Demand side management of electric car charging: Benefits for consumer and grid

Finn, P.; Fitzpatrick, Colin; Connolly, David

doi:10.1016/j.energy.2012.03.042

Cited by 121 publications

(42 citation statements)

References 9 publications

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“…The introduction of EVs can not only drastically modify the overall load profile [1], but also introduce uncertainty in the grid since, with the Vehicle-to-Grid (V2G) functionality [2], EVs can also provide energy to the power grid by discharging the battery. Several studies in literature have focused on assessment of techno-economic potential of integrated EV-grid systems [3], on ancillary services that EVs can provide to the smart grid [4] and smart mobility [5] scenarios, on renewable energy sources penetration based on EV proliferation [6], and on cybersecurity architectures for smart EV charging [7]. All these studies agree on one crucial point: developing appropriate algorithms to control the charging/discharging process is of fundamental importance for the widespread diffusion of EVs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Asymptotic Tracking Control of Uncertain Time-Driven Switched Linear Systems

Yuan

Schutter

2017

IEEE Trans. Automat. Contr.

122

107

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Abstract-Managing grid-connected charging stations for fleets of electric-vehicles leads to an optimal control problem where user preferences must be met with minimum energy costs (e.g. by exploiting lower electricity prices through the day, renewable energy production, stored energy of parked vehicles). Instead of state-of-the-art charging scheduling based on open-loop strategies that explicitly depend on initial operating conditions, this paper proposes an approximate dynamic programming feedback-based optimization method with continuous state space and action space, where the feedback action guarantees uniformity with respect to initial operating conditions, while price variations in the electricity and available solar energy are handled automatically in the optimization. The resulting control action is a multi-modal feedback which is shown to handle a wide range of operating regimes, via a set of controllers whose action that can be activated or deactivated depending on availability of solar energy and pricing model. Extensive simulations via a charging test case demonstrate the effectiveness of the approach.

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Section: Introductionmentioning

confidence: 99%

Adaptive Asymptotic Tracking Control of Uncertain Time-Driven Switched Linear Systems

Yuan

Schutter

2017

IEEE Trans. Automat. Contr.

122

107

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“…At present the day-ahead auction market is the main wholesale electricity market in western European countries. In some countries such as Ireland (Finn et al, 2012) and the Iberian Peninsula (Tomé Saraiva, 2007;Camus et al, 2011) there is a pool, but in Scandinavia and in the Central West Europe market (Benelux, France and Germany) only part of the electricity is sold through the organized market.…”

Section: Introductionmentioning

confidence: 99%

Optimal recharging strategy for battery-switch stations for electric vehicles in France

et al. 2013

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Most papers that study the recharging of electric vehicles focus on charging the batteries at home and at the work-place. The alternative is for owners to exchange the battery at a specially equipped battery switch station (BSS). This paper studies strategies for the BSS to buy and sell the electricity through the day-ahead market. We determine what the optimal strategies would have been for a large fleet of EVs in 2010 and 2011, for the V2G and the G2V cases. These give the amount that the BSS should offer to buy or sell each hour of the day. Given the size of the fleet, the quantities of electricity bought and sold will displace the market equilibrium. Using the aggregate offers to buy and the bids to sell on the day-ahead market, we compute what the new prices and volumes transacted would be. While buying electricity for the G2V case incurs a cost, it is possible to generate revenue in the V2G case, if the arrivals of the EVs are evenly spaced during the day. We compare the total cost of implementing the strategies proposed with the cost of buying the same quantity of electricity from EDF.

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“…Several applications have been proposed in the literature [2], [12]- [15]. In [2] the authors describe how demand response using electric vehicle charging can be effectively used to provide significant gains without any further technological improvements, achieving financial savings in Ireland by optimizing the charging cycles of an EV. Authors in [12] proposes an optimal load management strategy for a residential consumer that uses the communication infrastructure of future smart grids.…”

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confidence: 99%

“…EVs have the possibility of providing a significant amount of DR through a variety of approaches while using their storage potential to enable a higher penetration of intermittent and variable generation such as wind and solar energy resources [11]. Several applications have been proposed in the literature [2], [12]- [15]. In [2] the authors describe how demand response using electric vehicle charging can be effectively used to provide significant gains without any further technological improvements, achieving financial savings in Ireland by optimizing the charging cycles of an EV.…”

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confidence: 99%

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Day-ahead resource scheduling including demand response for electric vehicles

Soares

Morais

Sousa

et al. 2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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Abstract-The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.Index Terms-Demand response, electric vehicle, energy re-source management, particle swarm optimization. NOMENCLATURE:Period duration (e.g., 15 min., 30 min., 1 hour) Grid-to-Vehicle efficiency when the vehicle is in charge mode Vehicle-to-Grid efficiency when the vehicle is in discharge mode Voltage angle at bus (rad) Maximum voltage angle at bus (rad) Minimum voltage angle at bus (rad) Voltage angle at bus (rad) Manuscript

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Demand side management of electric car charging: Benefits for consumer and grid

Cited by 121 publications

References 9 publications

Adaptive Asymptotic Tracking Control of Uncertain Time-Driven Switched Linear Systems

Adaptive Asymptotic Tracking Control of Uncertain Time-Driven Switched Linear Systems

Optimal recharging strategy for battery-switch stations for electric vehicles in France

Day-ahead resource scheduling including demand response for electric vehicles

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