Islanded Microgrid Research Articles

Deep Q-Learning-Based Mud Ring Optimization Approach for Optimal Power Flow in Islanded DC Microgrid

Microgrid (MG) is the basic element that recreates a significant role in integrating renewable energy sources (RES). Direct current (DC) MG has advantages over alternating current (AC) for many applications. A DC-MG is connected to the islanded mode through power electronic converters, which are also an important component for RES integration. Due to environmental factors, RES will struggle to load the conditions. Branches of DC-MG have battery energy storage systems (BESSs) installed for compensating the supply–load imbalance. Even though BESS are installed, accurate maintenance is still required to maintain a balanced power flow. This work proposes an adaptive deep Q-learning (DQL) based mud ring optimization technique (DQMR) for optimal power flow in islanded DC-MG. DQL is effective for the task with discrete and low-dimensional action spaces, and mud ring algorithm (MRA) is to clear up large-scale optimization problems and power flow problems. The implementation is done through MATLAB/Simulink model. The error is minimized to 6% by this method, and the losses are also reduced. IEEE-9 bus system and IEEE-30 bus system are used in the proposed work to validate the performance.

Accurate voltage regulation for a DC microgrid using nonlinear state feedback controller

AbstractThe high penetration of distributed generation systems poses challenges in effectively managing both DC bus voltage and power‐sharing in DC microgrids (MGs). To ensure precise voltage regulation across various distributed generation systems and maintain overall system stability, this paper studies the modelling and control of an islanded DC MG described by a set of nonlinear‐nonaffine differential equations. In the proposed DC MG, a battery storage, a photovoltaic (PV) power plant, and a back‐end power converter with DC loads have been connected to a common DC bus. First, a dynamic model of a DC MG was developed. Then, to find the maximum power point of solar PV, a straightforward method based on an artificial neural net is employed. Afterward, a nonlinear local state feedback controller through output regulation theory is applied to achieve voltage stability, overcoming the drawbacks of droop‐based techniques. Moreover, partial state feedback capability simplifies controller design. To verify the effectiveness of the proposed approach, various scenarios, including sudden load variation, illumination change, and changes in the MG configuration, have been examined. Finally, numerical studies were carried out in a simulation environment to demonstrate the superiority of the proposed nonlinear control scheme over constant feedback control.

Enhanced four-port dual-active-bridge converter employing power decoupling capability for DC microgrid islanding mode operation

Enhanced four-port dual-active-bridge converter employing power decoupling capability for DC microgrid islanding mode operation

Virtual Inertia Based SVPN Sequence Control Scheme for Voltage Unbalance Mitigation in Coordination with DSM on a Stand Alone Microgrid

There is a growing proliferation of renewable sources of energy and single-phase loads, which are unevenly distributed across distribution networks. This imbalanced distribution intensifies unfavorable conditions within the network, leading to detrimental impacts on power quality, stability, and reliability. The objective of this paper is to demonstrate the collaborative functioning of a space vector-based positive and negative (SVPN) sequence controller and demand-side management to maintain voltage unbalance factor in the accepted threshold of 2%, as specified by the IEC standard 61000-3-13 in a typical IEEE 33 bus network. The presented approach for managing thermostatically controlled devices also proves to be highly effective in ensuring the thermal comfort of end users. Also, the absence of rotational masses in converter-interfaced microgrids leads to a reduction in system inertia. Any disturbance in the system leads to significant issues, such as an increase in the rate of change of frequency (RoCoF) and poor frequency response. Therefore, virtual synchronous generator is integrated with the SVPN controller to improve the frequency response of the system. The coordinated control scheme of the islanded microgrid has undergone assessment using the MATLAB/Simulink platform. Small signal modeling technique has been applied to the converters based on VSG control, incorporating positive and negative sequence controllers to address the escalating dynamic challenges in power electronics-based microgrids. This enhanced stability analysis facilitates handling the ever-increasing dynamic issues encountered in such microgrids.

Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. This study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

Voltage and frequency control of solar – battery – diesel based islanded microgrid

Abstract Islanded microgrids with low-inertia distributed energy resources (DERs) are prone to frequency fluctuations. With the increasing integration of DERs in microgrids, the complexity of control and stability has also increased. Moreover, the integration of DERs into microgrids may result in a power imbalance between energy supply and demand during sudden changes in load or energy generation. This can cause frequency variations in the microgrid, which could have disastrous consequences such as equipment damage or even blackouts. This paper proposes a control strategy to ensure the efficient operation of an islanded hybrid microgrid consisting of a PV generator, battery energy storage system (BESS), and emergency diesel generator. According to Energy Exchange Model proposed in this paper, the hybrid system presented operates independently without being connected to the electrical grid, where the PV system and BESS act as the main energy sources, while the emergency diesel generator provides active power backup with voltage and frequency regulation. The novel in this paper is also that DER aids in frequency regulation during active power transients by delivering and absorbing active power in accordance with the inverter's suggested P droop control strategy. In this way inverter droop control decreases system frequency nadir emulating so called “synthetic inertia”. To design both the islanded hybrid system and the proposed control strategy, the MATLAB/Simulink environment is utilized. Based on the results, it can be concluded that the analyzed microgrid system is capable of maintaining stability and operating efficiently in a range of operating conditions.

Droop-Based DC Microgrids Analysis and Control Design Using a Weighted Dynamic Aggregation Modeling Approach

In this paper, Weighted Dynamic Aggregation (WD agg) approach is used for modeling, analyzing and control loop design of islanded DC microgrids. The proposed approach models <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$ \textbf {n}$</tex-math></inline-formula></i> DC-DC converters and their controllers with a single equivalent converter and an equivalent control system, which without sacrificing the accuracy, reduces the complexity of such large-scale system studies. It is shown that the model equivalent converter and control system parameters can be determined by the weighted average of the corresponding parameters of the large-scale system. The weight of each converter is quantified based on the contribution of that converter in the overall dynamic behavior of the large-scale system. WD agg model can accurately predict the transient response and can be employed in power planning, stability and sensitivity analyses with high accuracy. It is also shown that the proposed model can be used in designing the controller parameters of the large-scale system to ensure desirable system performance. The accuracy and applications of the proposed WD agg model are evaluated through time-domain simulations, and experiments of an islanded microgrid consisting of three paralleled converters with different control parameters connected to a Constant Power Load (CPL) emulating a challenging system stability case.

Combined static state and frequency estimation in an islanded AC microgrid with limited SCADA measurements

Combined static state and frequency estimation in an islanded AC microgrid with limited SCADA measurements

A New Fractional-Order Virtual Inertia Support Based on Battery Energy Storage for Enhancing Microgrid Frequency Stability

Microgrids have a low inertia constant due to the high penetration of renewable energy sources and the limited penetration of conventional generation with rotating mass. This makes microgrids more susceptible to frequency stability challenges. Virtual inertia control (VIC) is one of the most effective approaches to improving microgrid frequency stability. Therefore, this study proposes a new model to precisely mimic inertia power based on an energy storage system (ESS) that supports low-inertia power systems. The developed VIC model considers the effect of both the DC-DC converter and the DC-AC inverter on the power of the ESS used. This allows for more precise and accurate modeling of the VIC compared to conventional models. Moreover, this study proposes a fractional-order derivative control for the proposed VIC model to provide greater flexibility in dealing with different perturbations that occur in the system. Furthermore, the effectiveness of the proposed fractional-order VIC (FOVIC) is verified through an islanded microgrid that includes heterogeneous sources: a small thermal power plant, wind and solar power plants, and ESSs. The simulation results performed using MATLAB software indicate that the proposed VIC scheme provides fast stabilization times and slight deviations in system frequency compared to the conventional VIC schemes. The proposed VIC outperforms the conventional load frequency control by about 80% and the conventional VIC model by about 45% in tackling load/RESs fluctuations and system uncertainty. Additionally, the studied microgrid with the proposed FOVIC scheme is noticeably more stable and responds faster than that designed with integer-order derivative control. Thus, the proposed FOVIC scheme gives better performance for frequency stability of low-inertia power systems compared to conventional VIC schemes used in the literature.

