Value of V2G Frequency Regulation in Great Britain Considering Real Driving Data

Thingvad, Andreas; Calearo, Lisa; Andersen, Peter Bach; Marinelli, Mattia; Neaimeh, Myriam; Suzuki, Kenta; Murai, Kensuke

doi:10.1109/isgteurope.2019.8905679

Cited by 10 publications

(12 citation statements)

References 10 publications

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“…Furthermore, in [16], the potential is explored in terms of earnings through frequency containment reserves using V2G enabled EVs considering driving requirements and the efficiency of the V2G chargers used. In [17], the authors explore the earning potential for frequency regulation in the UK based on a large dataset of real EV driving data and historical market prices. Finally, the authors in [18] compare the earning potential of providing frequency containment reserves both with and without the use of V2G.…”

Section: State-of-the-artmentioning

confidence: 99%

The Parker Project: Cross-Brand Service Testing Using V2G

Andersen

Hashemi

Sousa

et al. 2019

WEVJ

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The Parker project sought to experimentally validate that contemporary series-produced electric vehicles (EVs), capable of V2G, are ready to participate in a number of advanced grid services. In such services, the timing, size and direction of power and energy exchanged between the EV battery and grid is controlled as to support either a single building, the local neighborhood or the regional power system. Vehicles purposely designed for such services are referred to as grid integrated electric vehicles (GIVs). The field of research, describing how GIVs may be used to actively support the power system, is called Vehicle–Grid Integration (VGI).The purpose of this paper is to present how the Danish Parker project has systematically categorized a range of grid services, collected in a service catalog, and then illustrate state-of-the-art EVs ability to support such services through experimental validation. Results are presented for three different tests performed in Parker; marginal emission factor charging, frequency containment reserves and a performance test for controlling power setpoints. The ultimate aim of this paper, and the Parker project, is to promote the GIV concept so that it may inform the design and capabilities of present and future EVs, EV supply equipment (EVSE) and communication standards.

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Section: State-of-the-artmentioning

confidence: 99%

The Parker Project: Cross-Brand Service Testing Using V2G

Andersen

Hashemi

Sousa

et al. 2019

WEVJ

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“…The rest of the paper is structured as follows: Section II includes the methodology. First the analysis of the provided driving data set, from Nissan [12], is made. In continuation, a description of the system frequency in Denmark follows and afterwards, the energy model of the battery is formulated.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Electric Vehicles for Ancillary Service Provision with Real Driving Data

Tsagkaroulis

Thingvad

Marinelli

et al. 2021

2021 56th International Universities Power Engineering Conference (UPEC)

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Electric Vehicles (EV)s can with the right charger and aggregated in large numbers be considered as a large storage unit. If the chargers have bidirectional power converters, EVs connected to the grid, could provide Vehicle-to-Grid (V2G) ancillary services. The annual earnings from delivering frequency-controlled normal operation reserve in Denmark is calculated, based on individual user profiles. The individual earnings are strongly dependant on the driving time, distance and parking time at different locations which determine the availability of each EV to provide ancillary services. The specific user behaviour is based on a set of telematics data acquired from 7,163 Nissan LEAFs in the United States, with information about every driving and charging sessions during one year. The profit of the individual EV, spreads from 51 to 1654 A C/year. A data set of one year of system frequency measurements from the Nordic grid is used to calculate the impact of the service provision on the State of charge (SOC).

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“…Meanwhile, vehicle-to-grid (V2G) technology [6] is being introduced, and EV batteries are also gaining recognition as an energy resource by supplying energy to the grid [7]. Therefore, considering that dynamic pricing and V2G are being introduced, it would be beneficial to schedule real-time charging/discharging in response to the price signal, as this makes it possible to minimize EV charging costs and ensure grid reliability [8]. However, this would be difficult to achieve as, practically, an EV user cannot control charging/discharging by monitoring the price signal in real-time.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme

Lee

Kim

2020

Energies

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In the smart grid environment, the penetration of electric vehicle (EV) is increasing, and dynamic pricing and vehicle-to-grid technologies are being introduced. Consequently, automatic charging and discharging scheduling responding to electricity prices that change over time is required to reduce the charging cost of EVs, while increasing the grid reliability by moving charging loads from on-peak to off-peak periods. Hence, this study proposes a deep reinforcement learning-based, real-time EV charging and discharging algorithm. The proposed method utilizes kernel density estimation, particularly the nonparametric density function estimation method, to model the usage pattern of a specific charger at a specific location. Subsequently, the estimated density function is used to sample variables related to charger usage pattern so that the variables can be cast in the training process of a reinforcement learning agent. This ensures that the agent optimally learns the characteristics of the target charger. We analyzed the effectiveness of the proposed algorithm from two perspectives, i.e., charging cost and load shifting effect. Simulation results show that the proposed method outperforms the benchmarks that simply model usage pattern through general assumptions in terms of charging cost and load shifting effect. This means that when a reinforcement learning-based charging/discharging algorithm is deployed in a specific location, it is better to use data-driven approach to reflect the characteristics of the location, so that the charging cost reduction and the effect of load flattening are obtained.

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Value of V2G Frequency Regulation in Great Britain Considering Real Driving Data

Cited by 10 publications

References 10 publications

The Parker Project: Cross-Brand Service Testing Using V2G

The Parker Project: Cross-Brand Service Testing Using V2G

Optimal Scheduling of Electric Vehicles for Ancillary Service Provision with Real Driving Data

Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme

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