Ebook: Mathematical Models and Algorithms for Power System Optimization

A number of mathematical models and algorithms are presented in this book for solving the
practical problems in planning, operation, control, and marketing decisions for power systems.
It focuses on economic dispatching, generator maintenance scheduling, load flow, optimal load
flow, load optimization, reactive optimization, load frequency control, transient stability, and
electricity marketing where mathematical models are transformed into relatively standard
optimization models to make optimization applications possible. The optimization models
discussed include linear (0–1, integer, mixed-integer), nonlinear, mixed integer, and nonlinear
mixed integer models. Both numerical and non-numerical optimization algorithms are used in
this book, the former (mathematical programming approachs) includes linear programming,
nonlinear programming, mixed integer programming and dynamic programming, the latter
(rules based approaches) includes Genetic Algorithm (GA), Simulated Annealing (SA), and
Expert System (ES). Based on the authors’ extensive research experience in developing models
and algorithms for power system optimization, this book also provides an in-depth analysis of
some practical modeling techniques which are seldom explained comprehensively in the
existing textbooks, both from theoretical and practical standpoints, for example, validity testing
of data, type setting of variables, special setting of limit values of variables, special setting of
constraints, and preprocessing of parameter and data. These techniques can be effectively
applied to the modeling of power system optimization problems. Therefore, the readers of
Mathematical Models and Algorithms for Power System Optimization will gain important
insights into: how to transform the practical problems into mathematical models, how to
develop the standard optimal mathematical models and utilize commercially available and
reliable programming software, how to deal with various issues that affect the performance of a
model, and how to evaluate the effectiveness of the models.