Mathematical Programming for Power Systems Operation

by Alejandro Garces Ruiz (Author)
Buy for $100.00 Excerpt

Explore the theoretical foundations and real-world power system applications of convex programming

In Mathematical Programming for Power System Operation with Applications in Python, Professor Alejandro Garces delivers a comprehensive overview of power system operations models with a focus on convex optimization models and their implementation in Python. Divided into two parts, the book begins with a theoretical analysis of convex optimization models before moving on to related applications in power systems operations.

The author eschews concepts of topology and functional analysis found in more mathematically oriented books in favor of a more natural approach. Using this perspective, he presents recent applications of convex optimization in power system operations problems.

Mathematical Programming for Power System Operation with Applications in Python uses Python and CVXPY as tools to solve power system optimization problems and includes models that can be solved with the presented framework. The book also includes:

  • A thorough introduction to power system operation, including economic and environmental dispatch, optimal power flow, and hosting capacity
  • Comprehensive explorations of the mathematical background of power system operation, including quadratic forms and norms and the basic theory of optimization
  • Practical discussions of convex functions and convex sets, including affine and linear spaces, politopes, balls, and ellipsoids
  • In-depth examinations of convex optimization, including global optimums, and first and second order conditions

 Perfect for undergraduate students with some knowledge in power systems analysis, generation, or distribution, Mathematical Programming for Power System Operation with Applications in Python is also an ideal resource for graduate students and engineers practicing in the area of power system optimization.

Format
EPUB
Protection
DRM Protected
Publication date
December 01, 2021
Publisher
Collection
Page count
304
ISBN
9781119747284
Paper ISBN
9781119747260
File size
5 MB
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