Article records
https://feeds.library.caltech.edu/people/Gan-Lingwen/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 13:37:06 +0000Energy-efficient congestion control
https://resolver.caltech.edu/CaltechAUTHORS:20130109-105014583
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Walid-Anwar', 'name': {'family': 'Walid', 'given': 'Anwar'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2012
DOI: 10.1145/2318857.2254770
Various link bandwidth adjustment mechanisms are being
developed to save network energy. However, their interaction
with congestion control can significantly reduce throughput,
and is not well understood. We firstly put forward an easily
implementable link dynamic bandwidth adjustment (DBA)
mechanism, and then study its iteration with congestion control.
In DBA, each link updates its bandwidth according
to an integral control law to match its average buffer size
with a target buffer size. We prove that DBA reduces link
bandwidth without sacrificing throughput-DBA only turns
off excess bandwidth-in the presence of congestion control.
Preliminary ns2 simulations confirm this result.https://authors.library.caltech.edu/records/dajde-cy387Optimal Decentralized Protocol for Electric Vehicle Charging
https://resolver.caltech.edu/CaltechAUTHORS:20131003-094643635
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Topcu-U', 'name': {'family': 'Topcu', 'given': 'Ufuk'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2013
DOI: 10.1109/TPWRS.2012.2210288
We propose a decentralized algorithm to optimally schedule electric vehicle (EV) charging. The algorithm exploits the elasticity of electric vehicle loads to fill the valleys in electric load profiles. We first formulate the EV charging scheduling problem as an optimal control problem, whose objective is to impose a generalized notion of valley-filling, and study properties of optimal charging profiles. We then give a decentralized algorithm to iteratively solve the optimal control problem. In each iteration, EVs update their charging profiles according to the control signal broadcast by the utility company, and the utility company alters the control signal to guide their updates. The algorithm converges to optimal charging profiles (that are as "flat" as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update. Moreover, the algorithm only requires each EV solving its local problem, hence its implementation requires low computation capability. We also extend the algorithm to track a given load profile and to real-time implementation.https://authors.library.caltech.edu/records/6bhnt-fy360Exact convex relaxation for optimal power flow in distribution networks
https://resolver.caltech.edu/CaltechAUTHORS:20161025-153928779
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Li-Na', 'name': {'family': 'Li', 'given': 'Na'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}, {'id': 'Topcu-U', 'name': {'family': 'Topcu', 'given': 'Ufuk'}}]}
Year: 2013
DOI: 10.1145/2494232.2465535
The optimal power flow (OPF) problem seeks to control the power generation/consumption to minimize the generation cost, and is becoming important for distribution networks. OPF is nonconvex and a second-order cone programming (SOCP) relaxation has been proposed to solve it. We prove that after a "small" modification to OPF, the SOCP relaxation is exact under a "mild" condition. Empirical studies demonstrate that the modification to OPF is "small" and that the "mild" condition holds for all test networks, including the IEEE 13-bus test network and practical networks with high penetration of distributed generation.https://authors.library.caltech.edu/records/agta3-mdn57Optimal Power Flow in Direct Current Networks
https://resolver.caltech.edu/CaltechAUTHORS:20141205-135843283
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2014
DOI: 10.1109/TPWRS.2014.2313514
The optimal power flow (OPF) problem determines power generations/demands that minimize a certain objective such as generation cost or power loss. It is non-convex and NP-hard in general. In this paper, we study the OPF problem in direct current (DC) networks. A second-order cone programming (SOCP) relaxation is considered for solving the OPF problem. We prove that the SOCP relaxation is exact if either 1) voltage upper bounds do not bind; or 2) voltage upper bounds are uniform and power injection lower bounds are negative. Based on 1), a modified OPF problem is proposed, whose corresponding SOCP is guaranteed to be exact. We also prove that SOCP has at most one optimal solution if it is exact. Finally, we discuss how to improve numerical stability and how to include line constraints.https://authors.library.caltech.edu/records/zf7qf-8jb24Exact Convex Relaxation of Optimal Power Flow in Radial Networks
https://resolver.caltech.edu/CaltechAUTHORS:20150203-084513340
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Li-Na', 'name': {'family': 'Li', 'given': 'Na'}}, {'id': 'Topcu-U', 'name': {'family': 'Topcu', 'given': 'Ufuk'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2015
DOI: 10.1109/TAC.2014.2332712
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.https://authors.library.caltech.edu/records/7byye-f8609An Online Gradient Algorithm for Optimal Power Flow on Radial Networks
https://resolver.caltech.edu/CaltechAUTHORS:20160426-075434882
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2016
DOI: 10.1109/JSAC.2016.2525598
We propose an online algorithm for solving optimal power flow (OPF) problems on radial networks where the controllable devices continuously interact with the network that implicitly computes a power flow solution given a control action. Collectively the controllable devices and the network implement a gradient projection algorithm for the OPF problem in real time. The key design feature that enables this approach is that the intermediate iterates of our algorithm always satisfy power flow equations and operational constraints. This is achieved by explicitly exploiting the network to implicitly solve power flow equations for us in real time at scale. We prove that the proposed algorithm converges to the set of local optima and provide sufficient conditions under which it converges to a global optimum. We derive an upper bound on the suboptimality gap of any local optimum. This bound suggests that any local minimum is almost as good as any strictly feasible point. We explain how to greatly reduce the gradient computation in each iteration by using approximate gradient derived from linearized power flow equations. Numerical results on test networks, ranging from 42-bus to 1990-bus, show a great speedup over a second-order cone relaxation method with negligible difference in objective values.https://authors.library.caltech.edu/records/hb1hg-80v18