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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 13:37:05 +0000Optimal Decentralized Protocols for Electric Vehicle Charging
https://resolver.caltech.edu/CaltechCDSTR:2011.009
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
We propose decentralized algorithms for optimally scheduling electric vehicle charging. The algorithms exploit the elasticity and controllability of electric vehicle related loads in order to fill the valleys in electric demand profile. We formulate a global optimization problem whose objective is to impose a generalized notion of valley-filling, study properties of the optimal charging profiles, and give decentralized offline and online algorithms to solve the problem. In each iteration of the proposed algorithms, electric vehicles choose their own charging profiles for the rest horizon according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. The offline algorithms are guaranteed to converge to optimal charging profiles irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles at the expense of a restrictive assumption that all electric vehicles are available for negotiation at the beginning of the planning horizon. The online algorithms relax this assumption by using a scalar prediction of future total charging demand at each time instance and yield near optimal charging profiles. The proposed algorithms need no coordination among the electric vehicles, hence their implementation requires low communication and computation capability. Simulation results are provided to support these results.https://authors.library.caltech.edu/records/fgea1-qdv23Stochastic Distributed Protocol for Electric Vehicle Charging with Discrete Charging Rate
https://resolver.caltech.edu/CaltechCDSTR:2011.011
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: 2011
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/d5316-qwp84Exact Convex Relaxation of Optimal Power Flow in Tree Networks
https://resolver.caltech.edu/CaltechAUTHORS:20190628-105122130
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: 2019
DOI: 10.48550/arXiv.1208.4076
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 in the network. It is becoming increasingly important for distribution networks, which are tree networks, due to the emergence of distributed generation and controllable loads. In this paper, we study the OPF problem in tree networks. The OPF problem is nonconvex. We prove that after a "small" modification to the OPF problem, its global optimum can be recovered via a second-order cone programming (SOCP) relaxation, under a "mild" condition that can be checked apriori. Empirical studies justify that the modification to OPF is "small" and that the "mild" condition holds for the IEEE 13-bus distribution network and two real-world networks with high penetration of distributed generation.https://authors.library.caltech.edu/records/ncdc2-0yt10Distributed Load Balancing with Nonconvex Constraints: A Randomized Algorithm with Application to Electric Vehicle Charging Scheduling
https://resolver.caltech.edu/CaltechAUTHORS:20190628-094804246
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: 2019
DOI: 10.48550/arXiv.1401.7604
With substantial potential to reduce green house gas emission and reliance on fossil fuel, electric vehicles (EVs) have lead to a booming industry, whose growth is expected to continue for the next few decades. However, EVs present themselves as large loads to the power grid. If not coordinated wisely, the charging of EVs will overload power distribution circuits and dramatically increase power supply cost. To address this challenge, significant amount of effort has been devoted in the literature to schedule the charging of EVs in a power grid friendly way. Nonetheless, the majority of the literature assumes that EVs can be charged intermittently at any power level below certain rating, while in practice, it is preferable to charge an EV consecutively at a pre-determined power to prolong the battery lifespan. This practical EV charging constraint is nonconvex and complicates scheduling. To schedule a large number of EVs with the presence of practical nonconvex charging constraints, a distributed
and randomized algorithm is proposed in this paper. The algorithm assumes the availability of a coordinator which can communicate with all EVs. In each iteration of the algorithm, the coordinator receives tentative charging profiles from the EVs and computes a broadcast control signal. After receiving this broadcast control signal, each EV generates a probability distribution over its admissible charging profiles, and samples from the distribution to update its tentative charging profile. We prove that the algorithm converges almost surely to a charging profile in finite iterations. The final charging profile (that the algorithm converges to) is random, i.e., it depends on the realization. We characterize the final charging profileāa charging profile can be a realization of the final charging profile if and only if it is a Nash equilibrium of the game in which each EV seeks to minimize the inner product of its own charging profile and the aggregate electricity demand. Furthermore, we provide a uniform suboptimality upper bound, that scales O(1=n) in the number n of EVs, for all realizations of the final charging profile.https://authors.library.caltech.edu/records/1caq0-gv292