DGs)in microgrids.Different from the existing sharing strategies that allocate the harmonic and unbalanced power according to the rated capacities of DGs,the proposed control strategy intends to shape the lowest output impedances of DGs to optimize the power quality of the microgrid.To achieve this goal,the feasible range of virtual impedance is analyzed in detail by eigenvalue analysis,and the findings suggest a simultaneous adjustment of real and imaginary parts of virtual impedance.Because virtual impedance is an open-loop control that imposes DG to the risk of overload,a new closed-loop structure is designed that uses residual capacity and absorbed power as feedback.Accordingly,virtual impedance can be safely adjusted in the feasible range until the power limit is reached.In addi-tion,a fuzzy integral controller is adopted to improve the dynamics and convergence of the power distribution,and its performance is found to be superior to linear integral controllers.Finally,simulations and control hardware-in-the-loop experiments are conducted to verify the effectiveness and usefulness of the proposed control strategy.展开更多
For islanded microgrids(MGs),distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs.However,distributed control with successive communication restricts t...For islanded microgrids(MGs),distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs.However,distributed control with successive communication restricts the efficiency and resilience of the control system.To address this issue,this paper proposes a distributed event-triggered control strategy for the frequency secondary control in islanded MGs.The proposed event-triggered control is Zeno behavior free and enables each DG to update and propagate its state to neighboring DGs only when a specific“event”occurs,which significantly reduces the communication burden.Compared with the existing event-triggered control,a trigger condition checking period of the proposed event-triggered control is provided to reduce the computation burden when checking the trigger condition.Furthermore,using the aperiodicity and intermittent properties of the communication,a simple detection principle is proposed to detect and isolate the compromised communication links in a timely and economic fashion,which improves the resilience of the system against FDI attacks.Finally,the control effectiveness of the proposed control scheme is validated by the simulation results of the tests on an MG with 4 DGs.展开更多
Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most ...Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.展开更多
Load flow analysis is a significant tool for proper planning,operation,and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency.However...Load flow analysis is a significant tool for proper planning,operation,and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency.However,due to the absence of a slack bus in an islanded microgrid,modified load flow algorithms should be adopted considering the system frequency as one of the solution variables.This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids.Several nature-inspired algorithms such as genetic algorithm(GA),differential evolution(DE),flower pollination algorithm(FPA),and grasshopper optimization algorithm(GOA)are separately merged with imperialistic competitive algorithm(ICA)to form four hybrid algorithms named as ICGA,ICDE,ICFPA,and ICGOA.Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system.A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software.From the statistical analysis,it is identified that on an average,ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.展开更多
In this study,a novel observer-based scalable control scheme for large-scale systems(LSSs)with several interconnected subsystems is explored.Firstly,a scalable observer-based controller is designed to address complex ...In this study,a novel observer-based scalable control scheme for large-scale systems(LSSs)with several interconnected subsystems is explored.Firstly,a scalable observer-based controller is designed to address complex situations where system states are difficult to measure directly.Secondly,unlike the limited cascade and ring topology connections in previous results,this study considers a universal arbitrary topology.Furthermore,it is noteworthy that the plug-and-play(PnP)capability of LSSs is guaranteed thanks to the proposed scalable scheme.Specifically,when subsystems are added or removed,only the controller gains of directly connected neighbors need updating,eliminating the need to redesign the entire system.Moreover,by choosing a Lyapunov-Krasovskii function with a quadratic matrix-valued polynomial,sufficient conditions are deduced to guarantee the global exponential stability with the desired extended dissipative performance for the resulting LSSs.Finally,the effectiveness of the employed scheme is verified by numerical and microgrid examples.展开更多
This work presents a novel coordinated control strategy of a hybrid photovoltaic/battery energy storage(PV/BES) system. Different controller operation modes are simulated considering normal, high fluctuation and emerg...This work presents a novel coordinated control strategy of a hybrid photovoltaic/battery energy storage(PV/BES) system. Different controller operation modes are simulated considering normal, high fluctuation and emergency conditions. When the system is grid-connected, BES regulates the fluctuated power output which ensures smooth net injected power from the PV/BES system. In islanded operation, BES system is transferred to single master operation during which the frequency and voltage of the islanded microgrid are regulated at the desired level. PSCAD/EMTDC simulation validates the proposed method and obtained favorable results on power set-point tracking strategies with very small deviations of net output power compared to the power set-point. The state-of-charge regulation scheme also very effective with SOC has been regulated between 32% and 79% range.展开更多
