Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more

Recommended product

Popular links

Popular links


Convex Optimization of Power Systems

Convex Optimization of Power Systems

Convex Optimization of Power Systems

Joshua Adam Taylor, University of Toronto
February 2015
Hardback
9781107076877

Experience the eBook and the associated online resources on our new Higher Education website. Go to site For other formats please stay on this page.

    Optimization is ubiquitous in power system engineering. Drawing on powerful, modern tools from convex optimization, this rigorous exposition introduces essential techniques for formulating linear, second-order cone, and semidefinite programming approximations to the canonical optimal power flow problem, which lies at the heart of many different power system optimizations. Convex models in each optimization class are then developed in parallel for a variety of practical applications like unit commitment, generation and transmission planning, and nodal pricing. Presenting classical approximations and modern convex relaxations side-by-side, and a selection of problems and worked examples, this is an invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.

    • Demonstrates the mathematical fundamentals of power system optimization
    • Contains recent, powerful convex relaxation-based approaches
    • Contains a comprehensive discussion of nodal pricing and transmission rights, making a connection between duality, microeconomics, and electricity markets

    Product details

    February 2015
    Hardback
    9781107076877
    209 pages
    253 × 180 × 14 mm
    0.58kg
    31 b/w illus. 3 tables 43 exercises
    Available

    Table of Contents

    • 1. Introduction
    • 2. Background
    • 3. Optimal power flow
    • 4. System operation
    • 5. Infrastructure planning
    • 6. Economics
    • 7. Future directions.