Stochastic Programming Applied to EV Charging Points for Energy and Reserve Service Markets

Sánchez-Martin, Pedro; Lumbreras, Sara; Alberdi-Alen, Antonio

doi:10.1109/tpwrs.2015.2405755

Cited by 56 publications

(35 citation statements)

References 26 publications

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“…Similar to (Shi and Wong, 2011), we study price uncertainty in the context of V2G, but our study differs from theirs in that, Our algorithm is more scalable, thus it could be used when we consider the battery usage behavior as we plan for our future work. Though, they applied Q-learning, which does not work effectively when considering the battery usage behavior as we concluded from (Guo et al, 2004) In contrast to the aforementioned studies, (Ghiasnezhad Omran and Filizadeh, 2014), (Sanchez-Martin et al, 2015), (Valogianni et al, 2014), (Gonzalez Vaya and Andersson, 2013), and (Halvgaard et al, 2012) studies will be used as references of our model when we are going to model the driving behaviour in our future work. In details, (Ghiasnezhad Omran and Filizadeh, 2014) propose a procedure for location-based prediction of the possible vehicular charging load at charging stations.…”

Section: Related Workmentioning

confidence: 99%

“…In order to emulate drivers' charging behaviour they apply fuzzy decision-making systems. In a related vein, (Sanchez-Martin et al, 2015) argue that applying stochastic behaviour to manage EV charging points is more realistic and develop a stochastic programming model to achieve optimal management, taking into account price variations in day-ahead price markets. Along the same line, (Halvgaard et al, 2012) use Economic Model Predictive Control as a technique to reduce the cost of electricity consumption for a single EV.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Agent Trading on Behalf of V2G Drivers in a Day-ahead Price Market

Almansour¹,

Gerding²,

Wills³

2017

Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

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Abstract:Due to the limited availability of fuel resources, there is an urgent need for converting to use renewable sources efficiently. To achieve this, power consumers should participate actively in power production and consumption. Consumers nowadays can produce power and consume a portion of it locally, and then could offer the rest of the power to the grid. Vehicle-to-grid (V2G) which is one of the most effective sustainable solutions, could provide these opportunities. V2G can be defined as a situation where electric vehicles (EVs) offer electric power to the grid when parked. We developed an agent to trade on behalf of V2G users to maximize their profits in a day-ahead price market. We then ran the proposed model in three different scenarios using an optimal algorithm and compared the results of our solution to a benchmark. We show that our solution outperforms the benchmark strategy in the proposed three scenarios 49%, 51%, and 10% respectively in terms of profit.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Agent Trading on Behalf of V2G Drivers in a Day-ahead Price Market

Almansour¹,

Gerding²,

Wills³

2017

Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

View full text Add to dashboard Cite

show abstract

“…However, all the analyses are again conducted only for a single day and from the aspect of the EV owner as market participant. Stochastic EVs model is formulated in [26] where objective function incorporates multiple markets (day-ahead energy, stochastic intraday energy, regulating reserve) and costs (reserve compensation and driver satisfaction cost). The last mentioned cost represents penalties for non-supplied energy to EVs which results in a conclusion that committing EVs for reserve introduces profit reduction for EV.…”

Section: Main Contributions and Literature Overviewmentioning

confidence: 99%

“…This paper analyses only slow charging effect on the EPS so no additional description of fast charging model will be provided. Specific constraints for different charging modes are listed below (18)- (26). UCH: …”

Section: Electric Vehiclesmentioning

confidence: 99%

Value of flexible electric vehicles in providing spinning reserve services

2015

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“…Instead, required electricity is ordered as late as possible, regardless of the electricity prices. Sánchez-Martín et al [18] describe a two-stage approach to EV charging with trading on the electricity market: In the first stage, electricity is traded on the day-ahead market based on forecasts of EV staying patterns. In the second stage, deviations from the forecasts are handled by trading on the intraday market.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market

Naharudinsyah

Limmer

2018

Energies

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Trading on the energy market is a possible way to reduce the electricity costs of charging electric vehicles at public charging stations. In many European countries, it is possible to trade electricity until shortly before the period of delivery on so called intraday electricity markets. In the present work, the potential for reducing the electricity costs by trading on the intraday market is investigated using the example of the German market. Based on simulations, the authors reveal that by optimizing the charging schedule together with the trading on the intraday electricity market, the costs can be reduced by around 8% compared to purchasing all the required energy from the energy supplier. By allowing the charging station operator to resell the energy to the intraday electricity market, an additional cost reduction of around 1% can be achieved. Besides the potential cost savings, the impacts of the trading unit and of the lead time of the intraday electricity market on the costs are investigated. The authors reveal that the achievable electricity costs can be strongly affected by the lead time, while the trading unit has only a minor effect on the costs.

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Stochastic Programming Applied to EV Charging Points for Energy and Reserve Service Markets

Cited by 56 publications

References 26 publications

An Agent Trading on Behalf of V2G Drivers in a Day-ahead Price Market

An Agent Trading on Behalf of V2G Drivers in a Day-ahead Price Market

Value of flexible electric vehicles in providing spinning reserve services

Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market

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