Voltage Control Of Microgrids Research Articles

Distributed Voltage Control for Microgrids Against Time-Varying Communication Delay Interference

Distributed Voltage Control for Microgrids Against Time-Varying Communication Delay Interference

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

A stable output voltage of a boost converter is vital for the appropriate functioning of connected devices and loads in a DC microgrid. Variations in load demands and source uncertainties can damage equipment and disrupt operations. In this study, a modified twin-delayed deep deterministic policy gradient (TD3) algorithm is proposed to regulate the output voltage of a boost converter in a DC microgrid. TD3 optimizes PI controller gains, which ensure system stability by employing a non-negative, fully connected layer. To achieve optimal gains, multi-deep reinforcement learning agents are trained. The agents utilize the error signal to obtain the desired output voltage. Furthermore, a new reward function used in the TD3 algorithm is introduced. The proposed controller is tested under load variations and input voltage uncertainties. Simulation and experimental results demonstrate that TD3 outperforms PSO, GA, and the conventional PI. TD3 exhibits less steady-state error, reduced overshoots, fast response times, fast recovery times, and a small voltage deviation. These findings confirm TD3’s superiority and its potential application in DC microgrid voltage control. It can be used by engineers and researchers to design DC microgrids.

Coordinated Voltage Control of Microgrids With Integrated Electric Vehicles in Real-World Experiment and Application: Industrial Design and Implementation on EUREF-Campus in Berlin

Coordinated Voltage Control of Microgrids With Integrated Electric Vehicles in Real-World Experiment and Application: Industrial Design and Implementation on EUREF-Campus in Berlin

Safe and Stable Secondary Voltage Control of Microgrids Based on Explicit Neural Networks

This paper proposes a novel safety-critical secondary voltage control method based on explicit neural networks (NNs) for islanded microgrids (MGs) that can guarantee any state inside the desired safety bound even during the transient. Firstly, an integrator is introduced in the feedback loop to fully eliminate the steady-state error caused by primary control. Then, considering the impact of secondary control on the stability of the whole system, a set of transient stability and safety constraints is developed. In order to achieve online implementation that requires fast computation, an explicit NN-based secondary voltage controller is designed to cast the time-consuming constrained optimization in the offline NN training phase, by leveraging the local Lipschitzness of activation functions. Specially, instead of using the NN as a black box, the explicit representation of NN is substituted into the closed-loop MG for transferring the stability and safety constraints. Finally, the NN is trained by safe imitation learning, where an optimization problem is formulated by maximizing the imitation accuracy and volume of the stable region while satisfying the stability and safety constraints. Thus, the safe and stable region is approximated that any trajectory initiates within will converge to the equilibrium while bounded by safety conditions. The effectiveness of the proposed method is verified on a prototype MG with detailed dynamics.

A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids

This paper proposes a novel cooperative voltage control strategy for an isolated microgrid based on the multi-agent advantage actor-critic (MA2C) algorithm. The proposed method facilitates the collaborative operation of a distributed energy system (DES) by adopting an attention mechanism to adaptively boost information processing effectiveness through the assignment of importance scores. Additionally, the algorithm we propose, executed through a centralized training and decentralized execution framework, implements secondary control and effectively restores voltage deviation. The introduction of an attention mechanism alleviates the burden of information transmission. Finally, we illustrate the effectiveness of the proposed method through a DES consisting of six energy nodes.

Model predictive real-time architecture for secondary voltage control of microgrids

The integration of new elements and technologies to the power grid, driven by the expansion of electric microgrids, brings various challenges requiring a suitable control architecture for systems to operate ensuring economic and reliable access to electricity. This paper presents the development of a time-varying constraint real-time model predictive secondary voltage control (MPVC) strategy for microgrids built up in a multi-class Python environment with InfluxDB and SQLExpress databases storing the controller and microgrid data, and its experimental implementation with Modbus communication to physical devices at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The microgrid characteristics are described and the experimental setup is presented. The mathematical model of the resource used as case study is obtained through experimental identification, and it is integrated to the MPVC scheme. Polytopic invariant sets are used as terminal sets in the MPVC scheme to ensure stability of the solution, and time-varying constraints of reactive power availability are considered. Validation studies of the proposed control system in computational simulation are presented prior to real application in the UPB campus microgrid. Controller performance results are described for three different implementation scenarios of low, medium and high variability of external disturbances, presenting adequate voltage regulation around the reference value and considerable dependence on the meteorological forecast quality. The model predictive voltage controller is finally benchmarked against a commercial microgrid controller to evaluate its performance, standing out as a more comprehensive and tailor-made solution.

Data-driven distributed voltage control for microgrids: A Koopman-based approach

This paper presents a distributed data-driven control to regulate the voltage in an alternate current microgrid (MG). Following the hierarchical control frame for MGs, a secondary control for voltage is designed with a data-driven strategy using the Koopman operator. The Koopman operator approach represents the nonlinear behavior of voltage as a linear problem in the space of observables or lifted space. The representation in the lifted space is used together with linear consensus to design a model predictive control (MPC). The complete algorithm is proved in an MG model that includes changes in load, transmission lines, and the communication graph. The data-driven model regulates the voltage using a distributed approach based only on local measurements, and includes reactive power constraints and control cost minimization.

Optimal Communication Network Design of Microgrids Considering Cyber-Attacks and Time-Delays

Distributed secondary control stands out for its flexibility and expandability in microgrids (MGs) control, in where communication network plays a fundamental and critical role. The communication topology and link-weights have significant impact on the attack-resilience and dynamic performance of MGs, which should be appropriately designed. However, the joint optimization problem of communication topology and link-weights in the existing literature of consensus-based secondary voltage control of MGs is usually neglected. To bridge this gap, we propose a novel two-stage optimization approach for the communication network design, which jointly optimizes the topological structure to enhance the structural survivability and link weights to improve the dynamic performance (including the speed of convergence and robustness to time-delay). The first stage problem is formulated into a mixed-integer semi-define programming (MISDP) model based on convex relaxation technique, which is then converted equivalently into an integer quadratic programming (IQP) problem and then a MISDP feasibility problem to facilitate the solution. The second stage problem is formulated into a bi-objective SDP model to compromise between the convergence performance and robustness to time-delay. Simulations based on a microgrid with 10 distributed generation (DG) units under different scenarios are implemented to verify the effectiveness of the proposed method.

An approach to arranging primary and secondary control of the operating parameters in microgrids featuring inverter-connected generator sets

Various low-power generator sets (GS) are mainly integrated into microgrids by means of inverters coupled with energy storage systems (ESS). ESSs are connected either to the DC busbars of GSs or to the AC busbars of the microgrid, in this case they are equipped with individual inverters. Use of ESS helps balance power in cases of load surge / loadshedding when the microgrid is islanded. This paper overviews approaches to arranging primary and secondary control of the operating parameters of microgrids. It discusses the technical challenges of, and possible solutions for, implementing primary and secondary control in low- and medium-voltage distribution grids featuring inverter-connected GSs. Calculations of electromechanical transients show that inverter-connected GSs have advantages over conventional GSs when used microgrids that are weakly connected to the power system (the external grid) or islanded. In a microgrid featuring inverter-connected GSs, active and reactive power need to be controlled separately as in power systems featuring conventional GSs; implementation of such controls must be adjusted for the parametric and topological characteristics of low- and medium-voltage distribution grids. To address the issues of linked control, this paper proposes a comprehensive approach that adjusts microgrid design for the specifics of inverter-connected GSs, improves inductance, and incorporates algorithmic solutions. It details upon the virtual impedance algorithm, which extends the common principle of droop speed control. Its implementation helps load the GSs and the ESS with adjustments for their installed capacity; it can also simulate the mechanical constant of GS inertia. Primary control has a low margin that mainly comes from the ESS; thus, backups available to secondary control must be used to compensate for the deviations in operating parameters; this is a key challenge of secondary frequency and voltage control in microgrids. When primary and secondary voltage and frequency controls have been duly implemented in a microgrid, its GSs, the ESS, and electricity delivery to the loads within the microgrid can all operate reliably in a variety of operating situations.

