Spatial-Temporal Distribution Model of Electric Vehicle Charging Demand Based on a Dynamic Evolution Process

Su, Shu; Yan, Hui; Ding, Ning; Li, Peijun

doi:10.1109/ei2.2018.8582169

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…With the idea in [27,28], the power regulation of CS can meet the load reduction requirement by finding the optimal combination of charging piles to control. With the objective of minimum deviation between the demand and the response, the optimal numbers for controlling in each cluster can be obtained according to (20) and (21).…”

Section: Peak Shaving Response Of Clustered Charging Pilesmentioning

confidence: 99%

“…ese methods can effectively identify the changing trend of the electricity consumption profiles, but the quality of the algorithm is greatly affected by the large variation in data. e charging profiles of PEVs have large volatility [20] which require the robustness of the clustering method. Even for the same CS, there are load peaks with different amplitudes at different occurrence times [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

Zhang

Yao

et al. 2022

International Transactions on Electrical Energy Systems

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The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027.

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Section: Peak Shaving Response Of Clustered Charging Pilesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

Zhang

Yao

et al. 2022

International Transactions on Electrical Energy Systems

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“…By classifying the three levels, the charging speed and price were calculated, using ArcGIS, to improve the utilization rate. Su et al ( 13 ) developed the Agent-CA model to simulate the EV driving process as well as evaluate the EV charging load and random traffic conditions and predict the size and location of the charging stations. Software called CRUISE was used to calculate the power consumption under different conditions.…”

Section: Related Workmentioning

confidence: 99%

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Chen

Kang

Koo

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Historically the U.S. transportation system has been continuously improved to adopt new policies and technologies. The transportation vehicle energy transition from fossil fuels to electricity is promoted by policymakers and major automakers with an expectation of enhancing sustainability aspects such as fossil fuel consumption reduction, carbon emissions reduction, and lower operations and maintenance costs. However, the electrification of the existing transportation infrastructure system requires substantial upgrades to overcome two major concerns from ordinary drivers and the public. One is the driver’s range anxiety based on the current capability of the electric vehicle (EV) technologies. The other is the availability of EV charging stations near the planned route. To address these two issues, we introduce an agent-based simulation model to project the consequences of electrification in the Indiana state highway system. Specifically, the model is developed to monitor the status of long-distance EV trips between different regions. The multi-agent engine method guarantees the model can adapt to diverse scenarios and complex environments. The simulation experiment verifies that the proposed model can provide the expected outcomes, including the numerical data of electric energy demand and the geospatial information (as location coordinates) of failed trips. By performing a GIS-based analysis of the results, the derived geospatial data can help state transportation agencies determine where to deploy the charging facilities to satisfy the overall charging demand. The proposed simulation framework offers a novel and strategic way to resolve the challenges for EV charging-related research and projects.

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“…On the basis of the survey data of the National Household Vehicle Travel Survey (NHTS) released by the United States [7], assuming that electric cars have the same travel pattern as fuel cars, the fitted curves of departure time, charging time, and daily driving distance are obtained. In [8,9], the Monte Carlo random sampling method is used to simulate the charging load distribution of electric vehicles. In [10,11], the spatial and temporal distribution characteristics of charging loads of different types of EVs in different charging areas and on different typical days are studied according to the scale of EVs and the development level of charging facilities in China and taking into account the randomness of EV movements.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

et al. 2023

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The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load.

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Spatial-Temporal Distribution Model of Electric Vehicle Charging Demand Based on a Dynamic Evolution Process

Cited by 8 publications

References 26 publications

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

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