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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 13:37:06 +0000Optimal decentralized protocol for electric vehicle charging
https://resolver.caltech.edu/CaltechAUTHORS:20131003-100205684
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'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2011
DOI: 10.1109/CDC.2011.6161220
Motivated by the power-grid-side challenges in the integration of electric vehicles, we propose a decentralized protocol for negotiating day-ahead charging schedules for electric vehicles. The overall goal is to shift the load due to electric vehicles to fill the overnight electricity demand valley. In each iteration of the proposed protocol, electric vehicles choose their own charging profiles for the following day according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. This protocol is guaranteed to converge, irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. At convergence, the l2 norm of the aggregated demand is minimized, and the aggregated demand profile is as "flat" as it can possibly be. The proposed protocol needs no coordination among the electric vehicles, hence requires low communication and computation capability. Simulation results demonstrate convergence to optimal collections of charging profiles within few iterations.https://authors.library.caltech.edu/records/cjrzr-pn804Energy-efficient congestion control
https://resolver.caltech.edu/CaltechAUTHORS:20161025-154452155
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Walid-A', 'name': {'family': 'Walid', 'given': 'Anwar'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2012
DOI: 10.1145/2254756.2254770
Various link bandwidth adjustment mechanisms are being developed to save network energy. However, their interaction with congestion control can significantly reduce network throughput, and is not well understood. We firstly put forward a framework to study this interaction, and then propose an easily implementable dynamic bandwidth adjustment (DBA) mechanism for the links. 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/dnr2g-fc688Stochastic distributed protocol for electric vehicle charging with discrete charging rate
https://resolver.caltech.edu/CaltechAUTHORS:20170810-112506562
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: 2012
DOI: 10.1109/PESGM.2012.6344847
To address the grid-side challenges associated with the anticipated high electric vehicle (EV) penetration level, various charging protocols have been proposed in the literature. Most if not all of these protocols assume continuous charging rates and allow intermittent charging. However, due to charging technology limitations, EVs can only be charged at a fixed rate, and the intermittency in charging shortens the battery lifespan. We consider these charging requirements, and formulate EV charging scheduling as a discrete optimization problem. We propose a stochastic distributed algorithm to approximately solve the optimal EV charging scheduling problem in an iterative procedure. In each iteration, the transformer receives charging profiles computed by the EVs in the previous iteration, and broadcasts the corresponding normalized total demand to the EVs; each EV generates a probability distribution over its potential charging profiles accordingly, and samples from the distribution to obtain a new charging profile. We prove that this stochastic algorithm almost surely converges to one of its equilibrium charging profiles, and each of its equilibrium charging profiles has a negligible sub-optimality ratio. Case studies corroborate our theoretical results.https://authors.library.caltech.edu/records/1n3dg-b2s46Some Problems in Demand Side Management
https://resolver.caltech.edu/CaltechAUTHORS:20130815-100621563
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Jiang-Libin', 'name': {'family': 'Jiang', 'given': 'Libin'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven'}, 'orcid': '0000-0001-6476-3048'}, {'id': 'Topcu-U', 'name': {'family': 'Topcu', 'given': 'Ufuk'}}, {'id': 'Zhao-Changhong', 'name': {'family': 'Zhao', 'given': 'Changhong'}, 'orcid': '0000-0003-0539-8591'}]}
Year: 2012
We present a sample of problems in demand side
management in future power systems and illustrate how they
can be solved in a distributed manner using local information.
