Distributed linear programming models in a smart grid /

Distributed linear programming models in a smart grid /

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Bibliographic Details
Author / Creator:Ranganathan, Prakash, 1981- author.
Imprint:Cham, Switzerland : Springer, 2017.
Description:1 online resource (xxv, 213 pages) : illustrations
Language:English
Series:Power electronics and power systems
Power electronics and power systems (Springer)
Subject:
Smart power grids -- Mathematical models.
Smart power grids -- Data processing.
Linear programming.
Smart power grids
Linear programming.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11273018
Hidden Bibliographic Details
Other authors / contributors:Nygard, Kendall E., author.
ISBN:9783319526171
3319526170
9783319526164
3319526162
Digital file characteristics:text file PDF
Notes:Includes bibliographical references and index.
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.
Other form:Print version: Ranganathan, Prakash. Distributed linear programming models in a smart grid. Cham, Switzerland : Springer, 2017 3319526162 9783319526164
Standard no.:10.1007/978-3-319-52617-1
10.1007/978-3-319-52
Table of Contents:
  • 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 LP; 1.1.6 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 Control; 3.6 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 Algorithm; 5.3 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 SystemsĖ 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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