Distributed Event-triggered Control for Frequency Restoration in Islanded Microgrids with Reduced Trigger Condition Checking

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.

A novel coordinated control strategy considering power smoothing for a hybrid photovoltaic/battery energy storage system

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.

Power quality enhancement in islanded microgrids via closed-loop adaptive virtual impedance control

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.

Optimal Secondary Control of Islanded AC Microgrids with Communication Time-delay Based on Multi-agent Deep Reinforcement Learning

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.

Distributionally robust optimization configuration method for island microgrid considering extreme scenarios

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.

Review of Energy Management and Planning of Islanded Microgrids

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.

Nature-inspired Hybrid Optimization Algorithms for Load Flow Analysis of Islanded Microgrids

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.

Linear diophantine uncertain linguistic-based prospect theory approach for performance evaluation of islanded microgrid-system scenarios

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.

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

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.

Optimal Day-ahead Scheduling of Islanded Microgrid Considering Risk-based Reserve Decision

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.

MESO-based Robustness Voltage Sliding Mode Control for AC Islanded Microgrid

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.

Interactive Power Oscillation and Its Suppression Strategy for VSG-DSG Paralleled System in Islanded Microgrid

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.

Adaptive constrained population extremal optimisation-based robust proportional-integral-derivation frequency control method for an islanded microgrid

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.