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In this research, the main focus is on the inverter control and power sharing control. This research involves simulation of microgrid using Matlab/Simulink software with load variations in each parallel connected inverters. The microgrid is in island mode in which it is not connected to the main ac grid. Three single phase inverters connected in parallel are used in the microgrid. The voltage (amplitude and frequency) and current are supplied by the parallel connected inverters. The DC voltage at the input of inverters is assumed stiff.
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.
Microgrid technology is an emerging area, and it has numerous advantages over the conventional power grid. A microgrid is defined as Distributed Energy Resources (DER) and interconnected loads with clearly defined electrical boundaries that act as a single controllable entity concerning the grid. Microgrid technology enables the connection and disconnection of the system from the grid. That is, the microgrid can operate both in grid-connected and islanded modes of operation. Microgrid technologies are an important part of the evolving landscape of energy and power systems. Many aspects of microgrids are discussed in this volume, including, in the early chapters of the book, the various types of energy storage systems, power and energy management for microgrids, power electronics interface for AC & DC microgrids, battery management systems for microgrid applications, power system analysis for microgrids, and many others. The middle section of the book presents the power quality problems in microgrid systems and its mitigations, gives an overview of various power quality problems and its solutions, describes the PSO algorithm based UPQC controller for power quality enhancement, describes the power quality enhancement and grid support through a solar energy conversion system, presents the fuzzy logic-based power quality assessments, and covers various power quality indices. The final chapters in the book present the recent advancements in the microgrids, applications of Internet of Things (IoT) for microgrids, the application of artificial intelligent techniques, modeling of green energy smart meter for microgrids, communication networks for microgrids, and other aspects of microgrid technologies. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of microgrids, this is a must-have for any library.
The book discusses principles of optimization techniques for microgrid applications specifically for microgrid system stability, smart charging, and storage units. It also highlights the importance of adaptive learning techniques for controlling autonomous microgrids. It further presents optimization-based computing techniques like fuzzy logic, and neural networks to enhance the computational speed. Features Discusses heuristic techniques and evolutionary algorithms in microgrids optimization problems Covers operation management, distributed control approaches, and conventional control methods for microgrids Presents intelligent control for energy management and battery charging systems Highlights a comprehensive treatment of power sharing in DC microgrids Explains control of low-voltage microgrids with master-slave architecture, where distributed energy resources interface with the grid by means of conventional current-driven inverters It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, and communications engineering, computer science and engineering, and environmental engineering.
Power quality issue is one of the major concerns in modern power grids due to higher penetration of renewable energy based distributed generation sources. In this thesis, advanced inverter control mechanisms are developed to improve power quality, specifically 1) voltage and frequency regulation, and 2) reactive power sharing in microgrids. The virtual synchronous generator (VSG) control method is employed as a primary control mechanism in this research work, and several advanced control techniques are developed. The incorporation of a fuzzy secondary controller (FSC) and an adaptive virtual impedance loop in the VSG control scheme is proposed to improve voltage and frequency regulation, and reactive power sharing performance in microgrids, respectively. A systematic approach of the control system design is presented in details, and dynamic models of test microgrids are developed using MATLAB/Simulink. Extensive simulation studies are carried out to verify the effectiveness of the proposed methods through case and sensitivity studies. It is found that the proposed methods offer significantly improved performance compared to existing techniques, and dynamic characteristics of microgrids under disturbance conditions are enhanced. Furthermore, in this study a new data driven analytics approach is proposed for determination of Q-V (reactive power-voltage) curve of grid connected wind farms, which can provide useful information for voltage control action.
