Microgrid Hierarchical Control Research Articles

Iterative Learning-Based Distributed Secondary Control for AC Microgrid

Existing distributed secondary control strategies relying on the microgrid system model have good theoretical performance but are difficult to implement in practice due to the need for real-time observation of numerous system states. This article addresses this issue by proposing a distributed secondary control strategy for ac microgrids based on ILC. During operation, this strategy neither relies on the microgrid system model nor imposes any computational burden. First, this article introduces the microgrid hierarchical control structure and establishes a system model for primary control. Then, the proposed ILC-based secondary control strategy is elaborated, and the corresponding stability criterion is derived using the small-signal analysis method. Finally, the simulation results in MATLAB/Simulink demonstrate the effectiveness of the proposed ILC-based strategy. Specifically, the proposed strategy can limit the transient time within 0.4 s when handling various disturbances, which is comparable to the existing strategies based on MPC and SMC. However, the proposed strategy is easier to implement compared with the MPC-based and SMC-based strategies, which rely on the system model. Furthermore, in plug-and-play and load change scenarios, the proposed control strategy can suppress voltage fluctuation within 1 V, which is better than existing strategies.

Hierarchical control of island microgrid based on consensus algorithm

When the load inside the microgrid changes, droop control maintains a stable power supply cycle of the microgrid by controlling the voltage and frequency at the parallel network of the distributed generation. Since the traditional microgrid droop control usually uses a fixed droop coefficient to adjust the output active power and reactive power, the frequency and voltage of the inverter parallel network will deviate from the system frequency and voltage rating to varying degrees, so a new layer of control link is needed to restore the target value. Moreover, most of the hierarchical control methods adopt centralized control methods, which have limitations such as high dependence on communication. This paper combines a hierarchical control framework and a consistency algorithm to propose a distributed sag control strategy for islanded microgrids based on a multi-agent system. It achieves equal-to-ratio output when distributed generation responds to power changes while maintaining the stability of the microgrid’s main performance indicators. Since the distributed system with a consistency algorithm does not require a centralized control centre, it increases the system’s robustness and reduces the dependence of the system on the communication network. Finally, an islanded AC microgrid model is built by MATLAB/Simulink, and the effectiveness of the proposed islanded microgrid hierarchical control strategy is verified by simulation.

Synchronous oscillation characteristic and finite-time function projection synchronization control method for microgrids

Due to the nonsynchronization of each microsource node in a microgrid, the physical parameters of each node are not uniform. Therefore, unstable oscillation will occur for the whole microgrid system. To solve the oscillation problem caused by the nonsynchronization of each microsource node in the microgrid, a hierarchical control structure microgrid model is researched in this paper. Based on the theory of chaotic synchronization and finite time, a new microgrid synchronous oscillation characteristic and finite-time function projection synchronization control method is proposed. First, combining complex networks and microgrids, a nonlinear dynamic model of microgrid hierarchical control is established, and the abilities of the small-world network model and the formal network model to adjust for asynchronous oscillations are verified by comparing the two models. Then, aiming at the unsynchronized oscillations caused by external system noise, a new finite-time function projection synchronization control method is proposed. According to the verification results, the method can control external disturbances, avoiding system oscillation.

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

Moving Microgrid Hierarchical Control to an SDN-Based Kubernetes Cluster: A Framework for Reliable and Flexible Energy Distribution

Software Defined Networking (SDN) is a communication alternative to increase the scalability and resilience of microgrid hierarchical control. The common architecture has a centralized and monolithic topology, where the controller is highly susceptible to latency problems, resiliency, and scalability issues. This paper proposes a novel and intelligent control network to improve the performance of microgrid communications, solving the typical drawback of monolithic SDN controllers. The SDN controller's functionalities are segregated into microservices groups and distributed through a bare-metal Kubernetes cluster. Results are presented from PLECS hardware in the loop simulation to validate the seamless transition between standard hierarchical control to the SDN networked microgrid. The microservices significantly impact the performance of the SDN controller, decreasing the latency by 10.76% compared with a monolithic architecture. Furthermore, the proposed approach demonstrates a 42.23% decrease in packet loss versus monolithic topologies and a 53.41% reduction in recovery time during failures. Combining Kubernetes with SDN microservices can eliminate the single point of failure in hierarchical control, improve application recovery time, and enhance containerization benefits, including security and portability. This proposal represents a reference framework for future edge computing and intelligent control approaches in networked microgrids.

Oscillation characteristics and trajectory stability region analysis method of hierarchical control microgrids

To address the broadband oscillation problem caused by isomerized power electronic devices in microgrids, an oscillation characteristic and stability region analysis method was proposed for hierarchical control structure microgrids based on the dynamic time-varying fast and slow scale analysis method. From the perspective of control, the controller was combined with the main circuit, and a nonlinear dynamic model of the microgrid hierarchical control grid-connected inverter was established. Then, the dynamic time-varying fast and slow scale analysis method was used to obtain the Hopf bifurcation oscillation trajectory. Furthermore, and combining with three-dimensional and Hopf bifurcation, considering the correlation between the main circuit parameters and controller parameters as the starting point, the trajectory instability region analysis method is proposed. Finally, the instability mechanism of the hierarchical control structure microgrid is revealed using the above methods. These methods provide a theoretical basis for the correct selection of the main circuit structure parameters and controller parameters of the microgrid.

Distributed Energy Resource Control and Utility Interconnection Management: Implementation in a Distributed Microgrid

This article describes the implementation of a brownfield microgrid with the upgrade of existing distributed energy resources (DERs), design of the medium-voltage (MV) interconnection, and implementation of a microgrid control system to meet the U.S. Department of Defense (DOD) needs at Fort Custer, Michigan. Brownfield microgrids involve complexities in design, integration, and commissioning but offer better economic viability due to reduced capital cost upfront. This work tries to identify the challenges associated with brownfield microgrids and provides systematic practical solutions to mitigate them. As part of the upgrades, backup diesel generators were retrofitted to support grid-parallel operation. A battery energy storage system (BESS) was newly installed to manage intermittencies for the existing solar generation and to support the microgrid. The individual DER assets were integrated in a two-level hierarchical microgrid control architecture and controlled in a coordinated manner to achieve seamless islanding and reconnection with the utility grid at the point of common coupling (PCC). The article provides the field data demonstrating the improvement in resiliency through islanded operation as well as maintaining power quality when operating in parallel with the utility. The economic operation of the assets and participation in utility ancillary services were also key goals achieved through the microgrid. This ability allows the military to reduce its total cost of ownership to achieve its mandated energy security needs.

A switching event-triggered resilient control scheme for primary and secondary levels in AC microgrids

In microgrid hierarchical control, primary control to stabilize system and secondary control to eliminate frequency/voltage deviations are both necessary for islanded microgrids. In this paper, a switching event-triggered (SET) resilient control scheme for microgrid primary and secondary levels has been proposed. First, droop control and model predictive control (MPC) are used for power sharing and driving signal generation respectively on primary level. An SET control method to trigger different MPC cost functions is proposed to balance output voltage quality and switching frequency. Then, distributed consensus and pinning control based method is used on secondary level. An SET mechanism to dynamically adjust secondary ratios to coordinate the transient deviation and response speed is proposed. Next, to deal with severe accidents, a resilient control scheme integrating primary and secondary levels is designed to maintain the system sound operation and improve the system immunity. Finally, the effectiveness of the proposed control scheme has been demonstrated by simulation with comprehensive scenarios.

Distributed Hierarchical Control for Islanded Microgrids Based on Adjustable Power Consensus

For the problem of power allocation in microgrid hierarchical control, a distributed hierarchical control strategy based on consensus algorithm is proposed. When the load suddenly increases, due to the different adjustable power of different distributed generators (DGs), overcharging and discharging of DGs will result if the increased load is not redistributed. The distributed hierarchical control strategy proposed in this paper defines the proportion between the local increased load and the local adjustable power as the response ratio. With the objective of ensuring that the frequency and voltage of the microgrid can be restored to their rated value, the response ratios of DGs are adjusted by applying the consensus algorithm. Further, the microgrid realizes the reasonable operation of the microgrid by dynamically distributing the increased load in real time. Finally, the feasibility and effectiveness of the proposed strategy is verified by analyzing the stability of the system and building a microgrid simulation model in the Matlab/Simulink simulation platform.