In this paper,an optimal secondary control strategy is proposed for islanded AC microgrids considering communi-cation time-delays.The proposed method is designed based on the data-driven principle,which consists of an...In this paper,an optimal secondary control strategy is proposed for islanded AC microgrids considering communi-cation time-delays.The proposed method is designed based on the data-driven principle,which consists of an offine training phase and online application phase.For offline training,each control agent is formulated by a deep neural network(DNN)and trained based on a multi-agent deep reinforcement learning(MA-DRL)framework.A deep deterministic policy gradient(DDPG)algorithm is improved and applied to search for an optimal policy of the secondary control,where a global cost function is developed to evaluate the overall control performance.In addition,the communication time-delay is introduced in the system to enrich training scenarios,which aims to solve the time-delay problem in the secondary control.For the online stage,each controller is deployed in a distributed way which only requires local and neighboring information for each DG.Based on this,the well-trained controllers can provide optimal solutions under load variations,and communication time-delays for online applications.Several case studies are conducted to validate the feasibility and stability of the proposed secondary control.Index Terms-Communication time-delay,global cost function,islanded AC microgrid,multi-agent deep reinforcement learning(MA-DRL),secondary control.展开更多
The limitation of fault currents from converter based distributed generators(CBDGs)in hybrid AC/DC islanded microgrids poses a significant challenge for microgrid protection.This paper presents a novel interharmonic c...The limitation of fault currents from converter based distributed generators(CBDGs)in hybrid AC/DC islanded microgrids poses a significant challenge for microgrid protection.This paper presents a novel interharmonic current differential protection scheme for the AC side of hybrid AC/DC islanded microgrids supplied by CBDGs.During faults,the proposed scheme exploits the varying interharmonic components of the currents at both terminals of the faulted line,arise due to variations in the droop-based no-load frequency limits of the interlinking converters(ICs)and the CBDGs.By leveraging these variations,the scheme effectively detects and isolates internal faults within the AC sub-grid,enhancing system reliability.The effectiveness of the suggested scheme is assessed using an enhanced IEEE33-bus hybrid AC/DC microgrid modelled in PSCAD/EMTDC,demonstrating its ability to reliably detect and isolate faults under various operating conditions.Additionally,the scheme is further evaluated using a real-time hardware-in-the-loop experimental setup implemented on an RTDS platform,validating its practical applicability.The simulation and experimental results validate that the presented protection scheme accurately discriminates between normal and faulty conditions across various fault locations,types,and resistance values.This discrimination is achieved without requiring high-bandwidth communication,overcoming a key limitation of existing protection schemes and improving feasibility in real-world deployments.展开更多
This paper employs artificial intelligence and machine learning techniques to predict the dominant oscillation modes in AC microgrids.The dominant modes are highly dependent on the droop gains and only slightly affect...This paper employs artificial intelligence and machine learning techniques to predict the dominant oscillation modes in AC microgrids.The dominant modes are highly dependent on the droop gains and only slightly affected by the loading conditions.This paper utilizes the least absolute shrinkage and select operator(LASSO)algorithm to extract the key features contributing directly to dominant modes.The adaptive neuro-fuzzy inference system(ANFIS)is employed as a nonlinear regression technique to train a model that relates the system’s key features to the dominant modes of the AC microgrid.The data obtained from a 6-bus AC microgrid test system is used to train the LASSO-based ANFIS model.The results show that the proposed method can substantially reduce the data volume of the training set due to LASSO sparse feature.The precision of the proposed algorithm is de-termined by comparing its output to the modes deter-mined by the derived small-signal model of the system.展开更多
Due to the lack of support from the main grid,the intermittency of renewable energy sources(RESs)and the fluctuation of load will derive uncertainties to the operation of islanded microgrids(IMGs).It is crucial to all...Due to the lack of support from the main grid,the intermittency of renewable energy sources(RESs)and the fluctuation of load will derive uncertainties to the operation of islanded microgrids(IMGs).It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs.With the high penetration of RESs,it faces both economic and environmental challenges if we only use spinning reserve for reserve support.To solve these problems,a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation,load,and storage.The operation risk due to reserve shortage is modeled by the conditional value-at-risk(CVaR)method.The correlation of input variables is considered for the forecasting error modeling of RES and load,and Latin hypercube sampling(LHS)is adopted to generate the random scenarios of the forecasting error,so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches.Furthermore,an optimal day-ahead scheduling model of joint energy and reserve considering riskbased reserve decision is established to coordinate the security and economy of the operation of IMGs.Finally,the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.展开更多
