TY - BOOK ID - 7240736 TI - Convex optimization of power systems PY - 2015 SN - 9781107076877 1107076870 PB - Cambridge: Cambridge university press, DB - UniCat KW - Electric power systems KW - Electric power distribution KW - Convex programming KW - Mathematical optimization KW - Mathematical models KW - Mathematics KW - 621.315 KW - Convex programming. KW - Mathematical optimization. KW - Programming (Mathematics) KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis KW - Electricity KW - Power distribution, Electric KW - Power transmission KW - Electric power transmission KW - Electrification KW - Transmission of electric energy. Power distribution and telecommunication lines. Conductors. Insulating materials. Accessories. Design, construction of lines KW - Mathematics. KW - Distribution KW - 621.315 Transmission of electric energy. Power distribution and telecommunication lines. Conductors. Insulating materials. Accessories. Design, construction of lines KW - Electric power systems - Mathematical models KW - Electric power distribution - Mathematics UR - https://www.unicat.be/uniCat?func=search&query=sysid:7240736 AB - 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. ER -