Networked Microgrids Research Articles

Resilience-Oriented Coordination of Networked Microgrids: A Shapley Q-Value Learning Approach

High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this paper suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.

Moment-Based Distributionally Robust Peer-to-Peer Transactive Energy Trading Framework Between Networked Microgrids, Smart Parking Lots and Electricity Distribution Network

Moment-Based Distributionally Robust Peer-to-Peer Transactive Energy Trading Framework Between Networked Microgrids, Smart Parking Lots and Electricity Distribution Network

A Fully Distributed Privacy-Preserving Energy Management System for Networked Microgrid Cluster Based on Homomorphic Encryption

A Fully Distributed Privacy-Preserving Energy Management System for Networked Microgrid Cluster Based on Homomorphic Encryption

An Improved Neural Lyapunov Method for Transient Stability Assessment of Networked Microgrids

An Improved Neural Lyapunov Method for Transient Stability Assessment of Networked Microgrids

Networked Microgrids: A Review on Configuration, Operation, and Control Strategies

The increasing impact of climate change and rising occurrences of natural disasters pose substantial threats to power systems. Strengthening resilience against these low-probability, high-impact events is crucial. The proposition of reconfiguring traditional power systems into advanced networked microgrids (NMGs) emerges as a promising solution. Consequently, a growing body of research has focused on NMG-based techniques to achieve a more resilient power system. This paper provides an updated, comprehensive review of the literature, particularly emphasizing two main categories: networked microgrids’ configuration and networked microgrids’ control. The study explores key facets of NMG configurations, covering formation, power distribution, and operational considerations. Additionally, it delves into NMG control features, examining their architecture, modes, and schemes. Each aspect is reviewed based on problem modeling/formulation, constraints, and objectives. The review examines findings and highlights the research gaps, focusing on key elements such as frequency and voltage stability, reliability, costs associated with remote switches and communication technologies, and the overall resilience of the network. On that basis, a unified problem-solving approach addressing both the configuration and control aspects of stable and reliable NMGs is proposed. The article concludes by outlining potential future trends, offering valuable insights for researchers in the field.

A Control Architecture for Regulating Voltage and Power Flows in a Networked Microgrid System

This paper presents a unique control system to regulate power exchanges and load bus voltage in a networked microgrid (NMG) system comprising AC and DC microgrids. During the islanding of a microgrid in this NMG system, load voltage and power balance can get disturbed. A control system and associated converter and inverter control methods are presented to rectify these issues. An efficient model predictive control (MPC) method, which gives a tracking error of 50% lower than a conventional proportional-integral (PI) controller, is used to control multiple inverters in the NMG system. Simulation studies are conducted to test the NMG in islanding and load change scenarios. With the help of these studies, it is verified that the MPC-controlled inverters can provide better tracking accuracy in achieving desired power flows in the NMG system.

A Photovoltaic-based Novel Transformerless High Gain Converter for DC Microgrid Applications

Aim: A typical microgrid network sourced by renewable energy encounters a technical setback owing to the voltage imbalance across the source integration and load power dissemination. Transformers employed to stabilize the potential may deteriorate the network efficiency and increases the cost and size of the system as well. Introduction: Photovoltaic based transformerless high gain DC-DC converter (THG-DC) is proposed here to aid the microgrid infrastructure. Microgrid fuelled by renewable energy sources demands the high gain converter interface to boost low voltage generation. The proposed THG-DC is employed with four switched inductors and three active power switches (IGBT) which are brought together under dual leg configurations. Methods: The proposed topology offers dual-duty cycle modes of regulating the active switches to realize the desired output voltage. Moreover, it is reliable to drive the proposed THG-DC with lower values of duty cycles to achieve a higher gain. The voltage stress across the switches is minimized and the magnitude of inductor current ripples is quashed to an extent. The proposed THGDC is simple in architecture and easy to control in all three operating modes. Results: The operating characteristics and performance investigation of the novel converter during the continuous and discontinuous modes are elucidated briefly and the comparative analysis on switching stress, gain, and efficiency are executed to justify the standards of the proposed THGDC. Conclusions: Finally, the miniature prototype model is experimented with in the laboratory (0.3 kW) and the obtained results are in agreement with the theory. It is evident from the investigations that the proposed THG-DC shows its dominance over other converters on the voltage gain, switching stress, number of components, and overall efficiency.

Distributed Event-Triggered Algorithms for Multi-Objective Resource Allocation Problem

This paper considers a multi-objective resource allocation problem for a multi-agent network where each agent has multiple local objective functions. The goal is to obtain the Pareto optimum by exchanging information between agents. To this end, first, we introduce the weighted [Formula: see text] preference index to reformulate this problem into a single-objective resource allocation problem, in which the weighting factor of each objective depends on its relative importance. Moreover, in order to reduce the communication burden, we propose distributed event-triggered algorithms to solve the reformulated problem. When local objective functions are strongly convex and have Lipschitz gradients, we prove that the proposed algorithms are free of Zeno behavior and achieve exponential convergence. Finally, we demonstrate the effectiveness of the proposed algorithms by a microgrid network example.