Distributed secondary voltage control of microgrids with actuators bias faults and directed communication topologies: Event‐triggered approaches

AbstractMicrogrid (MG) is an effective means to achieve flexible management and control of distributed power sources. Compared with the traditional centralized secondary control method, the distributed coordinated secondary control strategy has higher flexibility and reliability. Nevertheless, few fault‐tolerant distributed secondary voltage control strategies for isolated MGs considered event‐triggered control mechanisms. This article proposes two event‐triggered fault‐tolerant control (ETFTC) algorithms for voltage restoration in MGs. The algorithms consider the presence of bias faults in the actuators of the distributed generators (DGs). First, an ETFTC algorithm is proposed considering the case where the communication graph is known. The algorithm eliminates the continuous communication constraint with neighboring DGs based on avoiding the constant updating of the controllers. Next, an adaptive ETFTC algorithm that is independent of the global information of the network graph and fault factor boundaries is proposed for the case where the directed topology is unknown. Furthermore, the algorithms can be applied to general linear multi‐agent systems (MASs) tracking control systems. In the end, to evaluate the performance of the proposed distributed ETFTC algorithms, a simulation study was conducted in the MATLAB/Simulink environment.

Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers

Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers.

Backstepping Predictive Control of Hybrid Microgrids Interconnected by Neutral Point Clamped Converters

In this work, DC and AC parts of hybrid microgrids are interconnected by a neutral point clamped—NPC converter controlled using a new backstepping predictive (BP) method. The NPC converter is controlled to operate in the DC microgrid voltage control mode or in the AC microgrid power control mode. The novel backstepping predictive controller is designed using the dq state space dynamic model of the NPC converter connected to the hybrid microgrid. The designed BP controller regulates the DC voltage or AC injected power, balances the capacitor voltages, controls the AC currents, and enforces the near unity power factor. Simulation (MATLAB/Simulink) and experimental (laboratory prototype) results show that the converter can regulate the DC voltage in the DC microgrid interconnection point, by adjusting the AC power conversion to compensate variations on the loads or on the distributed renewable energy sources in the DC microgrid. AC currents are sinusoidal with low harmonic distortion. The obtained BP controller is faster at balancing capacitor voltages than PWM (pulse width modulation) control with carrier offset. The fast AC power response allows the converter to be used as a primary frequency regulator of the AC microgrid. This research is appropriate for power and voltage control in hybrid microgrids with renewable energy.

A new robust decentralized approach based on adaptive fuzzy backstepping method for microgrid secondary voltage control

Secondary microgrid voltage control is one of the most important parts of hierarchical control in islanded mode. By using of fuzzy and adaptive control methods, this paper presents a new method for secondary voltage control of islanded microgrid and also uses the rules of the backstepping method to obtain a control signal. The backstepping method has been used as a robust control technique to overcome the uncertainty and the effects of nonlinear terms, and the fuzzy method has been used to reduce its complexity. The adaptive method also gives fuzzy approximation coefficients. Considering the effect of neighbor agents on each other as uncertainty, a robust decentralized control method has been implemented in islanded microgrid. The simulation results in MATLAB environment show the controller’s ability to eliminate voltage deviations and the appropriate dynamic response.

Assessment of energy management technique for achieving the sustainable voltage level during grid outage of hydro generator interfaced DC Micro-Grid

An appropriate energy management scheme is needed for the reliable and sustainable operation of DC microgrid. The energy management scheme of DC microgrid (MG) is mainly controlled by main energy resource of MG. Main energy resource needs to be dispatchable unit for forming the reference voltage within DC MG. In this work, a micro hydro generator (MHG) is considered as a dispatchable source for forming the sustainable voltage level of DC MG operating under the abnormal condition. In this work, the abnormality refers to the outage of solar PV due to intermittency during fluctuating load condition. But, due to mechanical time constant of MHG and rate of charging/discharging current limit of energy storage system, the fluctuating load cannot get compensated. The uncompensated fluctuating load results in unsustainable voltage control of DC MG. The CEMS proposed in this work, maintains sustainable voltage level of DC MG during the load power fluctuation through a coordination of mechanical control loop (MCL)-electrical control loop (ECL). The coordination targets sustainable voltage control of MG with optimal power extraction of micro hydro generation. To ensure the optimal power from MHG during load power fluctuation, CEMS operates it in the variable speed mode through pre-set load controller (PSLC) and proportional predictive controller. Depending on the type of load power fluctuation, the proportional predictive controller generates a reference torque to MCL based on the predicted speed change set by PSLC. PSLC predicts the load power and speed change on iteration basis. MCL sets proportional mechanical power to the permanent magnet synchronous generator through hydro turbine. Then, MHG is going to dispatch the proportional DC power to the load through voltage-oriented control scheme of active rectifier. ECL balances the mismatched power flow between the output power of MHG and fluctuating load through a battery-SC based energy storage system. In this work, the proposed CEMS assessed against slow rate fluctuating (t ≥ 9secs) and fast rate fluctuating (t < 3secs) load patterns. Moreover, the proposed CEMS is validated in MATLAB/Simulink as well as OPAL-RT real time simulator.

