Modeling, Control, Estimation, and Optimization for Microgrids: A Fuzzy-Model-Based Method, Zhong, Zhixiong
Автор: Zhong, Zhixiong Название: Modeling, control, estimation, and optimization for microgrids ISBN: 1138491659 ISBN-13(EAN): 9781138491656 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Guo, Fanghong (agency For Science, Technology And Research, Singapore) Wen, Changyun Song, Yong-duan (chongqing University, China) Название: Distributed control and optimization technologies in smart grid systems ISBN: 1138088595 ISBN-13(EAN): 9781138088597 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book aims to equalize the theoretical involvement with industrial practicality by reducing the mathematical difficulties. It provides an overview of distributed control and distributed optimization theory, followed by specific details on industrial applications to smart grid systems, with a special focus on micro grid systems.
Автор: Carlos Bordons; F?lix Garcia-Torres; Miguel A. Rid Название: Model Predictive Control of Microgrids ISBN: 3030245691 ISBN-13(EAN): 9783030245696 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.
The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. The authors also provide a modular simulator to be run in MATLAB®/Simulink®, for readers to create their own microgrids using the blocks supplied, in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.
Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.
Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Автор: Chauhan, Rajeev Kumar Название: Distributed Energy Resources In Microgrids ISBN: 0128177748 ISBN-13(EAN): 9780128177747 Издательство: Elsevier Science Рейтинг: Цена: 88690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Distributed Energy Resources in Microgrids: Integration, Challenges and Optimization unifies classically unconnected aspects of microgrids by considering them alongside economic analysis and stability testing. In addition, the book presents well-founded mathematical analyses on how to technically and economically optimize microgrids via distributed energy resource integration. Researchers and engineers in the power and energy sector will find this information useful for combined scientific and economical approaches to microgrid integration.
Specific sections cover microgrid performance, including key technical elements, such as control design, stability analysis, power quality, reliability and resiliency in microgrid operation.
Addresses the challenges related to the integration of renewable energy resources
Includes examples of control algorithms adopted during integration
Presents detailed methods of optimization to enhance successful integration
Microgrids have emerged as a promising solution for accommodating the integration of renewable energy resources. But the intermittency of renewable generation is posing challenges such as voltage/frequency fluctuations, and grid stability issues in grid-connected modes. Model predictive control (MPC) is a method for controlling a process while satisfying a set of constraints. It has been in use for chemical plants and in oil refineries since the 1980s, but in recent years has been deployed for power systems and electronics as well.
This concise work for researchers, engineers and graduate students focuses on the use of MPC for distributed renewable power generation in microgrids. Fluctuating outputs from renewable energy sources and variable load demands are covered, as are control design concepts. The authors provide examples and case studies to validate the theory with both simulation and experimental results and review the shortcomings and future developments.
Chapters treat power electronic converters and control; modelling and hierarchical control of microgrids; use of MPC for PV and wind power; voltage support; parallel PV-ESS microgrids; secondary restoration capability; and tertiary power flow optimization.
Автор: Fan Lingling Название: Control and Dynamics in Power Systems and Microgrids ISBN: 0367782154 ISBN-13(EAN): 9780367782153 Издательство: Taylor&Francis Рейтинг: Цена: 43890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In traditional power system dynamics and control books, the focus is on synchronous generators. Within current industry, where renewable energy, power electronics converters, and microgrids arise, the related system-level dynamics and control need coverage. Wind energy system dynamics and microgrid system control are covered. The text also offer
Автор: Khokhar, Bhuvnesh ; Parmar, K P Singh ; Thakur, Tr Название: Load Frequency Control of Microgrids ISBN: 1032718315 ISBN-13(EAN): 9781032718316 Издательство: Taylor&Francis Рейтинг: Цена: 94930.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Sorin Olaru; Alexandra Grancharova; Fernando Lobo Название: Developments in Model-Based Optimization and Control ISBN: 3319266853 ISBN-13(EAN): 9783319266855 Издательство: Springer Рейтинг: Цена: 113190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Introduction.- Part I. Complexity and Structural Properties of Linear Model Predictive Control.- 1. Complexity Certifications of First Order Inexact Lagrangian Methods for General Convex Programming: Application to Real-time MPC.- 2. Fully Inverse Parametric Linear/Quadratic Programming Problems via Convex Liftings.- 3. Implications of Inverse Parametric Optimization in Model Predictive Control.- Part II. Distributed-coordinated and Multi-objective Features of Model Predictive Control.- 4. Distributed Robust Model Predictive Control of Interconnected Polytopic Systems.- 5. Optimal Distributed-Coordinated Approach for Energy Management in Multisource Electric Power Generation Systems.- 6. Evolutionary-game-based Dynamical Tuning for Multi-objective Model Predictive Control.- Part III. Collaborative Model Predictive Control.- 7. A Model Predictive Control-based Architecture for Cooperative Path-following of Multiple Unmanned Aerial Vehicles.- 8. Predictive Control for Path Following. From Trajectory Generation to the Parameterization of the Discrete Tracking Sequences.- 9. Formation Reconfiguration using Model Predictive Control Techniques for Multi-Agent Dynamical Systems.- Part IV. Applications of Optimization-based Control and Identification.- 10. Optimal Operation of a Lumostatic Microalgae Cultivation Process.- 11. Bioprocesses Parameter Estimation by Heuristic Optimization Techniques.- 12. Real-time Experimental Implementation of Predictive Control Schemes in a Small-scale Pasteurization Plant.- Part V. Optimization-based Analysis and Design for Particular Classes of Dynamical Systems.- 13. An Optimization-based Framework for Impulsive Control Systems.- 14. Robustness Issues in Control of Bilinear Discrete-Time Systems - Applied to the Control of Power Converters.- 15. On the LPV Control Design and its Applications to Some Classes of Dynamical Systems.- 16. Ultimate Bounds and Robust Invariant Sets for Linear Systems with State-dependent Disturbances.- 17. RPI Approximations of the mRPI Set Characterizing Linear Dynamics with Zonotopic Disturbances.
Автор: Kaushik Das Sharma; Amitava Chatterjee; Anjan Raks Название: Intelligent Control ISBN: 9811312974 ISBN-13(EAN): 9789811312977 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses systematic designs of stable adaptive fuzzy logic controllers employing hybridizations of Lyapunov strategy-based approaches/H? theory-based approaches and contemporary stochastic optimization techniques. The text demonstrates how candidate stochastic optimization techniques like Particle swarm optimization (PSO), harmony search (HS) algorithms, covariance matrix adaptation (CMA) etc. can be utilized in conjunction with the Lyapunov theory/H? theory to develop such hybrid control strategies. The goal of developing a series of such hybridization processes is to combine the strengths of both Lyapunov theory/H? theory-based local search methods and stochastic optimization-based global search methods, so as to attain superior control algorithms that can simultaneously achieve desired asymptotic performance and provide improved transient responses. The book also demonstrates how these intelligent adaptive control algorithms can be effectively utilized in real-life applications such as in temperature control for air heater systems with transportation delay, vision-based navigation of mobile robots, intelligent control of robot manipulators etc.
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