Abstract:In this paper, a robust method for quantifying the impact of short-circuit faults on microgrids is proposed. Microgrids can operate in both islanded (grid-forming) and grid-connected (grid-following) modes, and the ownership and responsibility for the microgrid operation can vary significantly from distribution system operators (DSOs) to third-party microgrid operators. This necessitates the development of a robust short-circuit calculation (SCC) method that can provide accurate results for all the possible microgrid topologies, operational modes, and ownership models. Unlike previously developed SCC methods for microgrids, the SCC method proposed in this paper provides highly accurate results for all possible microgrid topologies: islanded microgrid, grid-connected microgrid, and utility microgrid as a part of a larger distribution grid. In addition, the proposed SCC method solves the short-circuit faults of any complexity, with the same simplicity. The proposed SCC method is tested on a complete model of a real-life microgrid on the Case Western Reserve University campus, operating in both islanded and grid-connected modes. The computational results show the advantages of the proposed SCC method in comparison to the previous ones for microgrids, regarding the robustness (ability to solve complex short-circuit faults with an arbitrary number of faulted buses and phases that affect a microgrid of any topology), as well as the accuracy of the results.

Abstract:Photovoltaic (PV) inverter, as a promising voltage/var control (VVC) resource, can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage. Meanwhile, active power plays a significant role in microgrid voltage profile. Price-based demand response (PBDR) can shift load demand via determining time-varying prices, which can be regarded as an effective means for active power shifting. However, due to the different characteristics, PBDR and inverter-based VVC lack systematic coordination. Thus, this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method, including day-ahead PBDR price scheduling, hour-ahead reactive power dispatch of PV inverters, and real-time local droop control of PV inverters. Considering their mutual influence, a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two stages. To solve the bilinear constraints of droop control function, the problem is reformulated into a second-order cone programming relaxation model. Then, the concave constraints are convexified, forming a penalty convex-concave model for feasible solution recovery. Lastly, a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty model. The proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other methods. The results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.

Abstract:Traditional virtual synchronous generator (VSG) suffers from frequency steady-state deviation in islanded microgrids, which negatively affects the frequency-sensitive loads. Moreover, similar to the synchronous generator, VSG introduces active power oscillation, especially under the condition of multiple parallel VSGs, which may cause overload or damage to the VSG because of its low overcurrent capability as a power electronic inverter. To address these issues, a decentralized frequency restoration and power oscillation damping control method is proposed in this paper, in which the global variable characteristic of the microgrid frequency is considered to restore it to the rated value while ensuring precise active power sharing. Moreover, the proposed control method can dampen the power oscillation during load disturbance without affecting the steady-state characteristics. In addition, the fully decentralized manner obviates the requirement for communication networks, thereby considerably reducing the communication burden and improving system reliability. Finally, simulations and experiments are conducted to validate the effectiveness of the proposed control method.

Abstract:The large-scale integration of renewable energy sources, such as wind power and solar power, into the power system has significantly transformed its characteristics. The issue of sub-synchronous oscillation (SSO) becomes increasingly prominent, severely impacting the system stability. As the wind turbines vary in structures and parameters, existing model-based SSO suppression approaches do not fully consider wind turbine differences and multi-mode oscillation frequencies. To address these issues, this paper proposes a decentralized SSO suppression controller for doubly-fed induction generator (DFIG)-based wind farms using periodic updating data-enabled predictive control (PUDeePC) approach. Firstly, to better adapt to the time-varying system and external disturbance, a periodic updating algorithm is proposed incorporating anomaly detection. The stability of the PUDeePC approach is theoretically validated, and its robustness to variations and disturbances is qualitatively analyzed. Finally, the effectiveness of the PUDeePC approach is revealed through numerical simulations under various conditions, including compensation level variation, wind power output variation, number of online DFIG variations, multi-mode SSOs, and asynchronous PUDeePC approach.

Abstract:In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is an opportunity to include renewable resources in the energy mix. This paper develops an optimization model to determine the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a hybrid microgrid. By running a k-means clustering algorithm on a meteorological dataset of the community under study, the hourly representative values become input parameters in the proposed optimization model. The method for the optimal design of hybrid microgrid is analyzed in six operating scenarios considering ① 24-hour continuous power supply; ② load shedding percentage; ③ diesel power generator (genset) curtailment; ④ the worst meteorological conditions; ⑤ the use of renewable energy sources including battery energy storage systems (BESSs)； and ⑥ the use of genset. A mathematical programming language (AMPL) tool is used to find solutions of the proposed optimization model. Results show that the total costs of microgrid in the scenarios that cover 100% of the load demand (without considering the scenario with 100% renewables) increase by over 16% compared with the scenario with genset operation limitation. For the designs with power supply restrictions, the total cost of microgrid in the scenario with load shedding is reduced by over 27% compared with that without load shedding.