A virtual synchronous generator(VSG)can provide inertial support through renewables and energy storage.It generally operates in parallel with a diesel generator(DSG)in an islanded microgrid.However,unforeseen interact...A virtual synchronous generator(VSG)can provide inertial support through renewables and energy storage.It generally operates in parallel with a diesel generator(DSG)in an islanded microgrid.However,unforeseen interactive power oscillations occur in the paralleled system when loads fluctuate.These may also burn out the VSG owing to its low overcurrent capacity.The mechanism and suppression strategy of the power oscillation of a VSG-DSG paralleled system are investigated.It reveals that the interactive power oscillation is caused essentially by the physical difference and parameter mismatch between the VSG and DSG.Then,the elimination condition of oscillation generation is derived.Subsequently,a comprehensive suppression control strategy based on virtual inductance and dynamic mutual damping technology is proposed.Finally,the experimental results verify the effectiveness of the proposed method.展开更多
A new modified extended state observer(MESO)-based robustness voltage sliding mode control(SMC)strategy is proposed for an AC islanded microgrid under system uncertainties including system parameter and load variation...A new modified extended state observer(MESO)-based robustness voltage sliding mode control(SMC)strategy is proposed for an AC islanded microgrid under system uncertainties including system parameter and load variation.First,the disturbance effect on the system is regarded as a lumped uncertainty,and a state space model of the uncertain islanded microgrid system is established.Then,a modified extended state observer is designed to estimate external disturbances and internal perturbation.Finally,considering the lumped uncertainty,a sliding mode controller with a multi-power reaching law is proposed to enable the output voltage of the system to track its reference voltage rapidly and accurately,and to enhance the robustness of the system.The simulation results confirm that the proposed robustness voltage control strategy can perform satisfactory voltage control and demonstrate a strong capability to reject parameter and load variation.展开更多
Renewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals,i.e.‘Affordable and clean energy’.However,experts may be faced with the challenge of selecting th...Renewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals,i.e.‘Affordable and clean energy’.However,experts may be faced with the challenge of selecting the best one for the electrification of an area.To avoid the challenge and realize the ultimate goal of the United Nations,the present study,therefore,proposes a novel pros-pect theory-based decision-making approach to help experts in opting for the best microgrid scenario.The proposed decision-making framework considers the risk appetite of the decision-maker,a quintessential aspect of the process.Linear diophantine uncertain lin-guistic sets are used to model the linguistic evaluations from the experts.The information from different experts is aggregated using a linear diophantine uncertain linguistic power Einstein-weighted geometric operator.Finally,the prospect-theory-based TOmada de Decisao Interativa Multicriterio approach is employed to evaluate the performance of the available microgrid scenarios and hence opt for the best microgrid scenario.The proposed framework has been used to evaluate the performance of seven possible microgrid scenarios and hence select the best one that can be implemented for rural electrification of a remote village in Assam,India.The microgrid scenario consisting of a photovoltaic-wind turbine-fuel cell-battery converter(MG_(3))has been revealed to be the best scen-ario among the seven considered microgrid scenarios.The validity of the obtained ranking results has been adjudged through a com-prehensive evaluation regarding the attenuation factor and the weights of the criteria.Moreover,previous case studies have also been solved using the proposed methodology and the results reveal a good correlation between the obtained ranking results.展开更多
The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency control.To alleviate the influence caused...The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency control.To alleviate the influence caused by load fluctuations and inherent variability of renewable sources,this article proposes an optimised robust proportional-integralderivation(PID)frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID controller.During the process of iterated optimisation,a weighted objective function is used to balance the tracking error performance,robust stability and disturbance attenuation performance.Then,the robust PID frequency(RPIDF)controller is determined by an adaptive constrained population extremal optimisation algorithm based on self-adaptive penalty constraint-handling technique.The proposed control method is examined on a typical islanded microgrid,and the control performance is evaluated under various disturbances and parametric uncertainties.Finally,the simulation results indicate that the fitness value of the proposed method is 1.7872,which is lower than 2.9585 and 3.0887 obtained by two other evolutionary algorithms-based RPIDF controllers.Moreover,the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.展开更多