Risks assessment on island micro-grids construction schemes employing a fuzzy-MCDM framework

Risks assessment on island micro-grids construction schemes employing a fuzzy-MCDM framework

State-of-charge balancing strategy of battery energy storage units with a voltage balance function for a Bipolar DC mircrogrid

For an islanded bipolar DC microgrid, a special problem of making the better compromise between a state-of-charge (SOC) balance among multiple battery energy storage units (MBESUs) in positive and negative polar, and bus voltage balance, should be considered. In order to solve this problem, three kinds of the simplified load equivalent circuits on the different operation status of voltage balancer (VB) are established; based on these equivalent circuits, the SOC unbalance mechanism is analyzed and the rules of SOC deviation and voltage unbalance factor are summarized, which are influenced by micro-source and load changing; then a SOC automatic balancing strategy among MBESUs with a voltage balance function is proposed in this paper. The proposed controlling strategy takes into consideration the energy flow relationship under various working conditions; with the aim of controlling the equilibrium time and energy flow direction of SOC, creates a SOC balancing power bridge with the SOC deviation as the main control variable; adopts the adaptive virtual resistance algorithm for both positive and negative BESUs according to the variation rule of the SOC deviation. When the SOC deviation is significant, according to the net power difference of the system, the values of the virtual resistances can be adaptively adjusted to alter the operation mode of BESUs and the VB control strategy is also adjusted so as to ensure the stable running of the system and accelerate the SOC balancing speed via maximizing the balancing power by VB. In order to decrease the voltage unbalance on SOC balancing, apart from adding bus voltage recovery control to the positive and negative batteries, the virtual resistances are related to the SOC deviation when the SOC deviation is slight. The simulation results demonstrate that the control strategy proposed in this paper can quickly realize SOC balancing when the SOC deviation is significant, has superior equilibrium performance as load and micro-source power varying when the SOC deviation is slight, and is also feasible in decreasing the bus voltage unbalance.

A non-singular terminal sliding mode controller for a communication-based hybrid microgrid

The paper proposes a robust and effective networked control for a hybrid based islanded microgrid. Renewable energy sources with energy storage systems are linked in parallel using voltage source inverters to supply the loads in a microgrid. The master-slave control strategy is implemented. CAN and the ZigBee networks are selected to share data, where the master controller transmits the reference signals to the slave controllers in the microgrid via these two networks. As the control loop incorporates a communication network and energy storage systems, the impacts of external disturbances, parametric uncertainties, and time delay executed by the devices introduce challenges in control of voltage and current. Thereby, it is crucial to use a robust and stable control approach. Using the binary iteration algorithm, stability criteria in the form of linear matrix inequalities based on the Lyapunov-Krasovskii function are employed to derive the system's maximum allowable delay boundary. As the reliability of the communication network can be affected, a nonsingular terminal sliding mode controller is employed. The proposed robust control method was implemented in MATLAB/Simulink and the True-time 2.0 simulator and its performance were evaluated under different loads, uncertainties, disturbances, and communication impairments. Simulation results show that the desired performance i.e., maintaining the microgrid's stability with accurate voltage and current control even in unallowable delay limits is met.

Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG’s ability to confront communication failure and its ability to work reliably during plug-and-play operations.

