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 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.

Due to increasing economic and environmental pressures, small-scale grids have received increasing attention in the last fifteen years. These renewable sources, such as solar PVs, wind turbines, and fuel cells, integrated with grid, have changed the way we live our lives. This book describes microgrid dynamics modeling and nonlinear control issues from introductory to the advanced steps. The book addresses the most relevant challenges in microgrid protection and control including modeling, uncertainty, stability issues, local control, coordination control, power quality, and economic dispatch.

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 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.

Microgrids Understand microgrids and networked microgrid systems Microgrids are interconnected groups of energy sources that operate together, capable of connecting with a larger grid or operating independently as needed and network conditions require. They can be valuable sources of energy for geographically circumscribed areas with highly targeted energy needs, and for remote or rural areas where continuous connection with a larger grid is difficult. Microgrids’ controllability makes them especially effective at incorporating renewable energy sources. Microgrids: Theory and Practice introduces readers to the analysis, design, and operation of microgrids and larger networked systems that integrate them. It brings to bear both cutting-edge research into microgrid technology and years of industry experience in designing and operating microgrids. Its discussions of core subjects such as microgrid modeling, control, and optimization make it an essential short treatment, valuable for both academic and industrial study. Readers will acquire the skills needed to address existing problems and meet new ones as this crucial area of power engineering develops. Microgrids: Theory and Practice also features: Incorporation of new cyber-physical system technologies for enabling microgrids as resiliency resources Theoretical treatment of a wide range of subjects including smart programmable microgrids, distributed and asynchronous optimization for microgrid dispatch, and AI-assisted microgrid protection Practical discussion of real-time microgrids simulations, hybrid microgrid design, transition to renewable microgrid networks, and more Microgrids: Theory and Practice is ideal as a textbook for graduate and advanced undergraduate courses in power engineering programs, and a valuable reference for power industry professionals looking to address the challenges posed by microgrids in their work.

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.

The book focuses on the transient modelling, stability analysis and control of power electronic systems, since these systems face severe safe operation problems the during transient period. It discusses both theoretical analysis and practical applications, highlighting the transient characteristics of converters with different control strategies, and proposes transient modelling and model reduction methods. Furthermore, it classifies the transient stability problems of the system to help the readers gain an understanding of the basic theoretical methods for analysing the power electronic system, at the same time providing sufficient detail to enable engineers to design such systems. Comprehensively describing theoretical analyses, ranging from system modelling and stability analysis to transient control, the book is a valuable resource for researchers, engineers and graduate students in fields of transient modelling, stability analysis and control of power electronic systems.

Renewable energy sources interface with the ac grids via inverters and are termed inverter-based resources (IBRs). They are replacing traditional fossil fuel-based synchronous generators at a dazzling speed. In turn, unprecedented dynamic events have occurred, threatening power grid reliability. Modeling and Stability Analysis of Inverter-Based Resources provides a fundamental understanding of IBR dynamics. Developing reliability solutions requires a thorough understanding of challenges, and in this case, IBR-associated dynamics. Modeling and stability analysis play an indispensable role in revealing a mechanism of dynamics. This book covers the essential techniques of dynamic model building for IBRs, including type-3 wind farms, type-4 wind farms, and solar photovoltaics. Besides modeling, this book offers readers the techniques of stability analysis. The text includes three parts. Part 1 concentrates on tools, including electromagnetic transient simulation, analysis, and measurement-based modeling. Part 2 focuses on IBR modeling and analysis details. Part 3 highlights generalized dynamic circuit representation—a unified modeling framework for dynamic and harmonic analysis. This topic of IBR dynamic modeling and stability analysis is interesting, challenging, and intriguing. The authors have led the effort of publishing the 2020 IEEE Power and Energy Society’s TR-80 taskforce report “Wind Energy Systems Subsynchronous Oscillations: Modeling and Events,” and the two taskforce papers on investigation of real-world IBR dynamic events. In this book, the authors share with readers many insights into modeling and analysis for real-world IBR dynamic events investigation.

"Highlights the role of transmission and distribution grids that ensure the reliability and quality of electric power supply. - Original coverage of Analysis and Control of Loss of Synchronism including, Extended Equal Area Criterion (EEAC). - Timely and unique coverage of On-Line Detection of Loss of Synchronism, Wide Area Measurements and Applications, Wide-Area Feedback Control Systems for Power Swing Damping and Microgrids-Operation and Control. Market description (Please include secondary markets) Primary: Senior undergraduate and Ph. D. students on courses relating to power system dynamics and control/ electrical power industry professionals working on the planning, design and development of controls for enhancing grid performance. Secondary: Researchers in R & D laboratories connected with modernization and systems improvement of electricity supply systems"--Provided by publisher.