A review on model predictive control techniques applied to hierarchical control of AC microgrids

A microgrid (MG) is an advantageous option in the integration of distributed generation and in smart grid networking solutions. MG does not implement any fixed topologies. Therefore, a sophisticated control system with a good dynamic response, easy inclusion of constraints with nonlinearities, resiliency in case of system parameter changes, and stability are required. Traditional linear cascade control does not meet the above requirements. It also requires a complex tuning mechanism for changes in the system and also lags in fast dynamic response. The present trend is to focus on finite control set model predictive control (FCS-MPC) applied to power electronics, which acts as an alternative option compared to the century-old linear cascade control strategy. In this review article, FCS-MPC is reviewed for AC MG hierarchical control. Survey of MPC control strategy on converter level, grid-level their limitations and design constraints are reviewed as well as challenges of MPC applied to hierarchical control for AC MG environment are also discussed.

An MILP formulation for the optimum operation of AC microgrids with hierarchical control

An accurate linear model for the optimal operation of the microgrid is a key enabling solution for many real time and stochastic applications. Nonetheless, many of the existing microgrid’s optimal power flow models fall short in considering the details of the microgrid hierarchical control, which can lead to infeasible and suboptimal solutions. In this work, a new mixed integer linear programing (MILP) model for the optimal operation of three phase microgrids with hierarchical control is proposed. First, the nonlinear formulation is derived to account for the microgrid philosophy of operation in both grid connected and islanding modes of operation. Subsequently, a linearization approach is adopted to the derived nonlinear model to transform it into the required MILP formulation. The effectiveness of the proposed model is validated through several case studies considering balanced and unbalanced test systems. Additionally, a detailed error assessment is conducted to justify the proposed model.

Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection

Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.

Secondary control of islanded microgrid using robust finite time decentralized method based on adaptive fuzzy sliding mode controller

Secondary voltage control of islanded microgrid is one of the main needs of microgrid hierarchical control from one perspective and the realization of stable smart grids from another aspect. This article aims to achieve several goals simultaneously: First, it provides a decentralized structure for controlling and regulating the voltage of DGs, in which the effects of nonlinear terms on the dynamics of the microgrid system are considered without elimination or linearization. The fuzzy method has been used to approximate the uncertainties, the interactions of agents and nonlinear terms, and the adaptive method has been used to obtain the optimal fuzzy approximation gains. Sliding mode rules are also used to obtain a control signal and using Lyapunov's theory, the finite time and robust stability of the closed-loop control system is guaranteed. The finite time approach ensures the achievement of convergence in a finite and definite time, and the robust approach realizes the dynamic stability of the system against uncertainties and disturbances. The results of the microgrid system simulation show well the proposed controller capability in different operating conditions.

Investigation of Microgrid Hierarchical Control and Structure

This paper offers a literature review on different structures and control strategies of the microgrid. The implementation of a microgrid system provides significant advantages for both electric utility and end-users. Microgrid control is one of the most sophisticated parts of such implementations that must be taken into account before moving to the next steps. The different control objectives and structures of the main grid and microgrid lead to various control methods proposed for microgrids. The hierarchical structure is the most accepted method for microgrid control that has three levels including primary, secondary, and tertiary. The focus of this study will be on the main modes of typical microgrids, types of microgrid and how to control them. The investigation of this paper will give an overview to the readers, and in our next paper, we will focus more deeply on the subject, with a specific concentration on the concept of secondary control and corresponding trending methods.

Three-level hierarchical microgrid control—model development and laboratory implementation

This paper presents a three-level hierarchical control approach for microgrids in grid-connected mode. The first level optimizes microgrid operation in the long run, e.g. 15 min, with the goal of minimizing microgrid’s operating costs. The second level takes part in frequency control in grid-connected microgrids. It utilizes a Model Predictive Controller and Kalman Filter based on available frequency measurements in the microgrid. The third level is the plant level, in which classical controllers are used for tracking optimal set points received from upper two control levels. The developed control scheme is applied to the Smart Grid Lab (SGLab) at the University of Zagreb Faculty of Electrical Engineering and Computing. The findings from this close-to-real-world application are also presented.