Abstract:Power converters and their interfacing networks are often treated as modular state-space blocks for small-signal stability studies in microgrids; they are interconnected by matching the input and output states of the network and converters. Virtual resistors have been widely used in existing models to generate a voltage for state-space models of the network that require voltage inputs. This paper accurately quantifies the adverse impacts of adding the virtual resistance and proposes an alternative method for network modelling that eliminates the requirement of the virtual resistor when interfacing converters with microgrids. The proposed nonlinear method allows initialization, time-domain simulations of the nonlinear model, and linearization and eigenvalue generation. A numerically linearized small-signal model is used to generate eigenvalues and is compared with the eigenvalues generated using the existing modelling method with virtual resistances. Deficiencies of the existing method and improvements offered by the proposed modelling method are clearly quantified. Electromagnetic transient (EMT) simulations using detailed switching models are used for validation of the proposed modelling method.

Abstract:This paper introduces a distributed secondary control scheme for achieving current sharing and average voltage regulation objectives in a DC microgrid. The proposed scheme employs a dynamic diffusion algorithm (DDA) instead of the consensus algorithm to enable distributed communication among converters. To help understand DDA, the relation of DDA and other diffusion algorithms is discussed in detail and its superiority is shown by comparison with diffusion and consensus algorithms. Furthermore, considering the discrete nature and different sampling time of the digital controller and communication network, a z-domain model of the entire DC microgrid is established. The influence of communication and secondary control parameters on the system stability is investigated. Based on the established model, the tolerable communication rates are obtained. Real-time simulations conducted on the OPAL-RT platform validate the effectiveness of the proposed scheme, showcasing its advantages in terms of convergence speed and stability.

Abstract:DC microgrids (DCMGs) are made up of a network of sources and loads that are connected by a number of power electronic converters (PECs). The increase in the number of these PECs instigates major concerns in system stability. While interconnecting the microgrids to form a cluster, the system stability must be ensured. This paper proposes a novel step-by-step system matrix building (SMB) algorithm to update the system matrix of an existing DCMG cluster when a new microgrid is added to the cluster through a distribution line. The stability of the individual DCMGs and the DCMG cluster is analyzed using the eigenvalue method. Further, the particle swarm optimization (PSO) algorithm is used to retune the controller gains if the newly formed cluster is not stable. The simulation of the DCMG cluster is carried out in MATLAB/Simulink environment to test the proposed algorithm. The results are also validated using the OP4510 real-time simulator (RTS).

Abstract:In the realm of microgrid (MG), the distributed load frequency control (LFC) system has proven to be highly susceptible to the negative effects of false data injection attacks (FDIAs). Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG, this paper proposes a detection and defense method against unobservable FDIAs in the distributed LFC system. Firstly, the method integrates a bi-directional long short-term memory (BiLSTM) neural network and an improved whale optimization algorithm (IWOA) into the LFC controller to detect and counteract FDIAs. Secondly, to enable the BiLSTM neural network to proficiently detect multiple types of FDIAs with utmost precision, the model employs a historical MG dataset comprising the frequency and power variances. Finally, the IWOA is utilized to optimize the proportional-integral-derivative (PID) controller parameters to counteract the negative impacts of FDIAs. The proposed detection and defense method is validated by building the distributed LFC system in Simulink.

Abstract:A reliable and robust communication network is essential to exchange information between distributed generators (DGs) and accurately calculate their control actions in microgrids (MGs). However, the integration of the communication network and MGs poses challenges related to the flexibility, availability, and reliability of the system. Furthermore, random communication disorders such as time delays and packet loss can negatively impact the system performance. Therefore, it is essential to design a suitable secondary controller (SC) with a fast dynamic response to restore voltage and appropriate power-sharing, while ensuring that the effects of random communication disorders are eliminated. In this regard, an optimal distributed hybrid model predictive secondary control method is presented in this paper. Realistic simulations are carried out in a mixed simulation environment based on MATLAB and OMNET++, by considering IEEE 802.11 (WiFi) using the recently developed Internet networking (INET) framework. In the implemented application layer, the recoveryUnit is responsible for reducing the impact of random communication disorders. The effectiveness and performance of the proposed method in comparison with a conventional model predictive control are verified by simulation results.

