Modeling Control Estimation And Optimization For Microgrids
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Modeling, Control, Estimation, and Optimization for 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.
Model Predictive Control of Microgrids
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. 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.
Information Processing and Management of Uncertainty in Knowledge-Based Systems
Author: Marie-Jeanne Lesot
language: en
Publisher: Springer Nature
Release Date: 2025-02-12
This book is a collection of papers focused on techniques for managing uncertainty and aggregation. It provides a forum for exchanging ideas between theoreticians and practitioners in these and related areas. The papers are part of the 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, which will occur in Lisbon, Portugal, from July 22 to 26, 2024. The collection describes the latest findings on topics such as advances in fuzzy systems and data analysis, optimization, scheduling via modeling uncertainty, explainability, decision-making, implications, data aggregation, and aggregation operators. A special chapter is dedicated to the memory of Michio Sugeno. The book is a valuable resource for practitioners, researchers, and graduate students who want to apply fuzzy-based techniques to real-world data analysis and management processes involving imprecision and uncertainty.