Coordination of operational planning and real-time optimization in microgrids

Hierarchical microgrid control levels range from distributed device level controllers that run at a high frequency to centralized controllers optimizing market integration that run much less frequently. Centralized controllers are often subdivided in operational planning controllers that optimize decisions over a time horizon of one or several days, and real-time optimization controllers that deal with actions in the current market period. The coordination of these levels is of paramount importance. In this paper we propose a value function based approach as a way to propagate information from operational planning to real-time optimization. We apply this method to an environment where operational planning, using day-ahead forecasts, optimizes at a market period resolution the decisions to minimize the total energy cost and revenues, the peak consumption and injection related costs, and plans for reserve requirements. While real-time optimization copes with the forecast errors and yields implementable actions based on real-time measurements. The approach is compared to a rule-based controller on three use cases, and its sensitivity to forecast error is assessed.

Probabilistic Microgrid Energy Management with Interval Predictions

In this paper, we consider a probabilistic microgrid dispatch problem where the predictions of the load and the Renewable Energy Source (RES) generation are given in the form of intervals. A hybrid method combining scenario-selected optimization and reserve strategy using the Model Predictive Control (MPC) framework is proposed. Specifically, first of all, an appropriate scenario is selected by the optimizer at each optimization stage, and then the optimal scheduling and reservation of system capacity are determined based on the selected scenario and possible variations in the future as provided by the predictors. In addition, a new reserve strategy is introduced to adaptively maintain system reliability and respond to variations in the hierarchical microgrid control. Simulations are conducted to compare our proposed method with the existing robust method and the deterministic dispatch with perfect information. Results show that our proposed method significantly improves the system efficiency while maintaining system reliability.

To study unplanned islanding transient response of microgrid by implementing MSOG and SRF‐PLL based hierarchical control in the presence of non‐linear loads

This study considers the hierarchical control of microgrid and harmonic compensation of non-linear load specially in transition from grid-connected to unplanned islanding. The proposed controllers are based on the stationary reference frame with hierarchical control levels (inner, primary, and secondary). In the inner control loop, proportional and multi-resonant voltage and current controllers for three-phase voltage source inverters are proposed. Moreover, a droop control is used to share active and reactive power in the primary control loop. To achieve decoupled active and reactive power sharing, virtual impedance loop is implemented, too. The mentioned procedure increases the total impedance and consequently reduces the initial current and subsequently facilitates the continuous connection of DGs to the MG. To attain smoother transition, selective harmonic virtual impedance is applied by multiple second-order generalised integrator module to extract the fundamental positive sequence and harmonic components of the output current of the DGs. In secondary control level, synchronisation procedure is considered to make connection and restoration by the advanced three-phase synchronous reference frame PLL with in-loop moving average filter which significantly enhances its disturbance rejection capability. The results are shown smoother transition, robustness, and high performance of the proposed hierarchical control VSIs during variation of system configuration.

Meshed DC microgrid hierarchical control: A differential flatness approach

In this paper, a meshed DC microgrid control architecture whose goal is to manage load balancing and efficient power distribution is introduced. A novel combination of port-Hamiltonian (PH) modeling with differential flatness and B-splines parametrization is introduced and shown to improve the microgrid's performance. A three layer supervision structure is considered: (i) B-spline parametrized flat output provide continuous profiles for load balancing and price reduction (high level); (ii) the profiles are tracked through a MPC implementation with stability guarantees (medium level); (iii) explicit switching laws applied to the DC/DC converters ensure appropriate power injection. Each level functions at a different time-scale (from slow to fast), and the control laws are chosen appropriately. The effectiveness of the proposed approach is evaluated by simulations over a DC microgrid composed by a collection of solar panels (PV), an energy storage system (ES), a utility grid (UG) and a consumers’ demand.

A Review on Challenges and Techniques for Secondary Control of Microgrid

Environmental concern, limited availability of fossil fuel and economic aspects have resulted in increasing interest in microgrid technology comprising of various renewable energy based distributed generators such as PV, wind, fuel cell and biomass. Microgrid provides various advantages such as higher combined heat and power efficiency, high reliability and lesser carbon emission and also reduction in technical losses. However, microgrid operation and control is associated with various challenges such as power quality issues, bidirectional power flow, voltage and frequency variations, coordinated operation of multiple distributed generators, stability, power management and economic operation. In this paper, a brief discussion on these challenges is presented. A classification of various control principles on the basis of their controller function, connection with grid and response time is also presented. The paper classifies and compares microgrid hierarchical control into three levels, i.e. primary, secondary and tertiary control. Also, a detailed discussion on various schemes of secondary centralized, decentralized and distributed control has been presented. Finally, an attempt has been made to identify research gaps in various control strategies.