Optimal decentralized protocol for electric vehicle charging

By Lingwen Gan, Ufuk Topcu, Steven H. Low. IEEE Transactions on Power Systems, 2011

1,114 citations recorded by the source corpus.

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deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update.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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a0d79338fb52ec77ec9af257b4978d69b02537bc

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Citations in the corpus

A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates (2017, background): “Some examples include distributed average consensus [2]–[4], distributed spectrum sensing [5], information control [6], [7], power systems control [8], [9], statistical inference and learning [10]–[12].”
Storage-like devices in load leveling: Complementarity constraints and a new and exact relaxation method (2015, methodology): “An SLD can be an EV [26], a battery, or a pumped storage device [39].”
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Differentially Private Distributed Constrained Optimization (2014, background): “…al. [19] proposed a distributed charging strategy based on the notion of valley-filling charging profiles, which is guaranteed to be optimal when all vehicles have identical The authors are with the Department of Electrical and System Engineering, University of Pennsylvania, Philadelphia, PA 19104.”
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs (2016, background): “Some examples include distributed averaging [7,50,72], distributed spectrum sensing [1], formation control [49, 58], power system control [21, 55], statistical inference and learning [20, 45, 53].”
Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects (2016, neutral): “4) Repeat step 2) and 3) until the optimal strategies proposed by all EVs no longer change.”
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A geometrically convergent method for distributed optimization over time-varying graphs (2016, background): “Examples and works include distributed averaging [1], [2], distributed spectrum sensing [3], formation control [4], [5], power system control [6], [7], statistical inference and learning [8]–[10], and so on.”
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Smart Charging Strategy for Electric Vehicle Charging Stations (2018, background): “The grid can then provide this information in real time to the individual PEV user [15], [16].”