For providing a reliable power supply in an islanded AC microgrid,it is crucial to ensure that the operating states meet the safety constraints.However,conventional distributed secondary control methods often struggle...For providing a reliable power supply in an islanded AC microgrid,it is crucial to ensure that the operating states meet the safety constraints.However,conventional distributed secondary control methods often struggle to handle voltage and frequency constraints,limiting their practical applicability.To address these limitations,this paper proposes a novel prescribed performance control(PPC)based distributed secondary coordination method for islanded AC microgrids.The proposed method introduces a systematic framework that integrates bijective transformation with distributed secondary control.In this framework,the constrained distributed voltage and frequency control problems can be transformed into unconstrained ones,enabling strict adherence to predefined performance boundaries.Additionally,Lyapunov-Krasovskii functional analysis is employed in this paper to ensure asymptotic stability and calculate the stable boundaries in the transformed error space under time-delay conditions.The effectiveness of stability recovery and prescribed performance is tested in both MATLAB/Simulink and RTLab-based hardware-in-the-loop(HIL)experimental environments.展开更多
The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significan...The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.展开更多
An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overloa...An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.展开更多
基金financial support from the Swedish International Development Cooperation Agency(SIDA)through the research capacity-building program between Addis Ababa University and Swedish universities.
文摘This paper introduces a novel hybrid optimization algorithm,Adaptive Hybrid PSO-Embedded GA(AHPEGA),which dynamically adapts to optimization performance by integrating Particle Swarm Optimization(PSO)and Genetic Algorithms(GA).The primary objective is to enhance the neuroevolutionary training of multilayer perceptron-based controllers(MLPCs)through the joint optimization of model parameters and structural hyperparameters.Traditional training methods frequently encounter issues such as premature convergence and limited generalization.AHPEGA addresses these limitations through an adaptive training strategy that dynamically adjusts parameters during the evolutionary process,thereby improving convergence speed and solution quality.By effectively reducing entrapment in local minima and balancing exploration and exploitation,AHPEGA improves the quality of neural controller design.The algorithm’s performance is evaluated against conventional optimization methods,demonstrating significant improvements in accuracy,convergence speed,and consistency across multiple runs.The practical applicability of the proposed method is demonstrated through simulation in the context of a VSC-based islanded microgrid(MG),where ensuring reliable and effective control under variable operating conditions is critical.This highlights AHPEGA’s capability to optimize intelligent control strategies in MG systems,particularly under dynamic and uncertain conditions,reinforcing its practical value in real-world energy environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22B20104,52407080,52277090,52207097)the International Science and Technology Cooperation Program of China(Grant No.2022YFE0129300)+2 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.2023RC3102)the Excellent Innovation Youth Program of Changsha of China(Grant No.kq2209010)the Key Research and Development Program of Hunan Province(Grant No.2023GK2007)。
文摘Islanded microgrids(IMGs)offer a viable and efficient energy self-sustaining solution for distributed resources in remote areas.While without utility grid support,the frequency of IMG is susceptible to mismatches between demand and generation.Moreover,IMGs encounter uncertain and nonlinear load disturbances together with system parameter perturbation,which further compromises frequency stability.To this aim,this paper proposes a robust multi-virtual synchronous generators(multi-VSGs)coordinated control strategy for distributed secondary frequency regulation(DSFR)in IMGs,which exhibits minimal model dependency and avoids reliance on global information.Two critical methods are developed:(1)a robust VSG control framework that incorporates the linear active disturbance rejection control(LADRC)technique,which enables the estimation and effective elimination of uncertain load disturbances and system's parameter perturbations;(2)a novel secondorder consensus algorithm-based control law for robust secondary frequency regulation,which is featured with proper power sharing among different participants,suppressed power oscillation caused by response disparities,and reduced reliance on complex communication system.Building on methods(1)and(2),a novel multi-VSGs coordinated control strategy is proposed,providing a robust solution for IMG's frequency restoration,and its dynamic characteristics are explored in detail.The correctness and effectiveness of the proposal are verified by both simulation and the hardware-in-the-loop(HIL)experiment results across typical scenarios.
