Modern Optimization Techniques for Smart Grids

✍ Scribed by Adel Ali Abou El-Ela, Mohamed T. Mouwafi, Adel A. Elbaset

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 237
• Category: Library

✦ Synopsis

Modern Optimization Techniques for Smart Grids presents current research and methods for monitoring transmission systems and enhancing distribution system performance using optimization techniques considering the role of different single and multi-objective functions. The authors present in-depth information on integrated systems for smart transmission and distribution, including using smart meters such as phasor measurement units (PMUs), enhancing distribution system performance using the optimal placement of distributed generations (DGs) and/or capacitor banks, and optimal capacitor placement for power loss reduction and voltage profile improvement. The book will be a valuable reference for researchers, students, and engineers working in electrical power engineering and renewable energy systems.
Predicts future development of hybrid power systems;
Introduces enhanced optimization strategies;
Includes MATLAB M-file codes.

✦ Table of Contents

Preface
Contents
List of Symbols
Abbreviations
Chapter 1: Introduction
1.1 General
1.2 Book Contributions
1.3 Scope of the Book
Chapter 2: Optimization Techniques
2.1 Introduction
2.2 Conventional Optimization Techniques
2.2.1 Linear Programming (LP)
2.2.2 Quadratic Programming (QP)
2.2.3 Integer Programming (IP)
2.2.4 Dynamic Programming (DP)
2.3 Artificial Intelligence (AI) Techniques
2.3.1 Artificial Neural Network (ANN)
2.3.2 Fuzzy Linear Programming (FLP)
2.3.3 Expert Systems (ES)
2.4 Modern Optimization Methods
2.5 Evolutionary Optimization Techniques
2.5.1 Genetic Algorithm (GA)
2.6 Differential Evolution (DE) Algorithm
2.6.1 Standard DE Algorithm
2.7 Particle Swarm Optimization (PSO) Technique
2.7.1 PSO Mathematical Model
2.8 Seeker Optimization Algorithm (SOA)
2.8.1 SOA
2.9 Ant Colony Optimization (ACO) Algorithm
2.9.1 Description of Real Ants
2.9.2 Comparison Between Artificial and Real Ant Systems
2.9.3 ACO Mathematical Model
2.9.4 ACO Algorithm
2.10 Conclusion
Chapter 3: Smart Grid Technologies
3.1 Introduction
3.2 Smart Grid (SG)
3.2.1 Definitions of SG
3.2.2 Traditional Grid Versus SG
3.2.3 Benefits of SG
3.3 Smart Metering
3.3.1 Evolution of Electricity Metering
3.3.2 Comparison Between Conventional and Smart Metering
3.3.3 Examples of Smart Metering
3.3.3.1 Phasor Measurement Units (PMUs)
3.3.3.2 Supervisory Control and Data Acquisition (SCADA) System
3.3.3.3 The Comparison Between SCADA System and PMU System
3.4 Phasor Measurement Units (PMUs)
3.4.1 PMU Hardware Components
3.4.2 Applications of PMUs in Power Systems
3.5 Observability Analysis
3.6 Capacitor Banks
3.6.1 Fixed Versus Switched Capacitor Banks
3.6.2 Benefits of Capacitor Banks
3.7 Distributed Generations (DGs)
3.7.1 Definition of DG
3.7.2 Types of DGs
3.7.3 Applications of DGs
3.8 Conclusion
Chapter 4: Optimal Placement of PMUs in Smart Power Systems
4.1 Introduction
4.2 Rules of Observability Based on PMUs
4.3 Problem Formulation
4.3.1 Formulation of Optimal PMU Placement Problem
4.3.2 Installation Cost of PMUs
4.4 Optimal Solution Using Proposed Multistage Method
4.4.1 Optimal PMU Placement Using ACO Algorithm
4.4.2 Proposed Reduction Strategy (RS) Rules for PMU Channels
4.4.3 Numerical Example
4.4.3.1 Without Considering ZIBs
4.4.3.2 Considering ZIBs
4.5 Applications
4.5.1 Test Systems
4.5.2 Results and Comments
4.5.2.1 Without Considering ZIBs
4.5.2.2 Considering ZIBs
4.6 Conclusion
Chapter 5: Optimal Capacitor Placement for Power Loss Reduction and Voltage Profile Improvement
5.1 Introduction
5.2 Problem Formulation
5.3 Sensitivity Analysis and Loss Sensitivity Indices
5.4 Optimal Capacitor Placement Using ACO Algorithm
5.5 Applications
5.5.1 Test Systems
5.5.2 Results and Comments
5.5.2.1 10-Bus System
5.5.2.2 34-Bus System
5.5.2.3 85-Bus System
5.5.2.4 EDN System
5.6 Conclusion
Chapter 6: Optimal Combination of DGs and Capacitor Banks for Performance Enhancement of Distribution Systems
6.1 Introduction
6.2 Problem Formulation
6.2.1 Objective Functions
6.2.1.1 Objective 1: Minimization of Total Power Loss
6.2.1.2 Objective 2: Minimization of Voltage Deviation
6.2.1.3 Objective 3: Maximization of the Voltage Stability Index
6.2.2 Multi-objective Function
6.2.3 System Constraints
6.2.3.1 Equality Constraint
6.2.3.2 Inequality Constraints
6.3 Two Loss Sensitivity Indices
6.4 Placement of Optimal DGs and Capacitor Banks Using ACO Algorithm
6.5 Applications
6.5.1 Test Systems
6.5.2 Results and Comments
6.5.2.1 Results of Loss Sensitivity Indices
6.5.2.2 Results of Minimizing Power Loss
6.5.2.3 Results of Minimizing Voltage Deviation
6.5.2.4 Results of Minimizing the Inverse of Total VSI
6.5.2.5 Results of Multi-objective Function
6.5.2.6 Evaluation of the Results of Objective Functions
6.6 Conclusion
Chapter 7: Conclusions
7.1 Future Work
Appendix A. Test Systems
A.1. Power Systems
A.1.1. IEEE 14-Bus Test System
A.1.2. IEEE 24-Bus Test System
A.1.3. IEEE 30-Bus Test System
A.1.4. New England (NE) 39-Bus Test System
A.1.5. IEEE 57-Bus Test System
A.1.6. IEEE 118-Bus Test System
A.1.7. WDN 52-Bus System
A.2. Distribution Systems
A.2.1. 10-Bus System
A.2.2. 34-Bus System
A.2.3. 85-Bus System
A.2.4. East Delta Network (EDN) System
Appendix B. Backward/Forward Sweep (BFS) Algorithm
References
Index