Electric Power System Applications of Optimization is intended to introduce optimization, system theory, foundations of different mathematical programming techniques, and application to selected areas of electrical power engineering. The idea is to present theoretical background material from a practical power system point of view and then proceed to explore applications of optimization techniques, new directions, and continuous application problems.
The need for such a book stems from the extensive and diverse literature on topics of optimization methods in solving different classes of utility operations and planning problems. Optimization concepts and algorithms were first introduced to power system dispatching, resource allocation, and planning in the mid-1960s in order to mathematically formalize decision making with regard to myriad objectives subject to technical and nontechnical constraints. There has been a phenomenal increase in research activities aimed at implementing dispatched, resource allocation problems and at planning optimally.
This increase has been facilitated by several research projects (theoretical papers usually aimed at operation research communities) that promote usage of commercial programs for power system problems but do not provide any relevant information for power engineers working on the development of power system optimization algorithms. Most recently, there has been a tremendous surge in publications on research applications, especially on optimization in electric power engineering. However, currently no book serves as a practical guide to the fundamental and application aspects of optimization for power system work. This book is intended to meet the needs of a diverse range of groups interested in optimization application.
They include university faculty, researchers, students, and developers of power systems who are interested in or who plan to use optimization as a tool for planning and operation. The focus of this book is exclusively on the development of optimization methods, foundations, and algorithms and their application to power systems. The focus was based on the following factors. First, good references that survey optimization techniques for planning and operation are currently available but they do not detail theoretical formulation in one complete environment.
Second, optimization analysis has become so complex that examples that deal with nonpower system problems are only studied and many issues are covered by only a few references for the utility industry. Finally, in the last decade, new optimization technologies such as interior point methods and genetic algorithms (GAs) have been successfully introduced to deal with issues of computations and have been applied to new areas in power system planning and operation. This book provides both the analytical formulation of optimization and the various algorithmic issues that arise in the application of various methods in power system planning and operation.
In addition, it also provides a blend of theoretical approach and application based on simulation. Figure P.1 shows a summary of the main areas and topics covered for the benefit of power=non-power engineers as well as other optimization experts. The readers here are exposed to the foundations of classical optimization theories, which are extended to linear and nonlinear programming, integer programming, and dynamic programming (DP). This book then provides direct applications of these technologies from the operation research domain to electric power systems.
It also provides foundation knowledge and references in power systems operation, optimization, and control as background to the new readers. State estimation (SE), optimal power flow (OPF), pricing, and unit commitment (UC) are presented as applications in this book. In addition, new advances in the field of adaptive critics design have spurred interest in research and practical applications of approximate DP. The final chapter of the book combines fundamental theories and theorems from functional optimization, optimal control, and DP to explain new adaptive or approximate dynamic programming (ADP) concepts and its variants.
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