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Distributed Linear Programming Models in a Smart Grid

Overview of attention for book
Cover of 'Distributed Linear Programming Models in a Smart Grid'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Literature Review
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    Chapter 3 Energy Reallocation in a Smart Grid
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    Chapter 4 Resource Allocation Using Branch and Bound
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    Chapter 5 Resource Allocation Using DW Decomposition
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    Chapter 6 Implementation and Testing of the Dantzig-Wolfe Procedure
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    Chapter 7 Remarks About the Dantzig-Wolfe Scheme
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    Chapter 8 A Linear Classifier for Decision Support in a Smart Grid
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    Chapter 9 Maximization of the Utility Function, Time-Dependent Energy Allocation, and Fuzzy-Logic Resource-Allocation Models
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    Chapter 10 Placement of Synchrophasors Using Linear Programming and Zero-Injection Constraints
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    Chapter 11 Unbiased Optimal Power Flow (OPF) for Power Systems with Wind-Power Generation
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    Chapter 12 Smart-Grid Optimization Using A Capacitated Transshipment Problem Solver
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    Chapter 13 Decomposition of Microgrids in Large-Scale Electric Test Beds for Economic Dispatch Optimization
Attention for Chapter 13: Decomposition of Microgrids in Large-Scale Electric Test Beds for Economic Dispatch Optimization
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Citations

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Chapter title
Decomposition of Microgrids in Large-Scale Electric Test Beds for Economic Dispatch Optimization
Chapter number 13
Book title
Distributed Linear Programming Models in a Smart Grid
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-52617-1_13
Book ISBNs
978-3-31-952616-4, 978-3-31-952617-1
Authors

Prakash Ranganathan, Kendall E. Nygard

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Engineering 1 33%

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