文摘This survey paper provides a critical overview of optimization formulations for planning and operation of islanded microgrids,including optimization objectives,constraints,and control variables.The optimization approaches reviewed address methods both for increasing the resiliency of advanced distribution systems and electrification of remote communities.This paper examines over 120 individual optimization studies and discovers that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions,14 constraints,and 13 control variables.Each of the objectives,constraints,and variables are discussed exhaustively both from the perspective of their importance to islanded microgrids and chronological trends in their popularity.
基金supported by the Science and Technology Project of SGCC under grant 5400-202219417A-2-0-ZN.
文摘The high proportion of nonlinear and unbalanced loads results in power quality issues in islanded microgrids.This paper presents a novel control strategy for harmonic and unbalanced power allocation among distributed genera-tors(DGs)in microgrids.Different from the existing sharing strategies that allocate the harmonic and unbalanced power according to the rated capacities of DGs,the proposed control strategy intends to shape the lowest output impedances of DGs to optimize the power quality of the microgrid.To achieve this goal,the feasible range of virtual impedance is analyzed in detail by eigenvalue analysis,and the findings suggest a simultaneous adjustment of real and imaginary parts of virtual impedance.Because virtual impedance is an open-loop control that imposes DG to the risk of overload,a new closed-loop structure is designed that uses residual capacity and absorbed power as feedback.Accordingly,virtual impedance can be safely adjusted in the feasible range until the power limit is reached.In addi-tion,a fuzzy integral controller is adopted to improve the dynamics and convergence of the power distribution,and its performance is found to be superior to linear integral controllers.Finally,simulations and control hardware-in-the-loop experiments are conducted to verify the effectiveness and usefulness of the proposed control strategy.
基金supported by the National Key Research and Development Program of China(Basic Research Class)(2017YFB0903000)the National Natural Science Foundation of China(U1909201).
文摘For islanded microgrids(MGs),distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs.However,distributed control with successive communication restricts the efficiency and resilience of the control system.To address this issue,this paper proposes a distributed event-triggered control strategy for the frequency secondary control in islanded MGs.The proposed event-triggered control is Zeno behavior free and enables each DG to update and propagate its state to neighboring DGs only when a specific“event”occurs,which significantly reduces the communication burden.Compared with the existing event-triggered control,a trigger condition checking period of the proposed event-triggered control is provided to reduce the computation burden when checking the trigger condition.Furthermore,using the aperiodicity and intermittent properties of the communication,a simple detection principle is proposed to detect and isolate the compromised communication links in a timely and economic fashion,which improves the resilience of the system against FDI attacks.Finally,the control effectiveness of the proposed control scheme is validated by the simulation results of the tests on an MG with 4 DGs.
基金supported by the Natural Sciences and Engineering Research Council(NSERC)of Canada and Early Researcher Award,Ontario Government,Canada.
