Extended second price auctions for Plug-in Electric Vehicle (PEV) charging in smart distribution grids

Bhattacharya, Saptarshi; Kar, Koushik; Chow, Joe H.; Gupta, Aparna

doi:10.1109/acc.2014.6859300

Cited by 9 publications

(14 citation statements)

References 20 publications

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“…This result verifies the non‐increasing marginal valuation modeling assumptions made in the literature (Bhattacharya et al. , Galus and Andersson , Limmer , Robu et al. , Zheng and Shroff ).…”

Section: Model Descriptionsupporting

confidence: 86%

“…In the rest of the study, in accordance with the literature (Bhattacharya et al. , Galus and Andersson , Robu et al. , Zheng and Shroff ) and our findings, we assume a valuation function with non‐increasing marginal valuation for each extra electricity unit charged.…”

Section: Model Descriptionmentioning

confidence: 74%

“…This utility function

U (c_{t})

consists of the value that this amount of electricity has for an EV owner,

V (c_{t})

, reduced by the cost of this amount, c t · P t , ∀ t ∈ T (Bhattacharya et al. , Robu et al. ).…”

Section: Model Descriptionmentioning

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: Model Descriptionsupporting

confidence: 86%

Section: Model Descriptionmentioning

confidence: 74%

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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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“…PEV Charging: In a market capturing charging stations for plug-in electric vehicles, each good represents a time slot, and each buyer may desire specific (sets of) time slots based on time constraints and charging capacity. Varying demand and electricity generation costs necessitate differential pricing across time slots [6,32].…”

Section: Market Model: Item Pricing For Multi-minded Buyersmentioning

confidence: 99%

“…The revenue bound simply follows from the algorithm, so we focus on the social welfare here. Recall from Claim 5.2 that (i)SW * ≤ SW (0) + 5 + 6 1−α (π(−1) + π(0)). Moreover, applying Lemma 5.3 repeatedly for j = 0 to k and performing the telescoping summation, we get that (ii) : SW (0) − SW (k) ≤ 3π(0) + 6 k−1 j=1 π(j) + 3π(k).…”

Section: Final Pricing Algorithmmentioning

confidence: 99%

Pricing to Maximize Revenue and Welfare Simultaneously in Large Markets

Anshelevich

Kar

Sekar

2016

Web and Internet Economics

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We study large markets with a single seller which can produce many types of goods, and many multi-minded buyers. The seller chooses posted prices for its many items, and the buyers purchase bundles to maximize their utility. For this setting, we consider the following questions: What fraction of the optimum social welfare does a revenue maximizing solution achieve? Are there pricing mechanisms which achieve both good revenue and good welfare simultaneously? To address these questions, we give efficient pricing schemes which are guaranteed to result in both good revenue and welfare, as long as the buyer valuations for the goods they desire have a nice (although reasonable) structure, e.g., that the aggregate buyer demand has a monotone hazard rate or is not too convex. We also show that our pricing schemes have implications for any pricing which achieves high revenue: specifically that even if the seller cares only about revenue, they can still ensure that their prices result in good social welfare without sacrificing profit. Our results holds for general multi-minded buyers in large markets; we also provide improved guarantees for the important special case of unit-demand buyers.1 For convenience, we will use revenue and profit interchangeably in this work

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