Model Predictive Control of Microgrids / by Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao

By:

Bordons, Carlos, 1962- [VerfasserIn]

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Advances in Industrial Control | Springer eBooks EnergyPublisher: Cham : Springer, 2020Edition: 1st ed. 2020Description: 1 Online-Ressource (XIX, 266 p. 102 illus., 65 illus. in color)ISBN:

9783030245702

Subject(s):

Additional physical formats: 9783030245696 | Erscheint auch als: Model predictive control of microgrids. Druck-Ausgabe Cham : Springer, 2020. xix, 266 SeitenDDC classification:

621.042 23

RVK: RVK: SK 950LOC classification:

TK1001-1841

DOI: DOI: 10.1007/978-3-030-24570-2Online resources:

Summary: I: Microgrid Control Issues -- II: Overview of Control Methods Applied to Microgrids -- III: Dynamic Models of Microgrids and Components -- IV: Basic Energy and Power Management Systems in Microgrids -- V: Hybrid MPC Applied to Economical Dispatch of Microgrids -- VI: Enhancement of Power Quality using Finite-State MPC -- VII: Integration of Electric Vehicles in Microgrids -- VIII: Stochastic MPC and Failure Management -- IX: Distributed MPC for Networks of Microgrids -- X: Glossary -- XI: IndexSummary: 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 controlPPN: PPN: 1678681040Package identifier: Produktsigel: ZDB-2-ENE | ZDB-2-SEB | ZDB-2-SXEN

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