文摘Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.
文摘Load flow analysis is a significant tool for proper planning,operation,and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency.However,due to the absence of a slack bus in an islanded microgrid,modified load flow algorithms should be adopted considering the system frequency as one of the solution variables.This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids.Several nature-inspired algorithms such as genetic algorithm(GA),differential evolution(DE),flower pollination algorithm(FPA),and grasshopper optimization algorithm(GOA)are separately merged with imperialistic competitive algorithm(ICA)to form four hybrid algorithms named as ICGA,ICDE,ICFPA,and ICGOA.Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system.A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software.From the statistical analysis,it is identified that on an average,ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.
基金supported in part by the National Natural Science Foundation of China(62173218).
文摘In this study,a novel observer-based scalable control scheme for large-scale systems(LSSs)with several interconnected subsystems is explored.Firstly,a scalable observer-based controller is designed to address complex situations where system states are difficult to measure directly.Secondly,unlike the limited cascade and ring topology connections in previous results,this study considers a universal arbitrary topology.Furthermore,it is noteworthy that the plug-and-play(PnP)capability of LSSs is guaranteed thanks to the proposed scalable scheme.Specifically,when subsystems are added or removed,only the controller gains of directly connected neighbors need updating,eliminating the need to redesign the entire system.Moreover,by choosing a Lyapunov-Krasovskii function with a quadratic matrix-valued polynomial,sufficient conditions are deduced to guarantee the global exponential stability with the desired extended dissipative performance for the resulting LSSs.Finally,the effectiveness of the employed scheme is verified by numerical and microgrid examples.
文摘This work presents a novel coordinated control strategy of a hybrid photovoltaic/battery energy storage(PV/BES) system. Different controller operation modes are simulated considering normal, high fluctuation and emergency conditions. When the system is grid-connected, BES regulates the fluctuated power output which ensures smooth net injected power from the PV/BES system. In islanded operation, BES system is transferred to single master operation during which the frequency and voltage of the islanded microgrid are regulated at the desired level. PSCAD/EMTDC simulation validates the proposed method and obtained favorable results on power set-point tracking strategies with very small deviations of net output power compared to the power set-point. The state-of-charge regulation scheme also very effective with SOC has been regulated between 32% and 79% range.
基金supported by the Ministry of Education(MOE),Republic of Singapore,under grant(AcRFTIER-1 RT11/22)。
文摘In this paper,an optimal secondary control strategy is proposed for islanded AC microgrids considering communi-cation time-delays.The proposed method is designed based on the data-driven principle,which consists of an offine training phase and online application phase.For offline training,each control agent is formulated by a deep neural network(DNN)and trained based on a multi-agent deep reinforcement learning(MA-DRL)framework.A deep deterministic policy gradient(DDPG)algorithm is improved and applied to search for an optimal policy of the secondary control,where a global cost function is developed to evaluate the overall control performance.In addition,the communication time-delay is introduced in the system to enrich training scenarios,which aims to solve the time-delay problem in the secondary control.For the online stage,each controller is deployed in a distributed way which only requires local and neighboring information for each DG.Based on this,the well-trained controllers can provide optimal solutions under load variations,and communication time-delays for online applications.Several case studies are conducted to validate the feasibility and stability of the proposed secondary control.Index Terms-Communication time-delay,global cost function,islanded AC microgrid,multi-agent deep reinforcement learning(MA-DRL),secondary control.
基金supported by the ASPIRE Virtual Research Institute Program,Advanced Technology Research Council(No.VRI20-07),UAE.
文摘The limitation of fault currents from converter based distributed generators(CBDGs)in hybrid AC/DC islanded microgrids poses a significant challenge for microgrid protection.This paper presents a novel interharmonic current differential protection scheme for the AC side of hybrid AC/DC islanded microgrids supplied by CBDGs.During faults,the proposed scheme exploits the varying interharmonic components of the currents at both terminals of the faulted line,arise due to variations in the droop-based no-load frequency limits of the interlinking converters(ICs)and the CBDGs.By leveraging these variations,the scheme effectively detects and isolates internal faults within the AC sub-grid,enhancing system reliability.The effectiveness of the suggested scheme is assessed using an enhanced IEEE33-bus hybrid AC/DC microgrid modelled in PSCAD/EMTDC,demonstrating its ability to reliably detect and isolate faults under various operating conditions.Additionally,the scheme is further evaluated using a real-time hardware-in-the-loop experimental setup implemented on an RTDS platform,validating its practical applicability.The simulation and experimental results validate that the presented protection scheme accurately discriminates between normal and faulty conditions across various fault locations,types,and resistance values.This discrimination is achieved without requiring high-bandwidth communication,overcoming a key limitation of existing protection schemes and improving feasibility in real-world deployments.
基金supported by the Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia(No.GPIP:676-135-2024).
文摘This paper employs artificial intelligence and machine learning techniques to predict the dominant oscillation modes in AC microgrids.The dominant modes are highly dependent on the droop gains and only slightly affected by the loading conditions.This paper utilizes the least absolute shrinkage and select operator(LASSO)algorithm to extract the key features contributing directly to dominant modes.The adaptive neuro-fuzzy inference system(ANFIS)is employed as a nonlinear regression technique to train a model that relates the system’s key features to the dominant modes of the AC microgrid.The data obtained from a 6-bus AC microgrid test system is used to train the LASSO-based ANFIS model.The results show that the proposed method can substantially reduce the data volume of the training set due to LASSO sparse feature.The precision of the proposed algorithm is de-termined by comparing its output to the modes deter-mined by the derived small-signal model of the system.
基金This work was supported by the National Natural Science Foundation of China(No.51777077)the Natural Science Foundation of Guangdong Province(No.2017A030313304).
文摘Due to the lack of support from the main grid,the intermittency of renewable energy sources(RESs)and the fluctuation of load will derive uncertainties to the operation of islanded microgrids(IMGs).It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs.With the high penetration of RESs,it faces both economic and environmental challenges if we only use spinning reserve for reserve support.To solve these problems,a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation,load,and storage.The operation risk due to reserve shortage is modeled by the conditional value-at-risk(CVaR)method.The correlation of input variables is considered for the forecasting error modeling of RES and load,and Latin hypercube sampling(LHS)is adopted to generate the random scenarios of the forecasting error,so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches.Furthermore,an optimal day-ahead scheduling model of joint energy and reserve considering riskbased reserve decision is established to coordinate the security and economy of the operation of IMGs.Finally,the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.
基金Supported by the Science and Technology Project of China Southern Power Grid(ZBKJXM20180211).
文摘A virtual synchronous generator(VSG)can provide inertial support through renewables and energy storage.It generally operates in parallel with a diesel generator(DSG)in an islanded microgrid.However,unforeseen interactive power oscillations occur in the paralleled system when loads fluctuate.These may also burn out the VSG owing to its low overcurrent capacity.The mechanism and suppression strategy of the power oscillation of a VSG-DSG paralleled system are investigated.It reveals that the interactive power oscillation is caused essentially by the physical difference and parameter mismatch between the VSG and DSG.Then,the elimination condition of oscillation generation is derived.Subsequently,a comprehensive suppression control strategy based on virtual inductance and dynamic mutual damping technology is proposed.Finally,the experimental results verify the effectiveness of the proposed method.
文摘A new modified extended state observer(MESO)-based robustness voltage sliding mode control(SMC)strategy is proposed for an AC islanded microgrid under system uncertainties including system parameter and load variation.First,the disturbance effect on the system is regarded as a lumped uncertainty,and a state space model of the uncertain islanded microgrid system is established.Then,a modified extended state observer is designed to estimate external disturbances and internal perturbation.Finally,considering the lumped uncertainty,a sliding mode controller with a multi-power reaching law is proposed to enable the output voltage of the system to track its reference voltage rapidly and accurately,and to enhance the robustness of the system.The simulation results confirm that the proposed robustness voltage control strategy can perform satisfactory voltage control and demonstrate a strong capability to reject parameter and load variation.