Decision-making for sustainable urban transportation: A statistical exploration of innovative mobility solutions and reduced emissions

This research aims to investigate sustainable urban transportation decision-making through innovative mobility solutions, aiming to curtail emissions. To this end, this research presents a smart stochastic architecture to optimize renewable energy-dependent systems, addressing challenges in networked microgrids. By integrating electric vehicle (EV) charging demands and harnessing the unpredictability of renewable energy resources into the urban networks, the research introduces a vehicle-to-grid layout and a novel machine learning-enabled probabilistic Technique. These advancements cover the way for stable, sustainable, and efficient urban transportation systems with reduced environmental impact. A smart stochastic architecture to optimize the operation and management of such systems that rely heavily on renewable energy is proposed in this study, taking into account the poor reliability and complicated energy management in networked microgrids (MGs). The stochastic and variable charging EVs and also the unpredictability of renewable energy resources (RERs) are incorporated in the suggested layout. A vehicle to grid layout, which has become suitable for the MG cost function, is being used for mitigating adverse impacts of vehicles on MGs. In order to support the use of RERs, this study proposes a new machine learning-enabled probabilistic method utilizing support vector machine and point estimation methods for keeping the system in a stable state taking into account their random behavior. Furthermore, this paper develops a novel optimization algorithm using modified particle swarm optimization to determine what is optimal. IEEE systems evaluate the suitability of the suggested smart layout.

Adaptive Virtual Impedance Control with MPC’s Cost Function for DG Inverters in a Microgrid with Mismatched Feeder Impedances for Future Energy Communities

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities.

Event-Triggered Distributed Secondary Control With Model-Free Predictive Compensation in AC/DC Networked Microgrids Under DoS Attacks.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

Adaptive Load Shedding as Part of Primary Frequency Response To Support Networked Microgrid Operations

Global changes in the deployment of distributed energy resources, control and communications technologies, business models, and regulatory policy are increasing the operational options for future distribution systems. One such option is the coordinated operation of distributed resources to form microgrids and networks of microgrids to support traditional bulk power systems during normal operations and critical end-use loads during outages. While individual standalone microgrids have been extensively studied and deployed, the coordinated operation of networked microgrids is operationally more challenging due to the dynamic boundaries and changing mix of generation resources. This paper presents a method of using a distributed control architecture to support primary frequency response in networked microgrids operations. The support of primary frequency response is accomplished using the Open Field Message Bus reference architecture and Grid Friendly Appliance controllers.

Distributed Stochastic Model Predictive Control for Scheduling Deterministic Peer-to-Peer Energy Transactions Among Networked Microgrids With Hybrid Energy Storage Systems

Distributed Stochastic Model Predictive Control for Scheduling Deterministic Peer-to-Peer Energy Transactions Among Networked Microgrids With Hybrid Energy Storage Systems

Dynamic Congestion Management With Chance-Constrained MPC in Networked Microgrids Under Consumers-Related Uncertainties

Dynamic Congestion Management With Chance-Constrained MPC in Networked Microgrids Under Consumers-Related Uncertainties

Cyber-Resilient Automatic Generation Control for Systems of AC Microgrids

In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber security solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (μAGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the μAGC. To address the sensory challenges, we introduce a new microgrid model by exploiting the rank-one deficiency property of microgrid dynamics. This model is used to pose an optimal μAGC control problem that is easily implemented, because it does not require fast frequency measurements. An end-to-end cyber security solution to the False Data Injection (FDI) attack detection and mitigation is developed for the proposed μAGC. The front-end barrier of applying off-the-shelf algorithms for cyber attack detection is removed by introducing a data-driven modeling approach. Finally, we propose an observer-based corrective control for an islanded microgrid and a collaborative mitigation scheme in systems of AC microgrids. We demonstrate a collaborative role of systems of microgrids during cyber attacks. The performance of the proposed cyber-resilient μAGC is tested in a system of two networked microgrids.

Policies and Power Systems Resilience Under Time-Based Stochastic Process of Contingencies in Networked Microgrids

Policies and Power Systems Resilience Under Time-Based Stochastic Process of Contingencies in Networked Microgrids

Economic Model Predictive Control for Microgrid Optimization: A Review

Microgrids have emerged as a promising solution to integrate distributed energy resources (DERs) and supply reliable and efficient electricity. The operation of a microgrid involves the coordination of different DERs and loads. To date, various control methods have been developed to maximize the overall benefit while satisfying various constraints. Now it is urgently needed to understand and comprehend these approaches to further stimulate the deployment of microgrids. This paper presents an overview for researchers on economic model predictive control (EMPC) methods of microgrids to achieve a variety of objectives such as cost minimization and benefit maximization. The fundamental principle of the EMPC theory is explained in detail. The most popular and important strategies applied to stand-alone microgrids, grid-connected microgrids, residential smart homes, as well as networked microgrids are discussed. Future trends are also highlighted.

An Improved Machine Learning‐Based Model for Detecting and Classifying PQDs with High Noise Immunity in Renewable‐Integrated Microgrids

Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive devices in MG networks. Hence, this paper presents a new approach to detect and classify the PQDs using discrete wavelet transform, multiresolution analysis, and optimized‐kernel support vector machine. The obtained unique features from DWT‐MRA are fed to train the well‐known intelligent classifiers. In the optimized‐kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave‐one‐out (LOO) algorithm. To get higher separation in feature space, the kernel width of each sample is estimated based on the local density. By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. Moreover, a typical MG network is simulated in MATLAB software considering the validation of the proposed technique to address the power quality issues in MG networks, and the results of the proposed method are compared with other conventional ML classifiers. The simulation results confirm that the proposed method is more effective and accurate than other intelligent classifiers.

Optimal Switch Placement to Improve the Reliability of Distribution Network in Interconnected Network of Microgrids Using a Graph-Based Approach

Optimal Switch Placement to Improve the Reliability of Distribution Network in Interconnected Network of Microgrids Using a Graph-Based Approach

A Nonlinear Spatio-temporal Dynamical Stabilization and Control for Inverter Based Networked Microgrids: Energy Function based Approach

A Nonlinear Spatio-temporal Dynamical Stabilization and Control for Inverter Based Networked Microgrids: Energy Function based Approach