A Distributed Charging Coordination for Large-Scale Plug-In Electric Vehicles Considering Battery Degradation Cost

Ma, Zhongjing; Zou, Suli; Liu, Xiangdong

doi:10.1109/tcst.2015.2394319

Cited by 133 publications

(87 citation statements)

References 29 publications

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“…The parameters γ i , i = 1, 2, · · · , 7 specified as −1.148 × 10 −7 , 3.9984 × 10 −8 , 1.3158 × 10 −7 , 5.5487 × 10 −10 , −4.968 × 10 −8 , −1.1166 × 10 −8 , and 6.1675 × 10 −9 , respectively [21]. For a detailed analyze about the battery degradation cost please refer to [23]. The first term of (5) is the monetary cost imposed on customer k due to buying power x h k for his PEV and buying power l …”

Section: Cost Functionmentioning

confidence: 99%

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

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Abstract-This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.Index Terms-Plug-in electric vehicles (PEVs), decentralized charging, Nash Folk strategy, regret matching.

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Section: Cost Functionmentioning

confidence: 99%

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…Each battery has a specific lifetime that is determined by the charging power and the charge-discharge cycles. Each time a battery is charged at a FCS, a certain amount of battery degrades, which is related to the charging duration and the charging power [38]. The expected battery degradation cost in the charging duration E(t k ch ) at FCS k is expressed as:…”

Section: Battery Degradation Modelmentioning

confidence: 99%

“…The values of a, b and c can be obtained once the battery type is known. The work in [38] provides the detailed information about the battery degradation cost and the parameter description.…”

Section: Battery Degradation Modelmentioning

confidence: 99%

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

et al. 2017

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Abstract:Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.

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“…In [34], a real-time price model is formulated as the derivative of the generation supply cost, which represents the marginal cost of the generation supply. Therefore, based on a similar concept in [35], the generation supply cost of ith PEV is supposed to…”

Section: Optimization Problem Formulation Of Pevsmentioning

confidence: 99%

Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

Zhao

Ding

2017

IEEE Trans. Ind. Inf.

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Abstract-This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative control strategy of PEVs is proposed to meet system interests while respecting each PEV's charging constraint. The proposed strategy is distributed, which only needs to be interacted with the neighbouring agents. Our analysis shows that the proposed strategy solves the optimal charging problem of PEVs in an initialization-free approach, which avoids any procedure for initialization during PEVs' charging process. Furthermore, the proposed strategy is robust to the time-varying available charging power and plug-and-play operations. The simulation studies validate the effectiveness of the proposed distributed strategy.

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A Distributed Charging Coordination for Large-Scale Plug-In Electric Vehicles Considering Battery Degradation Cost

Cited by 133 publications

References 29 publications

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

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