Cyber–physical anomaly detection for inverter-based microgrid using autoencoder neural network

Contemporary cyber–physical systems have evolved into highly autonomous and distributed entities, enabled by cutting-edge control frameworks and advanced communication networks. This transformation has empowered these systems to efficiently accommodate inverter-based resources, harnessing the grid’s dynamic potential. However, the increased integration of cyber and communication infrastructure has exposed these systems to vulnerabilities stemming from both cyber and physical disturbances. Power system errors in the physical layer of microgrids and malicious attacks on the communication layer pose substantial threats to grid stability and load supply. In response to these challenges, this paper presents an innovative unsupervised learning method for cyber–physical anomaly identification. Leveraging an autoencoder-based neural network, this approach addresses the intricate interplay between the physical and cyber aspects of power system failures and false data injection within the communication network. The method’s efficacy is evaluated on an islanded inverter-based microgrid, representing a realistic and vulnerable testbed. The autoencoder error is introduced as a key performance metric, providing a robust and practical measure of the proposed detection technique’s effectiveness. Simulation results demonstrate the promising utility of this method, positioning it as a valuable tool for enhancing the resilience of contemporary cyber–physical systems against cyber–physical anomalies.

Simultaneous compensation of distorted DC bus and AC side voltage using enhanced virtual synchronous generator in Islanded DC microgrid

ABSTRACT There are many effective techniques for virtual inertia emulation in DC microgrids that can help DC bus voltage stability through power exchange with virtual inertia injection. But one of the vexingly complicated challenges in virtual inertia emulation is the connection of unbalanced loads on the AC side of a DC microgrid. Unbalanced AC loads connected to a DC microgrid may cause severe fluctuations in DC bus voltage and battery power, as well as distorting AC side voltage. The need to solve this issue is very important because it can be a threat to the microgrid DC bus voltage stability and feed sensitive loads. One effective method to mimic the real inertia feature and dampen the unfavourable unbalanced conditions is to employ a virtual synchronous generator (VSG) equipped with a decoupled double synchronous reference frame (DDSRF) approach. The DDSRF can extract positive and negative components with high precision and create pure DC signals for the control system to improve accuracy and controllability. Hence, this paper investigates a combination of a VSG structure enhanced with a DDSRF technique to attenuate the fluctuations of DC bus voltage, battery power, and AC-side voltage caused by an unbalanced AC load in an islanded DC microgrid. The simulation results confirm that the unbalanced loads connected to the AC side of the microgrid are destructive for DC bus voltage, battery power, and also create voltage imbalances for AC loads. Furthermore, the proposed DDSRF-based VSG control system that has been implemented on the AC side of the microgrid can strongly dampen the fluctuations on the DC bus, battery, and AC loads.

“Adaptive virtual synchronous generator control using optimized bang-bang for Islanded microgrid stability improvement”

In this paper, a virtual synchronous generator (VSG) controller is applied to a hybrid energy storage system (HESS) containing a battery energy storage system and supercapacitor storage system for maintaining the frequency stability of an isolated microgrid. The microgrid contains a photovoltaic generation system and a diesel generator in addition to the HESS and two constant impedance loads that are fed through a medium voltage radial feeding system. The adaptive virtual inertia constant (H) with constant virtual damping coefficient (D) based on ‘ bang-bang’ control for the microgrid’s frequency stability enhancement is investigated and compared with the constant parameter VSG. In addition, the bang-bang control is modified to adapt the D beside the adaptive H, and the system response is investigated and compared with the conventional adaptive H technique. The VSG parameters are evaluated based on two different methods. The first is a computational method based on the simplified small signal stability analysis, while the other is based on an optimization method using two different objective functions and the particle swarm optimization technique. This paper also investigates the superiority of the proposed technique compared to other techniques in enhancing frequency stability, accelerating steady-state frequency restoration, and reducing the energy requirement of the HESS. The required power from the HESS is shared between the two energy storages using the low pass filter technique so as to reduce battery peak current.