With the rapidly increasing demand of energy, renewable energy generation becomes increasingly popular due to its clean and sustainable features. Therefore, microgrids are gaining more attention with increased penetration of Renewable Energy Sources (RES). Among all the RESs, the Photovoltaic (PV) technology brings a huge advantage in the power industry. Due to its wide availability, microgrids can be used as independent power source for critical loads; hospitals and military bases during emergency situations such as earthquakes or bad weather, where there is no access to the main grid. Highly improved storage facility can be an option for these critical issues in the energy industry. Although Battery Energy Storage System (BESS) would be an option, its limited cycle life makes it cost ineffective to use only batteries for smoothing out the PV power fluctuations caused by renewable intermittency. In view of this, the combination of battery and Supercapacitor (SC) is one of the popular Hybrid Energy Storage System (HESS) configurations. The SC absorbs all transients and that will increase the life span of the battery. However, an effective Energy Management System (EMS) is required for these HESSs to achieve cost-effective system with high efficiency along with system stability. In this research thesis, among different types of control systems, the fuzzy logic system is considered. First of all, Mamdani type fuzzy inference system is implemented to track the maximum power point of the PV as this is very important in isolated microgrids where there is no backup power from the smart grid. The DC bus power balance can be achieved through the proposed Sugeno type fuzzy controller due to its high efficiency. With the variation of the load demand can be addressed successfully through another Mamdani type Fuzzy Inference System (FIS) to keep the State of Charge (SOC) of the battery and SC within safe range. Furthermore, in grid-connected mode, the new fuzzy logic based PQ controlled Voltage Source Converter (VSC) is implemented with the results. The simulation results are obtained using the MATLAB/Simulink R2017b integrated development environment. At the end of the simulation, these fuzzy logic results are compared with the conventional PI controlled system’s results to prove the proposed system’s efficiency. A new adaptive fuzzy controller is introduced in the later of this research to achieve more cost-effective operation for PQ controlled VSC. This controller will be generated different gains due to the electricity market price variations and current SOC of the HESS. Therefore, active power reference which will be used to exchange power from the grid will be changed due to the system output gain variations. Finally, hardware experiments are carried out for microgrid DC bus voltage regulation to further verify the efficiency of the proposed method. The dSPACE MicroLabBox is used to perform real-time simulation. The dSPACE is capable of communicating with the hardware using the Ethernet protocol. A universal dSPACE experiment software (ControlDesk 6.3) is used as the supervisory controlled and data acquisition platform for the MicroLabBox hardware. SEMIKRON Insulated Gate Bipolar Transistors(IGBTs) and their drivers are used to implement the DC-DC converters. However, hardware results show the proposed system has better characteristics than traditional PI controller based system. Keywords: Hybrid Energy Storage System (HESS), State of Charge (SOC), Energy Management, Fuzzy Logic Controller (FLC), Fuzzy Inference System (FIS).
This book comprises the select proceedings of the International Conference on Power Engineering Computing and Control (PECCON) 2019. This volume focuses on the different renewable energy sources which are integrated in a smart grid and their operation both in the grid connected mode and islanded mode. The contents highlight the role of power converters in the smart grid environment, battery management, electric vehicular technology and electric charging station as a load for the power network. This book can be useful for beginners, researchers as well as professionals interested in the area of smart grid technology.
In addition to the control practice in microgrid, a nonmodel control approach is developed to improve the set point tracking capability of the electric drive system. The proposed approach reduces the overshoot without compromising the speed of the system.
The steadily increasing need for electrical power, rising costs of energy, market forces and industry deregulation, an aging infrastructure, tight constraints on new long distance transmission lines, global environmental concerns, and a public demand for greater electrical reliability and security are overwhelming our existing power system. One technology that offers solutions to many of these challenges and addresses smart grid objectives directly is: microgrids. A microgrid is a small (typically several MW or less in scale) power system incorporating distributed generators, load centers, potentially storage, and the ability to operate with or apart from the larger utility grid. Properly managed, assets connected within a microgrid can provide value to the utility power network, improve energy delivery to local customers, and facilitate a more stable electrical infrastructure, benefitting environmental emissions, energy utilization, and operational cost. While microgrids can achieve significant improvements for customers and utilities alike, microgrid research is in its infancy and, to date, a comprehensive means of managing microgrid operations has not been realized. In this work, two primary efforts are undertaken. First, given the lack of a comprehensive software test bed for microgrids, a simulation environment capable of incorporating microgrid operational concepts, electrical modeling, asset dynamics, and control conditions is developed. Second, using the simulation environment, an enhanced decentralized multi-agent power management and control system is designed and evaluated for the purpose of supervising multiobjective microgrid operations under normal and emergency conditions. Results presented demonstrate effective multi-agent methods that yield improved microgrid performance, as well as facilitate coordinated system decision-making without reliance on a centralized controller. These advancements represent innovation towards the autonomous operation of microgrids, as well as provide important insight into new tradeoff considerations associated with multi-objective design for power management. Microgrids are infrastructure elements that bridge the gap between emerging energy technologies and the existing power system. Simply put, smart grid objectives including higher penetration of renewables, integration of storage, delivery efficiency improvements, more responsive system elements, stronger resiliency, and improved flexibility will be difficult to achieve without microgrids. The simulation environment developed and the power management methodology presented are important steps towards enabling microgrids and realizing their benefits.
This study focuses on the improvement of the power quality in interconnected power distribution systems using connected microgrids. The power quality issues at the secondary distribution level are addressed using a unified power quality conditioner (UPQC), which aids in controlling voltage imbalances and current harmonics. Further, a power control method, adaptive neuro-fuzzy inference system (ANFIS), is proposed as a global solution by installing independent compensating devices at the point of common coupling. The proposed strategy can improve the power quality in traditional installations because the proposed UPQC method maximizes power utilization and improves the efficiency of the system. The microgrids integrate renewable energy sources, which generate green energy with low loss. Case studies, a prototype, and simulations via MATLAB/Simulink are used to validate the proposed method, and the results prove the enhancement in power quality control. The proposed control strategy had a response time of 0.3 s, reduced peak voltage distortions, and restored the voltage at the interconnecting point to a normal level. The total harmonic distortion of currents decreased from 21.13% to 14.74% when a PI technique was used to control the UPQC-P, and the proposed ANFIS-based UPQC-P reduced the harmonic content to 2.43% from 21.13%.