Distributed Charge Scheduling of Plug-In Electric Vehicles Using Inter-Aggregator Collaboration

Mukherjee, Joy Chandra; Gupta, Arobinda

doi:10.1109/tsg.2016.2515849

Cited by 86 publications

(55 citation statements)

References 18 publications

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“…The rapid growth in urban mobility and the corresponding increase in energy use, greenhouse gas emissions, (GHG), air quality [1], and their externalities have driven more people to switch from their conventional cars to cleaner fleets, electric cars. The population of plug-in electric vehicles (PEVs) increased drastically over the past decade [2]. The PEVs are equipped with a large battery bank and the need for fast charging of these batteries and range anxiety issues impose an enormous load demand on the residential and distribution networks and this made many operation problems for the distribution system operator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

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The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

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Section: Introductionmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

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“…They focus on the mathematical programming based approaches, instead of on the market based mechanisms, as discussed in [13], [19], [20], [37], [38]. From another aspect, market mechanisms are frequently applied in a discrete time and dynamic charging scheduling environment [11], [23], [39], [40], rather than in the continuous time and reservation environment. Second, [23] and Vickrey-Clarke-Groves (VCG) auction [27]: Stackelberg game aims to analyze and predict the potential outcomes of the leader-follower interaction, however, we should develop a mechanism for EV charging scheduling with the bidding and payment rule such that the desired outcomes can arise naturally from the strategic interactions among users; moreover, instead of forcing users to truthfully report their private preferences through VCG mechanism, we expect participants to gain greater utility by revealing less privacy through an iterative bidding process [41].…”

Section: Related Workmentioning

confidence: 99%

Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

Hou

Chun

Yan

2020

IEEE Trans. Intell. Transport. Syst.

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This paper considers an electric vehicle charging scheduling setting where vehicle users can reserve charging time in advance at a charging station. In this setting, users are allowed to explicitly express their preferences over different start times and the length of charging periods for charging their vehicles. The goal is to compute optimal charging schedules that maximize the social welfare of all users given their time preferences and the state of charge of their vehicles. Assuming that users are self-interested agents who may behave strategically to advance their own benefits rather than the social welfare of all agents, we propose an iterative auction, which computes high-quality schedules and, at the same time, preserves users' privacy by progressively eliciting their preferences as necessary. We conduct a game theoretical analysis on the proposed iterative auction to prove its individual rationality and the best response for agents. Through extensive experiments, we demonstrate that the iterative auction can achieve high-efficiency solutions with a partial value information. Additionally, we explore the relationship between scheduling efficiency and information revelation in the auction.

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“…Although this is shown to improve the aggregators' energy costs, the authors do not consider price impact in their model and each aggregator performs independently. Another related work can be found in (Mukherjee & Gupta, 2017). Their work considers a scenario where several private aggregators are present in a given city, and negotiate with each other in order to balance charging in the different limitedly available charging stations.…”

Section: Multi-aggregator Scenariosmentioning

confidence: 99%

Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

Perez‐Diaz

Gerding²,

McGroarty³

2020

jair

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Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. In order to reduce electricity costs and lower the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to improve their personal utility. Hence, we study strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different attack vectors and propose a mathematical framework to quantify and detect manipulation. Moreover, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show the convergence of the coordination algorithm, and that the detection algorithm accurately detects deviating behaviour in up to 96% of the cases. 1 intermediary between a fleet of EVs and the electricity grid and markets. The aggregator is able to control the charging of its fleet, and this way informed collective decisions can be made. In contrast with individual EV operation, the much higher degree of coordination possible when a fleet is centrally managed by an aggregator offers great benefits. For example, electricity consumption to charge the fleet's batteries can be spread over time, avoiding expensive and polluting demand peaks. In particular, in this work we focus on EV aggregators participating in day-ahead markets, in order to purchase the electricity needed to meet their clients' energy requirements. In more detail, day-ahead markets match electricity supply and demand on an hourly basis (see Section 3), and are the main source of wholesale electricity. Here, increased electricity demand means increased prices, resulting in the so-called price impact, and hence it is in every market participant's interest to avoid unnecessary demand peaks.In this work we focus on a scenario where different EV aggregators co-exist in the same day-ahead market. These aggregators may vary in nature and size, but it is reasonable to assume that they are self-interested. Indeed, reduced electricity costs translate into more profit for the aggregator and/or more benefits for their EV fleet. In this scenario, reduced overall costs can be achieved by inter-aggregator coordination, producing more inf...

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Distributed Charge Scheduling of Plug-In Electric Vehicles Using Inter-Aggregator Collaboration

Cited by 86 publications

References 18 publications

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

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