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000780614 019__ $$a981702253$$a981811279$$a984848358
000780614 020__ $$a9783319526171$$q(electronic book)
000780614 020__ $$a3319526170$$q(electronic book)
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000780614 049__ $$aISEA
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000780614 08204 $$a621.319$$223
000780614 08204 $$a621.042
000780614 1001_ $$aRanganathan, Prakash,$$eauthor.
000780614 24512 $$aA distributed linear programming models in a smart grid /$$cPrakash Ranganathan, Kendall E. Nygard.
000780614 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000780614 300__ $$a1 online resource.
000780614 336__ $$atext$$btxt$$2rdacontent
000780614 337__ $$acomputer$$bc$$2rdamedia
000780614 338__ $$aonline resource$$bcr$$2rdacarrier
000780614 347__ $$atext file$$bPDF$$2rda
000780614 4901_ $$aPower electronics and power systems
000780614 504__ $$aIncludes bibliographical references and index.
000780614 5050_ $$aPreface; 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
000780614 5058_ $$a1.1.5 Objective # 5. Develop Decision Models Using Linear Classifier, and Placement of Synchro phasors Using LP1.1.6 Objective # 6. Integrating Wind Source to Smart Grid Decision Using Linear Programming, and Modeling Capacitated Resourc...; Chapter 2: Literature Review; 2.1 Linear Programming in Practice; 2.2 Development of a Distributed Linear-Programming Model; Chapter 3: Energy Reallocation in a Smart Grid; 3.1 Introduction; 3.2 Problem Statement; 3.3 Physical Infrastructure Issues; 3.3.1 Distributed-Device Control Functions; 3.3.2 Selective Load Control; 3.4 Micro-Grid Islanding
000780614 5058_ $$a3.5 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?
000780614 5058_ $$a5.2 Objective Function and Illustration of the DW Algorithm5.3 LP Formulation of the IEEE 14-BUS System; 5.3.1 Region 1 Constraints; 5.3.1.1 Objective for Region 1 (ZLOSS); 5.3.1.2 Node 1; 5.3.1.3 Node 2; 5.3.1.4 Node 3; 5.3.1.5 Node 4; 5.3.1.6 Node 5; 5.3.1.7 Joint-Capacity Constraints for Region 3; 5.3.1.8 Other Constraints; 5.3.2 Region 3 Constraints (Nodes 6, 12, and 13); 5.3.2.1 Objective for Region 3 (ZLOSS); 5.3.2.2 Node 12; 5.3.2.3 Node 13; 5.3.2.4 Node 6; 5.3.2.5 Joint-Capacity Constraints for Region 3; 5.3.2.6 Other Constraints; 5.3.3 Region 2 Constraints
000780614 5058_ $$a5.3.3.1 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
000780614 506__ $$aAccess limited to authorized users.
000780614 520__ $$aThis 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.
000780614 650_0 $$aSmart power grids$$xMathematical models.
000780614 650_0 $$aSmart power grids$$xData processing.
000780614 650_0 $$aLinear programming.
000780614 7001_ $$aNygard, Kendall E.,$$eauthor.
000780614 77608 $$iPrint version:$$z3319526162$$z9783319526164$$w(OCoLC)966794931
000780614 830_0 $$aPower electronics and power systems (Springer)
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