First, we consider a set of users served by a single load-serving
entity (LSE). The LSE procures capacity a day ahead. When
random renewable energy is realized at delivery time, it manages
user load through real-time demand response and purchases
balancing power on the spot market to meet the aggregate
demand. Hence optimal supply procurement by the LSE and the
consumption decisions by the users must be coordinated over two
timescales, a day ahead and in real time, in the presence of supply
uncertainty. Moreover, they must be computed jointly by the
LSE and the users since the necessary information is distributed
among them. We present distributed algorithms to maximize
expected social welfare. Instead of social welfare, the second
problem is to coordinate electric vehicle charging to fill the valleys
in aggregate electric demand profile, or track a given desired
profile. We present synchronous and asynchronous algorithms
and prove their convergence. Finally, we show how loads can
use locally measured frequency deviations to adapt in real time
their demand in response to a shortfall in supply. We design
decentralized demand response mechanism that, together with
the swing equation of the generators, jointly maximize disutility
of demand rationing, in a decentralized manner.https://authors.library.caltech.edu/records/a5yj9-kj319On the exactness of convex relaxation for optimal power flow in tree networks
https://resolver.caltech.edu/CaltechAUTHORS:20131220-094526108
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'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2012
DOI: 10.1109/CDC.2012.6426045
The optimal power flow problem is nonconvex, and a convex relaxation has been proposed to solve it. We prove that the relaxation is exact, if there are no upper bounds on the voltage, and any one of some conditions holds. One of these conditions requires that there is no reverse real power flow, and that the resistance to reactance ratio is non-decreasing as transmission lines spread out from the substation to the branch buses. This condition is likely to hold if there are no distributed generators. Besides, avoiding reverse real power flow can be used as rule of thumb for placing distributed generators.https://authors.library.caltech.edu/records/xqngg-p9h45Branch flow model for radial networks: convex relaxation
https://resolver.caltech.edu/CaltechAUTHORS:20170810-114313789
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'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2012
Power flow optimization is generally nonlinear and non-convex, and a second-order cone relaxation has been proposed recently for convexification. We prove several sufficient conditions under which the relaxation is exact. One of these conditions seems particularly realistic and suggests guidelines on integrating distributed generations.https://authors.library.caltech.edu/records/0vwxf-41144An optimization-based demand response in radial distribution networks
https://resolver.caltech.edu/CaltechAUTHORS:20130730-133244823
Authors: {'items': [{'id': 'Li-Na', 'name': {'family': 'Li', 'given': 'Na'}}, {'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Chen-Lijun', 'name': {'family': 'Chen', 'given': 'Lijun'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2012
DOI: 10.1109/GLOCOMW.2012.6477803
Demand response has recently become a topic of active research. Most of work however considers only the balance between aggregate load and supply, and abstracts away the underlying power network and the associated power flow constraints and operating constraints. In this paper, we study demand response in a radial distribution network, by formulating it as an optimal power flow problem that maximizes the aggregate user utilities and minimizes the power line losses, subject to the power flow constraints and operating constraints. As the resulting problem is non-convex and difficult to solve, we propose a convex relaxation that is usually exact for the real-world distribution circuits. We then propose a distributed algorithm for the load-serving entity to set the price signal to coordinate the users' demand response so as to achieve the optimum. Numerical examples show that the proposed algorithm converges fast for real-world distribution systems.https://authors.library.caltech.edu/records/qtmgt-jpt39Real-time deferrable load control: handling the uncertainties of renewable generation
https://resolver.caltech.edu/CaltechAUTHORS:20161025-161438752
Authors: {'items': [{'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Wierman-A', 'name': {'family': 'Wierman', 'given': 'Adam'}}, {'id': 'Topcu-U', 'name': {'family': 'Topcu', 'given': 'Ufuk'}}, {'id': 'Chen-Niangjun', 'name': {'family': 'Chen', 'given': 'Niangjun'}, 'orcid': '0000-0002-2289-9737'}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}]}
Year: 2013
DOI: 10.1145/2487166.2487179
Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation. In this paper, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. At every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of the algorithm vanishes as time horizon expands. Further, we evaluate the algorithm via trace-based simulations.https://authors.library.caltech.edu/records/m4gqe-7j072Exact Convex Relaxation for Optimal Power Flow in Distribution Networks
https://resolver.caltech.edu/CaltechAUTHORS:20131008-160444682
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/2d15y-8ry21Optimal power flow in tree networks
https://resolver.caltech.edu/CaltechAUTHORS:20170810-113418095
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: 2013
DOI: 10.1109/CDC.2013.6760226
The optimal power flow (OPF) problem seeks to control power generation/demand to optimize certain objectives such as minimizing the generation cost or power loss. It is becoming increasingly important for tree distribution networks due to the emerging distributed generation and controllable loads. The OPF problem is nonconvex. We prove that after modifying the OPF problem, its global optimum can be recovered via a second-order cone programming (SOCP) relaxation for tree networks, under a condition that can be checked in advance. Empirical studies justify that the modification is "small", and that the condition holds, for the IEEE 13-bus network and two real-world networks.https://authors.library.caltech.edu/records/16qrb-p1t76Optimal Power Flow in Direct Current Networks
https://resolver.caltech.edu/CaltechAUTHORS:20170810-113122736
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: 2013
DOI: 10.1109/CDC.2013.6760774
The optimal power flow (OPF) problem seeks to control power generation/demand to optimize certain objectives such as minimizing the generation cost or power loss. Direct current (DC) networks (e.g., DC-microgrids) are promising to incorporate distributed generation. This paper focuses on the OPF problem in DC networks. The OPF problem is nonconvex, and we study solving it via a second-order cone programming (SOCP) relaxation. In particular, we prove that the SOCP relaxation is exact if there are no voltage upper bounds, and that the SOCP relaxation has at most one solution if it is exact.https://authors.library.caltech.edu/records/1dyvw-6kd28Chordal relaxation of OPF for multiphase radial networks
https://resolver.caltech.edu/CaltechAUTHORS:20150203-145330203
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/ISCAS.2014.6865509
We formulate optimal power flow problem for unbalanced multiphase radial networks. We show that there is an equivalent single-phase mesh network that has a radial structure at the macro-level and a clique structure corresponding to each line in the radial network. Existing sufficient conditions for exact semidefinite relaxation are therefore applicable to unbalanced multiphase networks. In particular, they imply that if a semidefinite relaxation is exact over each of the cliques in the mesh equivalent network, then it is exact for the entire network.https://authors.library.caltech.edu/records/szyhw-g8827Convexification of AC optimal power flow
https://resolver.caltech.edu/CaltechAUTHORS:20170123-174612725
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/PSCC.2014.7038373
This overview paper summarizes the key elements of semidefinite relaxations of the optimal power flow problem, and discusses several open challenges.https://authors.library.caltech.edu/records/qy345-88r41Convex relaxations and linear approximation for optimal power flow in multiphase radial networks
https://resolver.caltech.edu/CaltechAUTHORS:20170810-114020714
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/PSCC.2014.7038399
Distribution networks are usually multiphase and radial. To facilitate power flow computation and optimization, two semidefinite programming (SDP) relaxations of the optimal power flow problem and a linear approximation of the power flow are proposed. We prove that the first SDP relaxation is exact if and only if the second one is exact. Case studies show that the second SDP relaxation is numerically exact and that the linear approximation obtains voltages within 0.0016 per unit of their true values for the IEEE 13, 34, 37, 123-bus networks and a real-world 2065-bus network.https://authors.library.caltech.edu/records/1fcqw-beg37Distributional analysis for model predictive deferrable load control
https://resolver.caltech.edu/CaltechAUTHORS:20170810-105540217
Authors: {'items': [{'id': 'Chen-Niangjun', 'name': {'family': 'Chen', 'given': 'Niangjun'}, 'orcid': '0000-0002-2289-9737'}, {'id': 'Gan-Lingwen', 'name': {'family': 'Gan', 'given': 'Lingwen'}}, {'id': 'Low-S-H', 'name': {'family': 'Low', 'given': 'Steven H.'}, 'orcid': '0000-0001-6476-3048'}, {'id': 'Wierman-A', 'name': {'family': 'Wierman', 'given': 'Adam'}}]}
Year: 2014
DOI: 10.1109/CDC.2014.7040398
Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. Though the average-case performance of model predictive deferrable load control has been analyzed in prior works, the distribution of the performance has been elusive. In this paper, we prove strong concentration results on the load variation obtained by model predictive deferrable load control. These results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance.https://authors.library.caltech.edu/records/10c4g-rc417