J
Javad Lavaei
Researcher at University of California, Berkeley
Publications - 214
Citations - 6575
Javad Lavaei is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Optimization problem & Semidefinite programming. The author has an hindex of 33, co-authored 190 publications receiving 5567 citations. Previous affiliations of Javad Lavaei include Tsinghua University & Concordia University.
Papers
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Journal ArticleDOI
Zero Duality Gap in Optimal Power Flow Problem
Javad Lavaei,Steven H. Low +1 more
TL;DR: In this article, a necessary and sufficient condition is provided to guarantee the existence of no duality gap for the optimal power flow problem, which is the dual of an equivalent form of the OPF problem.
Journal ArticleDOI
A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems
Daniel K. Molzahn,Florian Dörfler,Henrik Sandberg,Steven H. Low,Sambuddha Chakrabarti,Ross Baldick,Javad Lavaei +6 more
TL;DR: This paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.
Book
Dynamic Network Energy Management Via Proximal Message Passing
TL;DR: It is shown that this message passing method Converges to a solution when the device objective and constraints are convex, and the method is fast enough that even a serial implementation can solve substantial problems in reasonable time frames.
Journal ArticleDOI
Convex Relaxation for Optimal Power Flow Problem: Mesh Networks
TL;DR: In this article, a convex relaxation based on semidefinite programming (SDP) is shown to find a global solution of OPF for IEEE benchmark systems, and moreover this technique is guaranteed to work over acyclic (distribution) networks.
Journal ArticleDOI
Geometry of Power Flows and Optimization in Distribution Networks
TL;DR: It is shown that under the practical condition that the angle difference across each line is not too large, the set of Pareto-optimal points of the injection region remains unchanged by taking the convex hull and the resulting convexified optimal power flow problem can be efficiently solved via semi-definite programming or second-order cone relaxations.