A Revenue Management Approach for Efficient Electric Vehicle Charging Coordination

Flath, Christoph M.; Gottwalt, Sebastian; Ilg, Jens P.

doi:10.1109/hicss.2012.79

Cited by 12 publications

(6 citation statements)

References 26 publications

(33 reference statements)

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“…Lopes et al (2010) base charging speed on local grid parameters to enable short-term reactions. Heterogeneous customer valuations for charging are the basis for the application of revenue management techniques as in Flath, Gottwalt, and Ilg (2012) and online mechanisms by Gerding et al (2011).…”

Section: Ev Charging Coordinationmentioning

confidence: 99%

Improving Electric Vehicle Charging Coordination Through Area Pricing

Flath

Ilg

Gottwalt

et al. 2014

Transportation Science

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M eeting charging demands of large electric vehicle fleets will raise electrical load significantly and may pose challenges for today's power system. Appropriate coordination of electric vehicle charging can reduce these threats. Acknowledging the interdependency between the transportation and the power system created by electric vehicles, we develop a charging coordination model based on German mobility data. We extend the prior work by explicitly accounting for both the temporal and the spatial dimension. We are thus able to analyze the loads from price-based EV fleet charging while at the same time accounting for distribution grid constraints. Furthermore, we propose a heuristic charging strategy based on limited trip and price information. Our results show that the sole use of time-based electricity prices for the coordination of electric vehicle charging produces high load spikes independent of the charging strategies and power levels. These peaks are induced by simultaneous charging activity and may cause stability problems within distribution grids in residential areas. To mitigate these load spikes, we introduce a spatial price component that reflects local capacity utilization. These local prices induce both a temporal and spatial shift of charging activity that mitigates the load spikes.

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Section: Ev Charging Coordinationmentioning

confidence: 99%

Improving Electric Vehicle Charging Coordination Through Area Pricing

Flath

Ilg

Gottwalt

et al. 2014

Transportation Science

Self Cite

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“…To the best of the authors' knowledge, the first study where revenue management was applied for electric vehicle charging was that of Flath et al [43]. The optimization objective was to minimize disruption at the distribution network level and to balance energy demand with supply.…”

Section: Revenue Management For Electric Vehicle Chargingmentioning

confidence: 99%

“…Compared to [43], our methodological approach adds the physical dimension of charging-post availability, moving from a single-resource (energy amount) to a dual-resource allocation. Most importantly, it uses a sophisticated representation of charging demand, and it enables customer segmentation using both quantitative and qualitative choice parameters.…”

Section: Revenue Management For Electric Vehicle Chargingmentioning

confidence: 99%

Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

2021

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The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.

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“…Whether the hypothesis of probabilistic model is reasonable or not is directly related to the usability of the generated EV load profiles. Since daily routines and the lifestyle are varied [20][21][22], there is generally no unique or canonical distribution type suitable for modeling EV loads.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder

et al. 2019

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Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.

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A Revenue Management Approach for Efficient Electric Vehicle Charging Coordination

Cited by 12 publications

References 26 publications

Improving Electric Vehicle Charging Coordination Through Area Pricing

Improving Electric Vehicle Charging Coordination Through Area Pricing

Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder

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