Abstract:In an autonomous droop-based microgrid, the system voltage and frequency (VaF) are subject to deviations as load changes. Despite the existence of various control methods aimed at correcting system frequency deviations at the secondary control level without any communication network, the challenges associated with these methods and their abilities to simultaneously restore microgrid VaF have not been fully investigated. In this paper, a multi-input multi-output (MIMO) model reference adaptive controller (MRAC) is proposed to achieve VaF restoration while accurate power sharing among distributed generators (DGs) is maintained. The proposed MRAC, without any communication network, is designed based on two methods: droop-based and inertia-based methods. For the microgrid, the suggested design procedure is started by defining a model reference in which the control objectives, such as the desired settling time, the maximum tolerable overshoot, and steady-state error, are considered. Then, a feedback-feedforward controller is established, of which the gains are adaptively tuned by some rules derived from the Lyapunov stability theory. Through some simulations in MATLAB/SimPowerSystem Toolbox, the proposed MRAC demonstrates satisfactory performance.

Abstract:In this paper, we apply a model predictive control based scheme to the energy management of networked microgrid ， which is reformulated based on column generation. Although column generation is effective in alleviating the computational intractability of large-scale optimization problems, it still suffers from slow convergence issues, which hinders the direct real-time online implementation. To this end, we propose a graph neural network based framework to accelerate the convergence of the column generation model. The acceleration is achieved by selecting promising columns according to certain stabilization method of the dual variables that can be customized according to the characteristics of the microgrid. Moreover, a rigorous energy management method based on the graph neural network accelerated column generation model is developed, which is able to guarantee the optimality and feasibility of the dispatch results. The computational efficiency of the method is also very high, which is promising for real-time implementation. We conduct case studies to demonstrate the effectiveness of the proposed energy management method.

Abstract:The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals, i.e., sharing disturbance mitigation among all controllable assets to even their burden. However, limited by neighboring communication, the time-consuming peer-to-peer coordination of the droop-free control slows down the nodal convergence to global consensus, reducing the power-sharing efficiency as the number of nodes increases. To this end, this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes, in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed. A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes, which also enables the autonomous switching between local and global power-sharing modes according to the system states. Systematic guidance for key control parameter designs is derived via the optimal control methods, by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to consensus. System stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct method. Moreover, simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode transitions. Moreover, compared with the classical global power-sharing scheme, the proposed scheme presents promising benefits in convergence speed and scalability.

Abstract:The effects of nonlinear loads on voltage quality represent an emerging concern for islanded microgrids. Existing research works have mainly focused on harmonic power sharing among multiple inverters, which ignores the diversity of different inverters to mitigate harmonics from nonlinear loads. As a result, the voltage quality of microgrids cannot be effectively improved. To address this issue, this study proposes an adaptive harmonic virtual impedance (HVI) control for improving voltage quality of microgrids. Based on the premise that no inverter is overloaded, the main objective of the proposed control is to maximize harmonic power absorption by shaping the lowest output impedances of inverters. To achieve this, the proposed control is utilized to adjust the HVI of each inverter based on its operation conditions. In addition, the evaluation based on Monte Carlo harmonic power flow is designed to assess the performance of the proposed control in practice. Finally, comparative studies and control-in-the-loop experiments are conducted.

Abstract:This paper focuses on the distributed control problem in a networked microgrid (NMG) with heterogeneous energy storage units (HESUs) in the environment considering multiple types of time delays, which include the state, input, and communication delays. To address this problem, a state feedback control (SFC) strategy based on nested predictor is proposed to mitigate the influence of multiple types of time delays. First, a distributed control method founded upon voltage observer is developed, which can realize proportional power distribution according to the state of charge (SOC) of the HESUs, while adjusting the average voltage of the point of common coupling (PCC) bus in the NMG to its rated value. Then, considering that there exists steady-state error resulting from the initial value of the observer and impact of time delays, an SFC strategy is proposed to further improve the robustness of the NMG against time delays. Finally, the experimental results demonstrate that the proposed distributed control method is capable of fully compensating for the state, input, and communication delay. Moreover, the NMG exhibits remarkable resistance to multiple types of time delays, which has higher reliability and robustness.