Probabilistic modeling of nodal electric vehicle load due to fast charging stations

Tang, Difei; Wang, Peng; Wu, Qiuwei

doi:10.1109/pmaps.2016.7764219

Cited by 10 publications

(11 citation statements)

References 13 publications

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“…A multivariate joint distribution function is created using a copula function in [25], to characterize the dependence structure between three critical variables describing PEV travel patterns, which are charging stat time, charging end time, and traveled distance. Modeling the spatial-temporal dynamics of PEVs is studied in [26,27], in which a probabilistic modeling of trip chains is used to represent the spatial randomness of PEV movements. A trip chain is a time-ordered trip sequence, which consists of locations and routes of daily trips.…”

Section: Travel Pattern Modelmentioning

confidence: 99%

“…Secondly, PEV users may not accept long waiting queues at charging facilities. Planning of charging infrastructure must ensure a suitable number of charging facilities on demand zones to prevent traffic congestion [26]. On the other hand, the initial investment of charging infrastructure construction is more likely to take place in the current densely populated regions.…”

Section: Charging Infrastructure Planningmentioning

confidence: 99%

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A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Abdalrahman

Zhuang

2017

Energies

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Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

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Section: Travel Pattern Modelmentioning

confidence: 99%

Section: Charging Infrastructure Planningmentioning

confidence: 99%

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Abdalrahman

Zhuang

2017

Energies

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“…To compare the fuel efficiency of fixed speed and variable speed gas reciprocating engines in a HPC station application, it is necessary to develop a system utilization model that takes into consideration arrival rates of BEVs and the arrival SoC of each vehicle's battery. There are a number of probabilistic models that address BEV charging system planning but few focus on the expected arrival rates at an HPC station [9], [10], [11]. HPC stations will experience different arrival patterns depending on their location: an urban location may experience daily peak usage at commuting hours but a highway/motorway station may experience higher usage over the weekends or holidays.…”

Section: Utilisation Rates and Arrival Modelsmentioning

confidence: 99%

Co-location of CHP units for High Power Charging of Battery Electric Vehicles: A comparison of the fuel efficiency for AC and DC coupled systems

Smith

Galloway

Burt

2017

2017 IEEE Second International Conference on DC Microgrids (ICDCM)

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This paper proposes the co-location of gas reciprocating generator sets with High Power Charging (HPC) stations for Battery Electric Vehicles (BEVs), to offer a low carbon source of heat and power which could help to meet national government transportation and heating strategies while minimizing the impact that HPC systems will have on the power network. Three different Combined Heat and Power (CHP) connection configurations are considered to determine the most fuel-efficient per forecasted utilization rates of the HPC station. The use of variable speed generators connected directly to the dc bus of the HPC station can offer improved fuel-efficiency performance compared to fixed speed, especially under partloading conditions, however, the sizing of engine-generator sets based on expected utilization rates of the HPC station has the most influence on fuel efficiency. Keywords-battery electric vehicles, charging systems, combined heat and power, variable speed engines, fixed speed engines. I.

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“…According to the development of the EV and the charging station, many pieces of literature have been written to be able to study the effect of the large scale demand; in terms of the reduction of the steady state voltage stability, system losses, voltage profiles, voltage imbalance, and harmonics impact based on the EV charging model [3][4][5][6][7][8][9][10]. The impact of the EVs load had affected the electrical power system; in terms of power system stability.…”

Section: Introductionmentioning

confidence: 99%

“…The battery packs of vehicles were introduced to the power grid; based on normal operating condition. The time variability of the nodal on the EV load, was evaluated by the queuing theory; with spatial varies arrival and service rate that can be moderated by sharing the EVs charging demand [8]. The heuristic algorithms were applied in the optimal charging scheduling of EVs in the smart grid by using thermal line limits, the load on the transformer, voltage limits, and parking availability patterns.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Quick Charging Station for Electric Vehicles based on Location and Population Density Data

Kongjeen¹,

Junlakan²,

Bhumkittipich³

et al. 2018

IJIES

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This paper has presented the estimation methodology of the quick charging station for electric vehicles (EVs) based on both area and population density data. The proportion of EV owners per number of population in location data; is also used to compute the number of the quick charging stations. The population density data and proportion of EVs owners per number of population in area data are varied from 1 to 6 % and 0.01 to 0.8 %, respectively. The simulation results showed that the number of EVs stations increased and the randomly selected Feeder No.1 was installed at EVs stations; of which range from No.1 to No.4. The total real power loss increased up to 18%. Therefore, this study could be verified that the quick charging stations should be considered both optimal in sizing and location of EVs charging stations.

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Probabilistic modeling of nodal electric vehicle load due to fast charging stations

Cited by 10 publications

References 13 publications

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Co-location of CHP units for High Power Charging of Battery Electric Vehicles: A comparison of the fuel efficiency for AC and DC coupled systems

Estimation of the Quick Charging Station for Electric Vehicles based on Location and Population Density Data

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