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 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.

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.

Nonlinear Control Systems and Power System Dynamics presents a comprehensive description of nonlinear control of electric power systems using nonlinear control theory, which is developed by the differential geometric approach and nonlinear robust control method. This book explains in detail the concepts, theorems and algorithms in nonlinear control theory, illustrated by step-by-step examples. In addition, all the mathematical formulation involved in deriving the nonlinear control laws of power systems are sufficiently presented. Considerations and cautions involved in applying nonlinear control theory to practical engineering control designs are discussed and special attention is given to the implementation of nonlinear control laws using microprocessors. Nonlinear Control Systems and Power System Dynamics serves as a text for advanced level courses and is an excellent reference for engineers and researchers who are interested in the application of modern nonlinear control theory to practical engineering control designs.

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area.

Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives is the first book to consider the principles and applications of decentralized decision-making in future power networks. The work opens by defining the emerging power system network as a system-of-systems (SoS), exploring the guiding principles behind optimal solutions for operation and planning problems. Chapters emphasize the role of regulations, prosumption behaviors, and the implementation of transactive energy processes as key components in decentralizing power systems. Contributors explore local markets, distribution system operation and proactive load management. The role of cryptocurrencies in smoothing transactive distributional challenges are presented. Final sections cover energy system planning, particularly in terms of consumer smart meter technologies and distributed optimization methods, including artificial intelligence, meta-heuristic, heuristic, mathematical and hybrid approaches. The work closes by considering decentralization across the cybersecurity, distributed control, market design and power quality optimization vertices. - Develops a novel framework for transactive energy management to enhance flexibility in future power systems - Explores interactions between multiple entities in local power markets based on a distributed optimization approach - Focuses on practical optimization, planning and control of smart grid systems towards decentralized decision-making

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.