Harmonic power sharing control using adaptive virtual harmonic impedance in islanded microgrids

Abstract Existence of nonlinear loads makes load power sharing more challenging in the islanded microgrids. Conventional droop control may cause the poor harmonic power sharing among the distributed generations (DGs) due to the mismatched line impedance. This paper proposes an adaptive regulation method of the virtual harmonic impedance for the accurate harmonic power sharing. Consensus algorithm is used to regulate adaptively the virtual harmonic impedance according to the harmonic power sharing error among the DGs with considering of the mismatched line impedance. This method does not require the prior knowledge of the line impedance. The feasibility of the proposed method is demonstrated by simulation result for achieving the accurate harmonic power sharing to reduce the voltage distortion at the point of common coupling (PCC).

Model-free frequency regulation in islanded microgrids: An event-triggered adaptive dynamic programming approach

This paper is concerned with frequency regulation in islanded microgrids via an event-triggered adaptive dynamic programming method. Comparatively to the existing works, we relax the typical limitations on the system, namely, exactly known models, constant load impedances, and single operation mode. The adopted frequency regulation scheme can reach frequency synchronization within the acceptable bounds in the presence of the model uncertainties, operation mode switching, and time-varying loads with no need for any prior information on the system parameters. By employing an event-triggered scheme, the secondary controller is updated only at the aperiodic triggering instants which contributes to less transmission cost. Besides, the use of measurements can help the adaptive weights to be adjusted online in the approximation process, which allows the proposed controller to adapt to the disturbances in real time. Finally, tests including the islanding process and load variation are performed in MATLAB/SimPowerSystem to show the effectiveness of the proposed method.

Design and FPGA-in-loop based validation of predictive hierarchical control for islanded AC microgrid

This paper proposes a decentralized control approach for the flexible operation of an autonomous AC microgrid (MG). AC MG typically consists of two or more voltage source inverters (VSI), capable of simultaneously regulating the voltage at the point of common coupling (PCC) and meeting the local power demand. The classical linear control techniques attain these functions. However, they have many limitations, such as slow transient response, high sensitivity to the parameter variations, and inability to handle the system nonlinearities. This paper aims to address these issues by presenting an improved finite control set model predictive control (FCS-MPC) approach for inverter-based distributed generation (DG). The proposed scheme tracks the voltage trajectory using cost function (CF) over two-step prediction horizons. The proposed control method is employed for the AC MG having two parallel DGs. Droop control is responsible for the power-sharing between the parallel DGs regardless of impedance mismatch at the distribution level of AC MG. The decentralized secondary control is developed to eliminate the deviation in the voltage and frequency caused due to overlooking the primary control. The proposed control scheme has been validated through extensive simulations and real-time controller hardware in the loop tests using an FPGA ZYBO Z7 board. Moreover, the proposed methodology depicts the enhanced transient response, less computational burden than the classic MPC, and shows robustness to parametric uncertainties in terms of THD than hierarchical linear control. The simulations and experimental results visually represent research outcomes, exhibiting the THD of 0.98 % for different dynamic loads, which is within the limit of IEEE and IEC standards. Furthermore, the proposed controller’s mathematical stability is further supported by analysis based on the Lyapunov stability theory.

Small-signal stability enhancement of islanded microgrids via domain-enriched optimization

A domain-enriched optimization algorithm is developed to enhance the dynamic resilience of islanded microgrids, with a specific emphasis on improving small-signal stability. A novel eigenvalue-oriented objective function and accompanying constraints are designed to optimize the controller parameters for the power-electronic interfaces of distributed energy resources (DERs), which are critically important to the system’s dynamic resilience. To solve the resulting non-smooth and non-convex optimization problem, we introduce an auxiliary loss term based on microgrid domain knowledge, which takes the form of a multivariate polynomial in the optimization variables. We refer to our proposed method as Domain-Enriched Navigation (DEN), which combines the gradient of this domain-enriched loss term as a navigation strategy with an evolutionary algorithm. By incorporating the navigated update, we successfully address initialization sensitivity and slow convergence commonly observed in ordinary evolutionary algorithms. Numerical tests on typical islanded microgrids validate the effectiveness of our approach in enhancing the small-signal stability. Our results demonstrate a notable improvement of over 1 dB in the objective function, along with much faster computation than state-of-the-art alternatives.