A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles

Mu, Yunfei; Wu, Jianzhong; Jenkins, Nick; Jia, Hongjie; Wang, Chengshan

doi:10.1016/j.apenergy.2013.10.006

Cited by 349 publications

(149 citation statements)

References 21 publications

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“…Wirges et al (2012) combined a Spatial-Temporal model (STM) for the diffusion of EVs with an economic model for financing charging infrastructure investments for the city of Stuttgart, Germany. Mu et al (2014) also developed a STM to evaluate impacts of large scale deployment of EVs, using an urban distribution network on a high customer density in the United Kingdom Generic Distribution System as a test case. Similarly, Tu et al (2015) developed a STM to optimize charging locations for an EV taxi fleet in Shenzhen, China.…”

Section: Figure 1: Map Of the Stockholm Bus Network -Bus Routes (Leftmentioning

confidence: 99%

Locating charging infrastructure for electric buses in Stockholm

Xylia

Leduc

Patrizio

et al. 2017

Transportation Research Part C: Emerging Technologies

229

101

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Highlights Presenting an optimization model for the location of electric bus charging stations.  Model application to a large-scale case study for the bus network of Stockholm, Sweden.  Results show that low fuel costs for electricity can balance annual infrastructure costs.  Emissions decrease up to 51% and energy consumption up to 34% with electrification.  The model may assist decision-making for investments in public transport.Keywords electric bus; charging infrastructure; optimization; Mixed Integer Linear Programing; public transport; Sweden AbstractCharging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

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Section: Figure 1: Map Of the Stockholm Bus Network -Bus Routes (Leftmentioning

confidence: 99%

Locating charging infrastructure for electric buses in Stockholm

Xylia

Leduc

Patrizio

et al. 2017

Transportation Research Part C: Emerging Technologies

229

101

View full text Add to dashboard Cite

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“…According to the type of EVs, the distributions of battery capacities (Q v ) are shown in Table 1 [29], and the parameters in the distributions are further defined by (7) of Gamma distribution and (8) of normal distribution.…”

Section: Battery Type Capacity and Energy Consumptionmentioning

confidence: 99%

“…Furthermore, it is assumed that EV charging is controlled according to the Aggregator's market negotiations or the need of system operators. Smart charging is described by (10) with l sc (1:00) and r sc (5 h) [29]. Compared with ''dumb'' charging, the model of ''smart'' charging represents the shift of EV charging load from the system peak demand time to the valley hours.…”

Section: Formulation Of E-eppmentioning

confidence: 99%

A preventive control strategy for static voltage stability based on an efficient power plant model of electric vehicles

Wang

Jia

et al. 2015

J. Mod. Power Syst. Clean Energy

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With the increasing integration of wind farms and electric vehicles (EVs) in power systems, voltage stability is becoming more and more serious. Based on vehicle-to-grid (V2G), an efficient power plant model of EVs (E-EPP) was developed to estimate EV charging load with available corresponding response capacity under different charging strategies. A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin (VSM) of power system above a predefined security level. Two control modes were used including the disconnection of EV charging load ('V1G' mode) and the discharge of stored battery energy back to power grid ('V2G' mode). A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy. Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation, but also guarantee the travelling comfort for EV owners.

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“…Besides, to consider the stochastic natures of EV transportation variables, a joint distribution function with copula functions was developed in [7]. In [8], a spatialtemporal model based on intelligent transportation researches was proposed, and the origin and destination (OD) analysis was introduced to model the mobility of EVs. In [9], the diversity of vehicle users' using habits is considered, and its modified model, which considers the effect of road slope, was presented in [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, although the methods in [4][5][6][7] are easy to implement, their results are not credible enough, as they lack effective methods to model the inherent randomness of EVs. Despite various improvements have been presented in [8][9][10][11], the behavior characteristics of EVs are still described by traditional analytical methods. On the other hand, the method in [12] is clear and articulate, but it is too idealistic to consider the complex driving and charging behavior of EVs.…”

Section: Introductionmentioning

confidence: 99%

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Ban

2015

J. Mod. Power Syst. Clean Energy

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The usage of each private electric vehicle (PrEV) is a repeating behavior process composed by driving, parking, discharging and charging, in which PrEV shows obvious procedural characteristics. To analyze the procedural characteristics, this paper proposes a procedural simulation method. The method aggregates the behavior process regularity of the PrEV cluster to model the cluster's charging load. Firstly, the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles. Secondly, all the basic processes are set as a simulation starting point, and they are dynamically reconstructed by several constraints. The simulation continues until the steady state of charge (SOC) distribution and behavior regularity of the PrEV cluster are obtained. Lastly, based on the obtained SOC and behavior regularity information, the PrEV cluster's behavior processes are simulated again to make the aggregating charging load model available. Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster's procedural characteristics.

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A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles

Cited by 349 publications

References 21 publications

Locating charging infrastructure for electric buses in Stockholm

Locating charging infrastructure for electric buses in Stockholm

A preventive control strategy for static voltage stability based on an efficient power plant model of electric vehicles

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

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