文摘Renewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals,i.e.‘Affordable and clean energy’.However,experts may be faced with the challenge of selecting the best one for the electrification of an area.To avoid the challenge and realize the ultimate goal of the United Nations,the present study,therefore,proposes a novel pros-pect theory-based decision-making approach to help experts in opting for the best microgrid scenario.The proposed decision-making framework considers the risk appetite of the decision-maker,a quintessential aspect of the process.Linear diophantine uncertain lin-guistic sets are used to model the linguistic evaluations from the experts.The information from different experts is aggregated using a linear diophantine uncertain linguistic power Einstein-weighted geometric operator.Finally,the prospect-theory-based TOmada de Decisao Interativa Multicriterio approach is employed to evaluate the performance of the available microgrid scenarios and hence opt for the best microgrid scenario.The proposed framework has been used to evaluate the performance of seven possible microgrid scenarios and hence select the best one that can be implemented for rural electrification of a remote village in Assam,India.The microgrid scenario consisting of a photovoltaic-wind turbine-fuel cell-battery converter(MG_(3))has been revealed to be the best scen-ario among the seven considered microgrid scenarios.The validity of the obtained ranking results has been adjudged through a com-prehensive evaluation regarding the attenuation factor and the weights of the criteria.Moreover,previous case studies have also been solved using the proposed methodology and the results reveal a good correlation between the obtained ranking results.
基金Key-Area Research and Development Program of Guangdong Province,Grant/Award Number:2020B0101090004National Natural Science Foundation of China,Grant/Award Number:61972288Natural Science Foundation of Shanghai,Grant/Award Number:20ZR1402800。
文摘The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency control.To alleviate the influence caused by load fluctuations and inherent variability of renewable sources,this article proposes an optimised robust proportional-integralderivation(PID)frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID controller.During the process of iterated optimisation,a weighted objective function is used to balance the tracking error performance,robust stability and disturbance attenuation performance.Then,the robust PID frequency(RPIDF)controller is determined by an adaptive constrained population extremal optimisation algorithm based on self-adaptive penalty constraint-handling technique.The proposed control method is examined on a typical islanded microgrid,and the control performance is evaluated under various disturbances and parametric uncertainties.Finally,the simulation results indicate that the fitness value of the proposed method is 1.7872,which is lower than 2.9585 and 3.0887 obtained by two other evolutionary algorithms-based RPIDF controllers.Moreover,the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.
基金supported by the National Natural Science Foundation of China(No.52407086)the China Postdoctoral Science Foundation(No.2023M740588)+1 种基金the Postdoctoral Fellowship Program of CPSF(No.GZC20230422)the Jiangsu Funding Program for Excellent Postdoctoral Talent(No.2023ZB391).
文摘For providing a reliable power supply in an islanded AC microgrid,it is crucial to ensure that the operating states meet the safety constraints.However,conventional distributed secondary control methods often struggle to handle voltage and frequency constraints,limiting their practical applicability.To address these limitations,this paper proposes a novel prescribed performance control(PPC)based distributed secondary coordination method for islanded AC microgrids.The proposed method introduces a systematic framework that integrates bijective transformation with distributed secondary control.In this framework,the constrained distributed voltage and frequency control problems can be transformed into unconstrained ones,enabling strict adherence to predefined performance boundaries.Additionally,Lyapunov-Krasovskii functional analysis is employed in this paper to ensure asymptotic stability and calculate the stable boundaries in the transformed error space under time-delay conditions.The effectiveness of stability recovery and prescribed performance is tested in both MATLAB/Simulink and RTLab-based hardware-in-the-loop(HIL)experimental environments.
基金funded by the National Natural Science Foundation of China(Grant/Award Numbers:52177107 and 52222704)Science and Technology Project of Tianjin Municipality,China(22JCZDJC00780).
文摘The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.
基金supported in part by National Natural Science Foundation of China under Grant 52125705in part by National Natural Science Foundation of China under Grant 52107194.
文摘An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.