Optimal control and learning for cyber‐physical systems

Optimal control and learning for <scp>cyber‐physical</scp> systems

An Islanded Microgrid Droop Control using Henry Gas Solubility Optimization

Coordination of various distributed generation (DG) units is required to meet the growing demand for electricity. Several control strategies have been developed to operate parallel-connected inverters for microgrid load sharing. Among these techniques, due to the lack of essential communication links between parallel-connected inverters to coordinate the DG units within a microgrid, the droop control method has been generally accepted in the scientific community. This paper discusses the microgrid droop controller during islanding using the Henry Gas Solubility Optimization (HGSO). The most important goals of droop control in the islanded mode of operation are the frequency and voltage control of microgrid and proper power sharing between distributed generations. The droop controller has been designed using HGSO to optimally choose PI gains and droop control coefficients in order to obtain a better microgrid output response during islanding. Simulation results indicate that the droop controller using HGSO improves the efficiency of micro-grid power by ensuring that variance in microgrid frequency and voltage regulation and effective power sharing occurs whenever micro-grid island mode or when variation in load occurs.

Robust Decentralized Tracking Voltage Control for Islanded Microgrids by Invariant Ellipsoids

This manuscript presents a robust tracking (servomechanism) controller for linear time-invariant (LTI) islanded (autonomous, isolated) microgrid voltage control. The studied microgrid (MG) consists of many distributed energy resources (DERs) units, each using a voltage-sourced converter (VSC) for the interface. The optimal tracker design uses the ellipsoidal approximation to the invariant sets. The MG system is decomposed into different subsystems (DERs). Each subsystem is affected by the rest of the system that is considered as a disturbance to be rejected by the controller. The proposed tracker (state feedback integral control) rejects bounded external disturbances by minimizing the invariant ellipsoids of the MG dynamics. A condition to design decentralized controllers is derived in the form of linear matrix inequalities. The proposed controller is characterized by rapid transient response, and zero error in the steady state. A robustness analysis of the control strategy (against load changes, load unbalances, etc.) is carried out. A MATLAB/SimPowerSystems (R2017b, MathWorks, Natick, MA, USA) simulation of the case study confirm the robustness of the proposed controller.

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input-bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. By an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

Disturbance Observer Based Distributed Consensus Control Strategy of Multi‐Agent System with External Disturbance in a Standalone DC Microgrid

AbstractThis article proposes the secondary voltage control of microgrid on the basis of cooperative control of multi agent system. The distributed cooperative control utilizes disturbance observer technique to synchronize the DC bus voltage. The proposed controller achieves balanced power sharing among the battery energy storage units (BESUs) in the presence of external disturbances in heterogeneous microgrid (MG) network. The dynamics of heterogeneous nodes affected by constant disturbance such as error measurements, sudden load variations and communication link failure between distributed generator units make the network highly heterogeneous. Firstly, a disturbance observer is established to estimate the disturbance generated from the MG system. Secondly, distributed consensus control law is designed to suppress the disturbances acting on the multi‐agent system. Furthermore, communication delays are explicitly considered in the analysis. The presented control strategy is compared with the existing mainstream algorithms to check the feasibility of the designed control method. The effectiveness of the proposed control law for isolated DC MG system has been validated by MATLAB/Simulink platform using simulation case studies.

A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids

In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier β, used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability.