Vehicle-to-Aggregator Interaction Game

Wu, Chenye; Mohsenian‐Rad, Hamed; Huang, Jianwei

doi:10.1109/tsg.2011.2166414

Cited by 368 publications

(222 citation statements)

References 27 publications

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“…The differences are not zeros due to the randomness of EV's WTP parameter. Similar to [17], we assume that the residue is further handled by a set of backup batteries to assure reaching the service request.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We then compare the proposed mechanism with the pricing scheme in [17] in terms of the total payment received by the aggregator. In [17], to achieve the service request, the aggregator randomly selects a certain number of EVs to charge/discharge their batteries at a fixed rate.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [17], to achieve the service request, the aggregator randomly selects a certain number of EVs to charge/discharge their batteries at a fixed rate. The aggregator will pay each selected EV a base price ω, which is the same for all selected EVs, and charge them penalty prices if the service request can not be reached.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Our work differs from the works in [13]- [16] primarily in that we focus on the scenario of providing ancillary services to the power grid through bidirectional power flows of EVs. In [17], Wu et al studied the problem of coordinating a large group of selfish and intelligent EVs to provide frequency regulation to the power gird and proposed a pricing scheme to accomplish the service request at the equilibrium. However, one major drawback is that they assume a homogeneous setting without taking into account different preferences of EVs.…”

Section: Related Workmentioning

confidence: 99%

“…Such a service request is accomplished by the aggregator through coordinating the N associated EVs to charge/discharge their batteries. Moreover, similar to [17], we assume that the aggregator is equipped with a set of backup batteries to assure reaching the service request.…”

Section: A V2g System Modelmentioning

confidence: 99%

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A contract-based approach for ancillary services in V2G networks: Optimality and learning

Gao

Chen

Wang

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A V2g System Modelmentioning

confidence: 99%

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A contract-based approach for ancillary services in V2G networks: Optimality and learning

Gao

Chen

Wang

et al. 2013

2013 Proceedings IEEE INFOCOM

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Demand Response at Distribution Grids: Exploiting Flexible Power Electronics Interfaces

Taylor

Mohsenian‐Rad

Davoudi

2016

Smart Grid Handbook

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We explore the unique opportunities and challenges that arise in developing demand response programs that are intended for power distribution systems. The key is to understand the characteristics of the underlying physical power system, such as whether the distribution system is of type AC or DC. The role of power electronics interfaces are of particular interest in this context. Looking at controllability as a resource for power electronics interfaces, new design potentials are explored and case studies are presented to achieve optimal demand response operation in power distribution systems.

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Reference

2018

Smart Grid Communication Infrastructures

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Vehicle-to-Aggregator Interaction Game

Cited by 368 publications

References 27 publications

A contract-based approach for ancillary services in V2G networks: Optimality and learning

A contract-based approach for ancillary services in V2G networks: Optimality and learning

Demand Response at Distribution Grids: Exploiting Flexible Power Electronics Interfaces

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