Grid Integration of Electric Vehicles for Economic Benefits: A Review

Patil, Harshavardhan; Kalkhambkar, Vaiju

doi:10.35833/mpce.2019.000326

Cited by 95 publications

(36 citation statements)

References 104 publications

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“…, minimum and maximum generation, ramping capabilities, minimum up and down times), and aggregate EV demand constraints. 178–180 Although there is not a standard set of aggregate EV constraints, PCMs generally include such constraints as daily energy demand constraints, charging/discharging power limits, and battery state-of-charge limits. 178,181,182…”

Section: Value Of Managed Charging In Analysis/modeling Studiesmentioning

confidence: 99%

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Anwar

Muratori

Jadun

et al. 2022

Energy Environ. Sci.

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Section: Value Of Managed Charging In Analysis/modeling Studiesmentioning

confidence: 99%

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Anwar

Muratori

Jadun

et al. 2022

Energy Environ. Sci.

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“…The Internet of Vehicles (IoV) applications are reshaping transportation systems by minimising human error, advancing travel convenience, and decreasing energy, operational and environmental costs [11,31,42]. EVs have emerged as a viable technology for reducing carbon emissions and travelling costs [43]. However, range anxiety is one of the major challenges of their wide adoption.…”

Section: Road Network Modelmentioning

confidence: 99%

A privacy preserving querying mechanism with high utility for electric vehicles

Atmaca¹,

Biswas²,

Maple³

et al. 2022

Preprint

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Recently, with the rising awareness about advancing towards a sustainable future, electric vehicles (EVs) are becoming more and more popular. As there are disproportionately fewer charging stations than EVs, range anxiety plays a major role in the rise in the number of queries made by EVs along their journeys to find or schedule a slot in the nearest available charging station, and a significant portion of such online querying is handled by third-party service providers and communicated via the Edge cloud. On the other hand, with the recent drive towards an information-based society, the use of personal data in various kinds of analytics is surging like never before. Hence, privacy threats to personal data and the risk of privacy violation are also increasing manifold. One of the recently popularised methods formalising utility-preserving location privacy is geo-indistinguishability (GeoI), a generalisation of the local variant of differential privacy (DP), which the state-of-the-art standard for privacy protection. However, the noise should be calibrated carefully, taking into account the implications for potential utility-loss for the users affecting their quality of service (QoS). In this paper, we focus on the environment where EVs dynamically query about charging stations along their journeys, and we introduce the idea of approximate geo-indistinguishability (AGeoI) on road networks which allows the EVs to obfuscate the individual query locations while ensuring that they remain within their preferred area of interest, as journeys are very sensitive to a sharp loss in QoS which may incur a high cost for the extra distance to be covered due to privacy. We propose a novel method to protect the privacy of EVs, both for the individual query locations and against the threat of tracing the trajectory of their journeys from adversarial third-parties, by applying AGeoI that enables the users to substantially preserve their QoS. We lay out an analytical insight on the working of our method and experimentally illustrate that under our method a very high fraction of the EVs attain privacy for free, and, in general, the utility-loss suffered due to gain in privacy is significantly low.

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“…In this context, as an emerging electricity seller that aggregates a large number of flexible user-side resources, LA has been developing rapidly [4]. The practical applications in Australia and Europe show that LA plays a crucial role in easing the peak power demand of power grid, slowing down the expansion of transmission and distribution capacity, and tapping the flexibility of user-side resources [5], [6]. However, user-side resources are characterized by various types, scattered layouts, different sizes, and uncertainty, which con- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).…”

Section: Introductionmentioning

confidence: 99%

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

Sun

Liu

Xiang

et al. 2022

Journal of Modern Power Systems and Clean Energy

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The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the operation mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

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Grid Integration of Electric Vehicles for Economic Benefits: A Review

Cited by 95 publications

References 104 publications

Assessing the value of electric vehicle managed charging: a review of methodologies and results

Assessing the value of electric vehicle managed charging: a review of methodologies and results

A privacy preserving querying mechanism with high utility for electric vehicles

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

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