TY - BOOK ID - 48259903 TI - Model Predictive Control of Microgrids AU - Bordons, Carlos. AU - Garcia-Torres, Félix. AU - Ridao, Miguel A. PY - 2020 SN - 3030245705 3030245691 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Production of electric energy or. KW - Biotechnology. KW - Energy Systems. KW - Control and Systems Theory. KW - Power Electronics, Electrical Machines and Networks. KW - Environmental Engineering/Biotechnology. KW - Chemical engineering KW - Genetic engineering KW - Microgrids (Smart power grids) KW - MGs (Smart power grids) KW - Micro-grids (Smart power grids) KW - Mini-grids (Smart power grids) KW - Electric power distribution KW - Smart power grids KW - Energy systems. KW - Control engineering. KW - Power electronics. KW - Environmental engineering. KW - Environmental control KW - Environmental effects KW - Environmental stresses KW - Engineering KW - Environmental health KW - Environmental protection KW - Pollution KW - Sustainable engineering KW - Electronics, Power KW - Electric power KW - Electronics KW - Control engineering KW - Control equipment KW - Control theory KW - Engineering instruments KW - Automation KW - Programmable controllers UR - https://www.unicat.be/uniCat?func=search&query=sysid:48259903 AB - 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. ER -