Control Of DC Microgrids Research Articles

Memory-Based Event-Triggered Control for Distributed Secondary Control in DC Microgrids With Quantized Communication

Memory-Based Event-Triggered Control for Distributed Secondary Control in DC Microgrids With Quantized Communication

Secondary Voltage Control for DC Microgrids: A Design Perspective for SoC With Voltage Restoration Provision

Secondary Voltage Control for DC Microgrids: A Design Perspective for SoC With Voltage Restoration Provision

On adaptive resilient secondary control for DC microgrids under false data injection attacks

AbstractDistributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method.

Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.

Prescribed-Time Control for DC Microgrids With Battery Energy Storage Systems

Prescribed-Time Control for DC Microgrids With Battery Energy Storage Systems

A Distributed Event-Triggered Fixed-Time Secondary Control for DC Microgrids Without Continuous Signal Transmission

A Distributed Event-Triggered Fixed-Time Secondary Control for DC Microgrids Without Continuous Signal Transmission

Linear Extended State Observer-Based Distributed Secondary Control of DC Microgrids Under False Data Injection Attacks

Linear Extended State Observer-Based Distributed Secondary Control of DC Microgrids Under False Data Injection Attacks

A Novel Scalable and Reliable Control for DC Microgrids With Varying Number of Agents.

Existing scalable control methods mainly rely on a fixed block-diagonal structure for the Lyapunov matrix, potentially resulting in numerical infeasibility issues. To overcome this limitation, this article proposes a novel scalable and reliable control scheme for dc microgrids. Initially, a general model for dc microgrids is established to enhance reliability, considering scenarios involving loss of control effectiveness (LoCE) and offset faults. Subsequently, a structured free-weight matrix technique is introduced to mitigate negative coupling effects of power lines, and to address numerical infeasibility by avoiding the assumption about the Lyapunov matrix. Furthermore, the stability of the entire dc microgrid is guaranteed by checking local agent conditions, independently of power line couplings. Therefore, the proposed control scheme ensures plug-and-play scalability with varying number of agents. Finally, theoretical results are validated through numerical simulations using the MATLAB/SimPowerSystems toolbox.

Distributed MPC-Based Constant Voltage Control for DC Microgrids Under DoS Attacks

Distributed MPC-Based Constant Voltage Control for DC Microgrids Under DoS Attacks

Fuzzy Resilient Control of DC Microgrids with Constant Power Loads Based on Markov Jump Models

This paper addresses the fuzzy resilient control of DC microgrids with constant power loads. The DC microgrid is subject to abrupt parameter changes which are described by the Markov jump model. Due to the constant power loads, the DC microgrid exhibits nonlinear dynamics which are characterized by a T-S fuzzy model. According to the parallel distributed compensation principle, mode-dependent fuzzy resilient controllers are designed to stabilize the resultant T-S fuzzy Markov jump DC microgrid. The “resilient” means the controller could cope with the uncertainty caused by the inaccurate execution of the control laws. This uncertainty is governed by a Bernoulli distributed random variable and thus may not occur. Then, the mean square exponential stability is analyzed for the closed-loop system by using the mode-dependent Lyapunov function. Since the stability conditions are not convex, a design algorithm is further derived to calculate the fuzzy resilient controller gains. Finally, simulations are provided to test the effectiveness of the proposed results.

Distributed secondary control for DC microgrid under the adaptive event-triggered protocol

Distributed secondary control for DC microgrid under the adaptive event-triggered protocol

PD distributed control and stability analysis for DC microgrids with multiple time delays

Distributed control is widely used for coordination of multiple distributed generations (DGs) in a microgrid (MG), but the information transmission in practice is inevitably suffered from time delays, which will deteriorate system performance and even cause system instability. To solve this problem, a proportional-differential (PD) distributed control method for DC MGs is proposed in this paper. Firstly, a small-signal system model considering multiple time delays including measurement delay and communication delay is established. The introduction of time-delayed differential terms results in the system being transformed into a neutral time-delayed system (NTDS). Then, the time delay margin (TDM) under different delayed scenarios is accurately determined by the frequency-domain stability analysis, and the relationship between the TDM and the differential gain is quantified. Compared with the conventional proportional (P-type) distributed control method, the TDM is enhanced effectively. In addition, an indicator is proposed to quantify the enhancement effect of introducing differential terms on TDM. Finally, the effectiveness of the proposed method and the accuracy of the stability analysis are verified by simulation and hardware-in-loop (HIL) tests.

Scalable Fuzzy Control for Nonlinear DC Microgrids Under Plug-and-Play Operations

Scalable Fuzzy Control for Nonlinear DC Microgrids Under Plug-and-Play Operations

Resilient Control of DC Microgrids Against FDI Attacks on Communication Links and Local Current Measurements

Resilient Control of DC Microgrids Against FDI Attacks on Communication Links and Local Current Measurements

Distributed control for DC microgrids with cyber attacks and constraints: A fault-tolerant model predictive controller

Communication security issues pose severe challenges to stability of microgrid system. This paper designs a cooperative control method based on fault-tolerant distributed model predictive control (DMPC) for DC microgrid against possible false information injection (FDI) attacks and communication constraints problems. First, a state variable is defined based on the voltage and current information, thereby deriving a system model of the microgrid under the cyber-physical framework. Then, since a combined uncertainty term consisting of attacks and disturbances exists in the constructed system model, an expanded state observer (ESO) is proposed to observe the uncertainty term. Further, based on the constructed system model and the designed ESO, a fault-tolerant distributed model predictive controller is designed. In the controller, DMPC is utilized to constrain the communication information and ensure the boundedness of the communication state and physical state. Simulation results indicate that the proposed DMPC-based distributed control method can achieve stable voltage regulation and accurate current sharing, and maintain smooth operation even under FDI attack, which verifies the feasibility for the proposed method.

Distributed Predefined-Time Optimization and Control for Multi-Bus DC Microgrid

Distributed Predefined-Time Optimization and Control for Multi-Bus DC Microgrid

Fuzzy H ∞ control of nonlinear DC microgrids under aperiodic DoS attacks – an event-triggered approach

This paper studies the fuzzy H ∞ control of nonlinear DC microgrids subject to the dynamic event-triggered mechanism (ETM), aperiodic DoS attacks, noises and mismatching premises. First, using the information of DC microgrid's T-S fuzzy model and aperiodic DoS attacks, a discrete-time resilient dynamic ETM is proposed, which can save constrained system resources, as well as actively exclude attack-induced dropouts and Zeno behaviour. Second, a fuzzy switched system model is built, which provides a unified platform to evaluate effects of all the affecting factors such as the dynamic ETM and DoS attacks. Third, by constructing a piecewise Lyapunov functional, criteria for exponential stability with guaranteed H ∞ performance are obtained, and an event-triggered fuzzy injection current controller is further designed. Simulation results confirm that, in the presence of aperiodic DoS attacks and noises, the proposed controller can stabilise the nonlinear DC microgrids, while the dynamic ETM works well in reducing the triggering rate without dropouts. Tradeoffs can be made between control and communication resources, and the proposed fuzzy controller achieves shorter settling times and smaller overshoots than the robust controller.

Data-Driven Distributed H ∞ Current Sharing Consensus Optimal Control of DC Microgrids via Reinforcement Learning

Data-Driven Distributed <i>H</i> _∞ Current Sharing Consensus Optimal Control of DC Microgrids via Reinforcement Learning

Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network

Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.

Secondary Control Strategies in the DC Microgrids

Now, DC microgrids have become more popular for several reasons, including the lack of issues related to reactive power and frequency control, the direct integration of energy storage devices and solar photovoltaic, and the higher utilization of DC loads. A DC microgrid using several sources (distributed generation) is a popular research area. The main issue in such a DC microgrid is to provide good voltage regulation and proportional power sharing among all sources. Control strategy is very important to solve the above issue in order to maintain the reliability and stability of DC microgrids. Hence, a comprehensive review of DC microgrid control techniques is essential. Compared to other methods, hierarchical control is widely used to solve the aforementioned issue. It consists of three layers of control: primary control, secondary control, and tertiary control. At the primary level, to improve current sharing performance, droop control is usually applied. Secondary control is used for voltage regulation of the DC bus. A tertiary control is a higher-level control to achieve Optimization and economical grid operation. In traditional primary control, it is not possible to attain accurate power sharing and voltage regulation simultaneously. Thus, secondary control is required. So, this paper reviews the secondary level control techniques in the hierarchical control strategy for DC microgrids. Precisely, Centralized, distributed, and decentralized approach-based secondary control are reviewed. Several secondary control techniques have been thoroughly examined in terms of their advantages and disadvantages making this an excellent resource for both academics and business executives.