A distributed linear programming models in a smart grid / Prakash Ranganathan, Kendall E. Nygard.
Ranganathan, Prakash, author.; Nygard, Kendall E., author.
2017
TK3105
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Title
A distributed linear programming models in a smart grid / Prakash Ranganathan, Kendall E. Nygard.
Author
Ranganathan, Prakash, author.
ISBN
9783319526171 (electronic book)
3319526170 (electronic book)
9783319526164
3319526162
DOI
https://doi.org/10.1007/978-3-319-52617-1
Published
Cham, Switzerland : Springer, 2017.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-319-52617-1 doi
Call Number
TK3105
Dewey Decimal Classification
621.319
621.042
Summary
This book showcases the strengths of Linear Programming models for Cyber Physical Systems (CPS), such as the Smart Grids. Cyber-Physical Systems (CPS) consist of computational components interconnected by computer networks that monitor and control switched physical entities interconnected by physical infrastructures. A fundamental challenge in the design and analysis of CPS is the lack of understanding in formulating constraints for complex networks. We address this challenge by employing collection of Linear programming solvers that models the constraints of sub-systems and micro grids in a distributed fashion. The book can be treated as a useful resource to adaptively schedule resource transfers between nodes in a smart power grid. In addition, the feasibility conditions and constraints outlined in the book will enable in reaching optimal values that can help maintain the stability of both the computer network and the physical systems. It details the collection of optimization methods that are reliable for electric-utilities to use for resource scheduling, and optimizing their existing systems or sub-systems. The authors answer to key questions on ways to optimally allocate resources during outages, and contingency cases (e.g., line failures, and/or circuit breaker failures), how to design de-centralized methods for carrying out tasks using decomposition models; and how to quantify un-certainty and make decisions in the event of grid failures. • The only book to focus on Linear Programming Methods for Cyber Physical Systems; • Features AMPL codes that show how to formulate IEEE test grid systems; • Includes code that can be used to tackle problems such as resource allocation, decomposition of a major grid into micro grids, and addressing uncertainty under renewable penetration scenarios.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Added Author
Nygard, Kendall E., author.
Series
Power electronics and power systems (Springer)
Available in Other Form
Print version: 9783319526164
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Preface; Acknowledgements; Contents; List of Figures; List of Tables; List of Abbreviations; Chapter 1: Introduction; 1.1 Objectives of the Book; 1.1.1 Objective #1. Formulate a Mathematical Model for the Smart-Grid Resource-Allocation Problem; 1.1.2 Objective #2. Design, Develop, and Implement a Distributed Solution Procedure for the Mathematical Model; 1.1.3 Objective #3. Develop an Experimental Design for Testing the Procedure Referenced in Objective 2; 1.1.4 Objective #4. Conduct the Experimental Testing Referenced in Objective 3

1.1.5 Objective # 5. Develop Decision Models Using Linear Classifier, and Placement of Synchro phasors Using LP1.1.6 Objective # 6. Integrating Wind Source to Smart Grid Decision Using Linear Programming, and Modeling Capacitated Resourc...; Chapter 2: Literature Review; 2.1 Linear Programming in Practice; 2.2 Development of a Distributed Linear-Programming Model; Chapter 3: Energy Reallocation in a Smart Grid; 3.1 Introduction; 3.2 Problem Statement; 3.3 Physical Infrastructure Issues; 3.3.1 Distributed-Device Control Functions; 3.3.2 Selective Load Control; 3.4 Micro-Grid Islanding

3.5 Distributed Pathway Control3.6 Smart-Grid Modeling; 3.7 Integer Linear-Programming Models; 3.8 Notation; 3.9 Uncertainty in Resource Allocation; 3.10 Smart-Grid Simulation; 3.11 Conclusions; Chapter 4: Resource Allocation Using Branch and Bound; 4.1 Distributed Energy Resources in a Smart Grid; 4.2 Related Work; 4.3 Assigning DER to RUA Formulation; 4.4 DER Capacities; 4.5 RUA Preferences; 4.5.1 Case 1; 4.6 Constraints; 4.7 Branch-and-Bound (BB) Strategy; 4.8 Results; 4.8.1 Case 1; 4.8.2 Case 2; 4.9 Conclusions; Chapter 5: Resource Allocation Using DW Decomposition; 5.1 Why Decompose?

5.2 Objective Function and Illustration of the DW Algorithm5.3 LP Formulation of the IEEE 14-BUS System; 5.3.1 Region 1 Constraints; 5.3.1.1 Objective for Region 1 (ZLOSS); 5.3.1.2 Node 1; 5.3.1.3 Node 2; 5.3.1.4 Node 3; 5.3.1.5 Node 4; 5.3.1.6 Node 5; 5.3.1.7 Joint-Capacity Constraints for Region 3; 5.3.1.8 Other Constraints; 5.3.2 Region 3 Constraints (Nodes 6, 12, and 13); 5.3.2.1 Objective for Region 3 (ZLOSS); 5.3.2.2 Node 12; 5.3.2.3 Node 13; 5.3.2.4 Node 6; 5.3.2.5 Joint-Capacity Constraints for Region 3; 5.3.2.6 Other Constraints; 5.3.3 Region 2 Constraints

5.3.3.1 Objective for Region 2 (ZLOSS)5.3.3.2 Node 7; 5.3.3.3 Node 8; 5.3.3.4 Node 9; 5.3.3.5 Node 10; 5.3.3.6 Node 11; 5.3.3.7 Node 14; 5.3.3.8 Joint-Capacity Constraints for Region 2; 5.3.3.9 Other Constraints; 5.3.4 Master Constraints (Linking Constraints); 5.4 Decomposing the IEEE 14-Bus System into Two Regions; 5.4.1 R2 Node Constraint in Region 1; 5.4.2 R1 Node Constraint in Region 1; 5.5 Formulating the IEEE 30-Bus Systemś Constraints; 5.5.1 Nodal Constraints for Region 1; 5.5.1.1 Node 1; 5.5.1.2 Node 2; 5.5.1.3 Node 3; 5.5.1.4 Node 4; 5.5.1.5 Node 12; 5.5.1.6 Node 13; 5.5.1.7 Node 14

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