Patterns of electric vehicle charging with time of use rates: Case studies in California and Portland

Biviji, Mustafa A.; Uçkun, Canan; Bassett, Gilbert W.; Wang, Jianhui; Ton, Dan

doi:10.1109/isgt.2014.6816454

Cited by 20 publications

(12 citation statements)

References 1 publication

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“…Literature has found that EV charging price elasticities are different from the electricity consumption ones (Biviji et al. , Soltani et al. ), however, there is limited data about their values (California and Portland (Biviji et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…), however, there is limited data about their values (California and Portland (Biviji et al. ), 13 EV owner data and synthetic California data (Soltani et al. )).…”

Section: Literature Reviewmentioning

confidence: 99%

“…All these reasons have given rise to a stream of literature identifying major grid benefits from pricing EV charging differently than the rest of the household demand (Biviji et al. , Fridgen et al. , Hu et al.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Electric Vehicle Charging using Adaptive Pricing

Valogianni

Ketter

Collins

et al. 2020

Production and Operations Management

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A transition to electric vehicles (EVs) is widely assumed to be an important step along the road to environmental sustainability. However, large‐scale adoption of EVs may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low‐price periods, if large numbers of EV owners use smart charging to benefit from low prices. This effect is expected to be amplified when EV owners adopt smart decision support to assist them with optimal charging decisions. Therefore, energy policymakers are interested in advanced pricing schemes that can smooth demand or induce demand that comes as close as possible to a desired profile. We show, through simulations calibrated with real‐world data, that current approaches to electricity pricing are limited in their ability to induce desired demand profiles. To address this challenge, we present adaptive pricing, a method to learn from EV owner reactions to prices and adjust announced prices accordingly. Our method draws on the Green Information Systems principles and can assist grid operators in ensuring the reliable operation of the grid. We evaluate our results in simulations, where we find that adaptive pricing outperforms current electricity pricing schemes, yielding results close to the theoretically optimal ones. We test our method in inducing both flat and extremely volatile demand profiles, and we see that in both cases it manages to induce EV charging close to the ideal scenario under perfect information.

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Section: Literature Reviewmentioning

confidence: 99%

“…), however, there is limited data about their values (California and Portland (Biviji et al. ), 13 EV owner data and synthetic California data (Soltani et al. )).…”

Section: Literature Reviewmentioning

confidence: 99%

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Sustainable Electric Vehicle Charging using Adaptive Pricing

Valogianni

Ketter

Collins

et al. 2020

Production and Operations Management

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“…1.14   . The plug-in, plug-out time and daily travel miles of each EV are generated from probability distribution ( 16)- (18).…”

Section: Case Studiesmentioning

confidence: 99%

Optimal Scheduling of Electric Vehicles Charging in low-Voltage Distribution Systems

Zhang

Yan

et al. 2016

Journal of Electrical Engineering and Technology

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-Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios.

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“…Literature has demonstrated examples of how battery charging demands are controlled to minimize cost and maintain user convenience (Chung et al, 2019). Previous work has shown that EV owners who voluntarily select TOU rate structures reliably charge their vehicles during the off-peak period as shown in a demonstration project in Los Angeles California (Schey et al, 2012) and a case study in California and Portland (Biviji et al, 2014). Therefore, EV participation in TOU load shifting is likely to be high.…”

Section: Introductionmentioning

confidence: 99%