ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Amini, M. Hadi; Kargarian, Amin; Karabaşoğlu, Orkun

doi:10.1016/j.epsr.2016.06.003

Cited by 295 publications

(110 citation statements)

References 41 publications

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“…The focus of these papers is varied: charging infrastructure placement problems (González, 2014;Jamian et al, 2014;Lam et al, 2014;Yi and Bauer, 2016), impact of EV charging on distribution systems (Maheswari et al, 2014), or demand simulation and prediction models (Amini et al, 2016;Bae and Kwasinski, 2012;Fan, 2012;Li and Zhang, 2012). Charging service demand, or simply charging demand, is a crucial part of all of them, because any problem of this kind ultimately depends on the EV owners' charging behaviour and the patterns of use of their vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

A Model of Charging Service Demand for the Czech Republic

Pekárek

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The paper introduces a standalone model of electric vehicle charging demand based on large-scale travel survey data of the Czech Republic. This demand model has been intended as a comprehensive input model for following charging infrastructure problem, where a spatial view of charging demand is usually needed. The model uses publicly available data, whose mutual incompatibility and information richness had to be overcome. The necessary data transformations are described and final data representation in the form of a mathematical graph allows the introduction of a point-defined (vertex-defined) charging demand model. Several drawbacks of the model are identified and their effect, as well as an application of whole model, is demonstrated on the large-scale numerical example. Sound demand model is a cornerstone for demand-related problems, such as general large-scale charging infrastructure problem, which is a common issue for countries that stand at the very beginning of the electric vehicle adoption process.

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Section: Literature Reviewmentioning

confidence: 99%

A Model of Charging Service Demand for the Czech Republic

Pekárek

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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“…A comprehensive framework for this pricing scheme can be complicated, where the operator has to consider the energy that it buys from the upstream grid, which might be uncertain, and then sell it to consumers considering the proposed pricing scheme in this paper. This leads to a complex mathematical optimization, which is usually solved through a typical stochastic programming approach [9,[22][23][24][25]. Please note that this comprehensive framework is not the focus of our current …”

Section: Microgrid Pricing Schemementioning

confidence: 99%

Microgrids Real-Time Pricing Based on Clustering Techniques

Liu

Mahmoudi²,

Chen³

2018

Energies

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Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers' pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers' load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

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“…In [4][5][6][7], the effects on the distribution system by the charging of the EVs have been discussed. In [8][9][10][11][12], the smart charging techniques that consider the pricing, the running pattern of the EV, and the power system operation have been discussed. The study on the reverse power transmission using the EVs, i.e., the vehicle-to-grid (V2G) system, has been performed.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Electricity Prices Using the Train to Grid (T2G) System in Urban Railway

Cho

Kim

et al. 2018

Energies

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Smart transportation technologies are being rapidly developed for enhancing the smart grid establishment. Such technologies are mostly focused on electric vehicles. However, the electric railroad has advantages in various aspects such as facility construction and utilization over an electric vehicle. Therefore, in this paper, we introduce the train-to-grid system using the electric railroads for the smart grid, and propose a reduction method for the electricity prices. The proposed method obtains actual data from the currently operating railroad systems. Furthermore, the number of trains for charging and discharging batteries is decided by using the time-of-use price and the number of railroad operations. The electricity prices are then determined by the energy consumption calculated using the number of trains used for charging and discharging and the capacity of the energy storage system in the trains. The proposed method is simulated using real data, and its superiority is verified by comparing its electric prices with the conventional electricity prices.

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ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Cited by 295 publications

References 41 publications

A Model of Charging Service Demand for the Czech Republic

A Model of Charging Service Demand for the Czech Republic

Microgrids Real-Time Pricing Based on Clustering Techniques

Reduction of Electricity Prices Using the Train to Grid (T2G) System in Urban Railway

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