Nonlinear droop control has been introduced to establish an effective power sharing between distributed generators without the need for communication links in microgrids (MGs). However, one of the missing studies in the literature is the effects of nonlinear droop relations on the stability of the MGs. In the first part of this thesis work, the stability of an inverter-based MG operating with the nonlinear frequency droop-control has been analyzed. A complete small-signal state-space linearized model of the MG system, with optimized nonlinear droop relations, has been developed considering the dynamics of the overall system, and is updated periodically. The stability of the system is then checked online at different operating points. Small signal stability analysis of an islanded microgrid was performed using MATLAB/Simulink and the results were experimentally verified on an MG setup. Since renewable energy sources (RESs) usually have fast dynamics and low inertia, conventional generators could encounter large disturbances in their real power productions. As the magnitudes of the disturbance exceed certain limits, instability could be induced in the operation of various generators. In the second part of the thesis, the impact of installing certain RES capacity at specific nodes on the operation of the power system is proposed to be evaluated through developing a modified load flow analysis model to the MG system and analyzing the impact of the variation in RES production on the distributed generators. The proposed method automatically determines the optimum placement of the RES to improve the stability of the system. The optimum placement point is referred to as the center of mass point for the microgrid system.

Microgrids Presents microgrid methodologies in modeling, stability, and control, supported by real-time simulations and experimental studies Microgrids: Dynamic Modeling, Stability and Control, provides comprehensive coverage of microgrid modeling, stability, and control, alongside new relevant perspectives and research outcomes, with vital information on several microgrid modeling methods, stability analysis methodologies and control synthesis approaches that are supported by real-time simulations and experimental studies for active learning in professionals and students alike. This book is divided into two parts: individual microgrids and interconnected microgrids. Both parts provide individual chapters on modeling, stability, and control, providing comprehensive information on the background, concepts, and architecture, supported by several examples and corresponding source codes/simulation files. Communication based control and cyber security of microgrids are addressed and new outcomes and advances in interconnected microgrids are discussed. Summarizing the outcome of more than 15 years of the authors’ teaching, research, and projects, Microgrids: Dynamic Modeling, Stability and Control covers specific sample topics such as: Microgrid dynamic modeling, covering microgrid components modeling, DC and AC microgrids modeling examples, reduced-order models, and model validation Microgrid stability analysis, covering stability analysis methods, islanded/grid connected/interconnected microgrid stability Microgrids control, covering hierarchical control structure, communication-based control, cyber-resilient control, advanced control theory applications, virtual inertia control and data-driven control Modeling, analysis of stability challenges, and emergency control of large-scale interconnected microgrids Synchronization stability of interconnected microgrids, covering control requirements of synchronous microgrids and inrush power analysis With comprehensive, complete, and accessible coverage of the subject, Microgrids: Dynamic Modeling, Stability and Control is the ideal reference for professionals (engineers, developers) and students working with power/smart grids, renewable energy, and power systems, to enable a more effective use of their microgrids or interconnected microgrids.

This book discusses relevant microgrid technologies in the context of integrating renewable energy and also addresses challenging issues. The authors summarize long term academic and research outcomes and contributions. In addition, this book is influenced by the authors’ practical experiences on microgrids (MGs), electric network monitoring, and control and power electronic systems. A thorough discussion of the basic principles of the MG modeling and operating issues is provided. The MG structure, types, operating modes, modelling, dynamics, and control levels are covered. Recent advances in DC microgrids, virtual synchronousgenerators, MG planning and energy management are examined. The physical constraints and engineering aspects of the MGs are covered, and developed robust and intelligent control strategies are discussed using real time simulations and experimental studies.

The increase in the global energy demand is one of the key factors that necessitates the developments in the field of renewable energy resources. Research in the field of microgrids has seen significant progress in recent years. It is driven by increased deployment, integration of renewable energy and energy storage, smart grid integration, innovative ownership models, grid-interactive buildings, standardization efforts, and a focus on resilience and emergency preparedness. These developments contribute to a more sustainable, reliable, and decentralized energy landscape. The high penetration of power-electronic interfaces in Distributed Energy Resources (DERs) integration makes microgrids highly susceptible to disturbances, causing severe transients, especially in the islanded mode. While the details of the system topology are easily obtainable, it is rather difficult to develop a high-fidelity model that represents the transient dynamics of the different DERs. A novel modularized Sparse Identification of Non-linear Dynamics (M-SINDy) algorithm is developed for effective data-driven modeling of the nonlinear transient dynamics of microgrid systems. The M-SINDy method realizes distributed discovery of nonlinear dynamics by partitioning a higher-order microgrid system into multiple subsystems and introducing pseudo-states to represent the impact of neighboring subsystems. This specific property of the proposed algorithm is found to be very useful while working with re-configurable and scalable microgrids. Dynamic discovery of system transients from measurements can be beneficial for designing control strategies that improves the overall microgrid stability and reliability. Prediction of future states is a difficult, but an essential tool in power systems for determining different control strategies that can aid in maintaining the transient stability of the overall system following a contingency. To understand the system and predict these transient dynamics of a microgrid in different operation modes, an extension of the M-SINDy method - Physics-informed hierarchical sparse identification has been proposed. The developed algorithm has a multi-layered structure to reduce the overall computational cost required to obtain accurate model dynamics. The different functions that affect the system dynamics are developed in the primary layer using the measured data. The terms developed in the primary layer are fit in the secondary layer to determine the exact dynamics of the system subject to different disturbances which can be leveraged to predict the system's future dynamics. The primary motivation to develop the data-driven prediction model is to incorporate the prediction data into a Model Predictive Control (MPC) framework that can generate an optimal control input to enhance the transient stability of microgrids. This MPC controller is augmented with the conventional droop control for frequency stabilization. Given the inherent fluctuations in typical microgrid operations, stemming from factors such as varying load demands, weather conditions, and other variables, reachability analysis is performed in this work. We aim to facilitate the design of a data-driven prediction model that can be leveraged to implement an effective control strategy to ensure the efficient working of microgrids for a wide range of operating conditions. Another potential challenge in the study of microgrids is caused by system imbalances. Variable loads, single phase DERs, network variations, etc. are some of the major contributing factors which are responsible for making the system unbalanced. Unbalanced transients in a microgrid can result in conditions that can impact the connected loads and damage the system equipments. Minimizing the overall imbalance in the system is important for maintaining the system's stability, reliability, and optimal performance. We developed a data-driven model using a domain-enriched Deep Neural Network (DNN) architecture that can accurately predict the voltage dynamics in an unbalanced microgrid system, based on dynamic power flow computation. A supervisory control strategy is developed to reduce the imbalance by modulating the power generation of dispatchable units within the microgrid. The overarching purpose of this thesis is to explore the advancements in data science and provide an insight on the role of machine learning in transforming power systems for operation optimization and system enhancements. The integration of data science in microgrids allows for a more informed decision-making on resource allocation and builds a more resilient and sustainable energy infrastructure. It accelerates the transition to a more flexible, decentralized, and intelligent grid.

Microgrids: Modeling, Control, and Applications presents a systematic elaboration of different types of microgrids, with a particular focus on new trends and applications. The book includes sections on AC, DC and hybrid AC/DC microgrids and reflects state-of-the-art developments, covering theory, algorithms, simulations, error and uncertainty analysis, as well as novel applications of new control techniques. Offering a valuable resource for students and researchers working on the integration of renewable energy with existing grid and control of microgrids, this book combines recent advances and ongoing research into a single informative resource. The book highlights recent findings while also analyzing modelling and control, thus making it a solid reference for researchers as well as undergraduate and postgraduate students. - Covers different types of microgrids and their architecture and control in a single book - Includes original, state-of-the-art research contributions by international experts - Features global case studies for better understanding and real-life examples

The stability of power systems and microgrids is compromised by the increasing penetration with power electronic devices, such as wind turbines, photovoltaics and batteries. A simulation and optimization environment for such low-inertia systems is created. It is investigated how accurate the models need to be to capture the prevailing modes. An evolutionary algorithm tailored to optimization problems with computationally intensive fitness evaluation is proposed in order to optimized the controller parameters of grid-forming and grid-supporting distributed generators. It becomes apparent that microgrids dominated by grid-forming inverters are very stable systems when well-designed and optimized controllers are used. Model simplifications, such as the neglect of inner control loops of inverters, must be examined carefully, as they can lead to an inaccurate stability assessment.

Modelling and Control Dynamics in Microgrid Systems with Renewable Energy Resources looks at complete microgrid systems integrated with renewable energy resources (RERs) such as solar, wind, biomass or fuel cells that facilitate remote applications and allow access to pollution-free energy. Designed and dedicated to providing a complete package on microgrid systems modelling and control dynamics, this book elaborates several aspects of control systems from classical approach to advanced techniques based on artificial intelligence. It captures the typical modes of operation of microgrid systems with distributed energy storage applications like battery, flywheel, electrical vehicles infrastructures that are integrated within microgrids with desired targets. More importantly, the techno-economics of these microgrid systems are well addressed to accelerate the process of achieving the SDG7 i.e., affordable and clean energy for all (E4ALL). This reference presents the latest developments including step by step modelling processes, data security and standards protocol for commissioning of microgrid projects, making this a useful tool for researchers, engineers and industrialists wanting a comprehensive reference on energy systems models. - Includes simulations with case studies and real-world applications of energy system models - Detailed systematic modeling with mathematical analysis is covered - Features possible operating scenarios with solutions to the encountered issues

This book brings together emerging objectives and paradigms in the control of both AC and DC microgrids; further, it facilitates the integration of renewable-energy and distribution systems through localization of generation, storage and consumption. The control objectives in a microgrid are addressed through the hierarchical control structure. After providing a comprehensive survey on the state of the art in microgrid control, the book goes on to address the most recent control schemes for both AC and DC microgrids, which are based on the distributed cooperative control of multi-agent systems. The cooperative control structure discussed distributes the co-ordination and optimization tasks across all distributed generators. This does away with the need for a central controller, and the control system will not collapse in response to the outage of a single unit. This avoids adverse effects on system flexibility and configurability, as well as the reliability concerns in connection with single points of failure that arise in traditional, centralized microgrid control schemes. Rigorous proofs develop each control methodology covered in the book, and simulation examples are provided to justify all of the proposed algorithms. Given its extensive yet self-contained content, the book offers a comprehensive source of information for graduate students, academic researchers, and practicing engineers working in the field of microgrid control and optimization.

A microgrid (MG) is described as a set of interconnected loads and distributed energy resources (DERs) that behaves as a controllable entity with respect to the grid. The MG has the ability to operate as connected and disconnected from the grid (i.e., in grid-connected or island mode). The main advantage of the MG is improving the grid's reliability and resilience when disturbances occur. Furthermore, adoption of MGs can be motivated by financial incentives to save infrastructure costs in dense urban settings with fast-growing electricity demand, remote isolated regions, and rough terrain. The technical feasibility of MGs relies on DERs through DC/AC power electronics converter interfaces, such as the conventional droop-controlled inverter. However, these technologies lack inertia, which makes them prone to oscillatory behavior under perturbations. The thesis will address gaps in the literature by investigating the large-signal stability of the conventional droop-controlled inverter operating in grid-connected and island mode by proposing nonlinear dynamic models that include saturable power and voltage controllers, phase-locked loop controller, and power-balancing control strategies. Previous studies ignored the impact of local loads and synchronization dynamics on the large-signal stability of the system. This thesis addressed that by developing a 16th-order nonlinear dynamic model to analyze the large signal stability of the droop-controlled inverter operating in grid-connected mode with local RL loads and phase-locked loop (PLL) controller. This work is important because it allows researchers to build on the model to investigate seamless transition between the operating modes (i.e., grid-connected and islanded modes). In addition, previous studies assumed inverters and the synchronous generators to have similar response behavior under disturbances, which was inaccurate. This thesis is one of the first to introduce saturable power and voltage controllers and study their effects on the stability of grid-connected droop-controlled inverter by implementing the 18th-order nonlinear dynamic models. This is important because it allows the inverter model to capture different physical and software limits that are different from the synchronous generators, which can assist researchers designing more effective protection systems and strategies for MGs. Furthermore, previous studies were unable to utilize the changes in the DC-link dynamics to enhance the conventional droop-controlled inverter performance by balancing the DC- and AC-side active power. In addition, these studies assumed the inverter was connected to an ideal constant DC source without the ability to control its output, which was not an accurate representation of how the DERs operate. The thesis addressed that by analyzing the large-signal stability of the two proposed control strategies that represented by the 17th- and 22nd-order nonlinear dynamic models. The control strategies models utilized the DC-link parameters as input to achieve power-balacing between the DC- and AC-side of the droop-controlled inverter operating in islanding mode. Most importantly, the developed control strategies could be utilized to mitigate DC-link undesired oscillations, which can lead to manufacturing and maintenance cost savings and prolong the inverter life expectancy. Previous studies adopted a large-signal stability analysis method that was feasible for analyzing dynamic models with small number of nonlinear terms. This thesis addressed that by modifying the Takagi-Sugeno (TS) algorithm to be able to quickly produce preliminary ROA estimates for the dynamic models, even when the number of nonlinear terms was large. This is important because it allows researchers to scale up the nonlinear dynamic models to study more complex MGs, and quickly produce preliminary results of time sensitive applications using readily available resources.