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This book presents the application of some AI related optimization techniques in the operation and control of electric power systems. With practical applications and examples the use of functional analysis, simulated annealing, Tabu-search, Genetic algorithms and fuzzy systems for the optimization of power systems is discussed in detail. Preliminary mathematical concepts are presented before moving to more advanced material.

Researchers and graduate students will benefit from this book. Engineers working in utility companies, operations and control, and resource management will also find this book useful. ​

### Chapter 1. Introduction

Abstract
The primary objectives of this chapter are to Provide a broad overview of standard optimization techniques. When using optimization techniques: Understand clearly where optimization fits into the problem. Be able to formulate a criterion for optimization. Know how to simplify a problem to the point at which formal optimization is a practical proposition. Have sufficient understanding of the theory of optimization to select an appropriate optimization strategy, and to evaluate the results that it returns.
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy

### Chapter 2. Mathematical Optimization Techniques

Abstract
The objectives of this chapter are: Explaining some optimization techniques. Explaining the minimum norm theorem and how it could be used as an optimization algorithm, where a set of equations can be obtained. Introducing the fuzzy system as an optimization technique. Introducing the simulated annealing algorithm (SAA) as an optimization technique. Introducing the tabu search algorithm (TSA) as an optimization technique. Introducing the genetic algorithm (GA) as an optimization technique. Introducing the particle swarm (PS) as an optimization technique.
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy

### Chapter 3. Economic Operation of Electric Power Systems

Abstract
It is the purpose of this chapter To formulate the problem of optimal short-term operation of hydrothermal-nuclear systems To obtain the solution by using a functional analytical optimization technique that employs the minimum norm formulation To propose an algorithm suitable for implementing the optimal solution To present and formulate the fuzzy economic dispatch of all thermal power systems and explain the algorithm suitable for solution To formulate the fuzzy economic dispatch problem of hydrothermal power systems and its solution
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy

### Chapter 4. Economic Dispatch (ED) and Unit Commitment Problems (UCP): Formulation and Solution Algorithms

Abstract
The objectives of this chapter are: Formulating the objectives function for ED and UCP Studying the system and unit constraints Proposing rules for generating solutions Generating an initial solution Explaining an algorithm for the economic dispatch problem Applying the simulated annealing algorithm to solve the problems Comparing simulated annealing with other simulated annealing algorithms Offering numerical results for the simulated annealing algorithm
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy

### Chapter 5. Optimal Power Flow

Abstract
The objectives of this chapter are Studying the load flow problem and representing the difference between the conventional load flow and the optimal load flow (OPF) problem Introducing the different states used in formulating the OPF Studying the multiobjective optimal power flow problem Introducing the particle swarm optimization algorithm as a tool to solve the optimal power flow problem
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy

### Chapter 6. Long-Term Operation of Hydroelectric Power Systems

Abstract
The objectives of this chapter are Formulating the problem of long-term operation of a multireservoir power system connected in cascade (series) Implementing the minimum norm approach to solve the formulated problem Implementing the simulated annealing algorithm (SAA) to solve the long-term hydro scheduling problem (LTHSP) Introducing an algorithm enhancement for randomly generating feasible trial solutions Implementing an adaptive cooling schedule and a method for variable discretization to enhance the speed and convergence of the original SAA Using the short-term memory of the tabu search (TS) approach to solve the nonlinear optimization problem in continuous variables of the LTHSP
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy

### Chapter 7. Electric Power Quality Analysis

Abstract
The objectives of this chapter are Studying the applications to the simulated annealing (SA) optimization algorithm for measuring voltage flicker magnitude and frequency as well as the harmonic contents of the voltage signal, for power quality analysis Estimating voltage magnitude, frequency, and phase angle of the fundamental component Solving the nonlinear optimization problem, which minimizes the sum of the absolute value of the error in the estimated voltage signal in continuous variables Using the simulated annealing algorithm to estimate the parameters of a system steady power system, having a constant frequency within a data window and a variable frequency within a data window Solving the nonlinear optimization problem, which is the minimization of the sum of the squares of the errors, as a function of the signal amplitude, frequency, and phase angle Testing the algorithm using samples of the voltage or current signal of one phase of simulated and actual recorded data for noise-free and harmonic contaminated signals Studying the effects of critical parameters, such as sampling frequency and number of samples, on the estimated parameters
Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy