Sensitivity analysis of electric vehicle impact on low-voltage distribution grids

Stiasny, Jochen; Zufferey, Thierry; Pareschi, Giacomo; Toffanin, Damiano; Hug, Gabriela; Boulouchos, Konstantinos

doi:10.1016/j.epsr.2020.106696

Cited by 36 publications

(15 citation statements)

References 9 publications

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“…The model can provide results for any given number of EVs under different charging scenarios. The model's EV power consumption estimations follow EV load estimated in [22,[27][28][29]. Particularly, the peak load values and peak load time period, and the load curve form follow nearly identical trends.…”

Section: Resultsmentioning

confidence: 75%

“…This model is further used to simulate the electricity demand of EVs in Belgium [26]. Traffic survey data are used to generate probability density functions (PDF) of trip related parameters such as arrival time, departure time and distance to make EV usage patterns in [27][28][29]. These travel patterns are later compared with actual EV charging data to generate SOC and EV load profiles.…”

Section: Overview Of the Existing Modelsmentioning

confidence: 99%

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Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

et al. 2021

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The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculated. The charging profiles are based on the measurements of several EVs.

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Section: Resultsmentioning

confidence: 75%

Section: Overview Of the Existing Modelsmentioning

confidence: 99%

Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

et al. 2021

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“…Several approaches for modeling EV load have been proposed in the past. According to Yi & Scoffield, 2018, we can find, for example, deterministic EV load modeling techniques (Kongjeen et al, 2019), Monte Carlo simulation approaches (MCS) (Li & Zhang, 2012), fuzzy methods (Shahidinejad et al, 2012), hybrid Fuzzy-MCS methods (Ahmadian et al, 2017) and many other techniques (Stiasny et al, 2021,Frendo et al, 2020 to model the EV load. In this paper, we intend to classify these methods into three groups: deterministic, data-driven, and uncertainty/variability approaches.…”

Section: Ev Charging Load Modelingmentioning

confidence: 99%

“…Although many studies mention differences between data-driven and machine learning techniques, we consider that both can be included into data-based approaches. We have found several approaches that use machine learning theory or concepts to model the EV load, charging behaviors, or driving patterns (Gerossier et al, 2019, Godde et al, 2015, Stiasny et al, 2021. Specifically, Gerossier et al, 2019 modeled the consumption profile of EVs from raw power measurements.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Review of Charging Load Modeling Strategies for Electric Vehicles: a Comparison of Grid-to-Vehicle Probabilistic Approaches

Zuluaga

Ceballos

Yepes

et al. 2021

Tecnura

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Objective: In this paper, we review different approaches to how the penetration of electric vehicles (EV) can be modeled in power networks. We also evaluate and compare experimentally the performance of three probabilistic electric vehicle charging load approaches considering four levels of penetration of EV. Methodology: We carry out a detailed search of the state-of-the-art in charging load modeling strategies for electric vehicles, where the most representative works on this subject were compiled. A probabilistic model based on Monte Carlo Simulation was proposed and two more methods were implemented. These models take into account the departure time of electric vehicles, the arrival time and the plug-in time, which were conceived as random variables. Results: Histograms of the demand for charging of electric vehicles were obtained for the three models contemplated. Additionally, a similarity metric was calculated to know the distribution that best fits the data of each model. The above was done considering 20, 200, 2000 and 20,000 electric vehicles on average. The results show that if there are a low penetration of electric vehicles, it is possible to model the EV charging demand using a gamma distribution. Otherwise, it is recommended to use a Gaussian or Lognormal distribution if you have a high VE penetration. Conclusions: A review of the state of the art of the modeling of electric vehicles under a G2V approach was presented, where three groups are identified: the deterministic approaches, methods that deal with uncertainty and variability, and finally data driven methods were also identified. Additionally, we observed that the EVCP model 3 and the gamma distribution can be appropriate for modeling the penetration of EVs in probabilistic load flow analysis or for stochastic planning studies for active distribution networks. Financing: Institución Universitaria Pascual Bravo

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“…Recently, the methods for modeling EV load have mainly used bottom-up behavioral techniques such as Markov chain [7,8,13], Markov chain Monte Carlo [14][15][16], machine learning [17,18], and agent-based [19][20][21][22][23][24][25][26][27] (aggregator) simulations. Moreover, some authors have used travel survey data to construct the EV load, using either a Monte Carlo simulation approach [28] or stochastic modeling [29]. The focus of the modeling in the reviewed literature is on the distribution grid (voltage and line loading), hub management (transactions on the distribution side), and grid infrastructure planning (capacity expansion country or microgrid).…”

Section: Introductionmentioning

confidence: 99%

Integrating Electric Vehicles into Power System Operation Production Cost Models

Guerrero

Bhattarai

Shrestha

et al. 2021

WEVJ

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The electrification of the transportation sector will increase the demand for electric power, potentially impacting the peak load and power system operations. A change such as this will be multifaceted. A power system production cost model (PCM) is a useful tool with which to analyze one of these facets, the operation of the power system. A PCM is a computer simulation that mimics power system operation, i.e., unit commitment, economic dispatch, reserves, etc. To understand how electric vehicles (EVs) will affect power system operation, it is necessary to create models that describe how EVs interact with power system operations that are suitable for use in a PCM. In this work, EV charging data from the EV Project, reported by the Idaho National Laboratory, were used to create scalable, statistical models of EV charging load profiles suitable for incorporation into a PCM. Models of EV loads were created for uncoordinated and coordinated charging. Uncoordinated charging load represents the load resulting from EV owners that charge at times of their choosing. To create an uncoordinated charging load profile, the parameters of importance are the number of vehicles, charger type, battery capacity, availability for charging, and battery beginning and ending states of charge. Coordinated charging is where EVs are charged via an “aggregator” that interacts with a power system operator to schedule EV charging at times that either minimize system operating costs, decrease EV charging costs, or both, while meeting the daily EV charging requirements subject to the EV owners’ charging constraints. Beta distributions were found to be the most appropriate distribution for statistically modeling the initial and final state of charge (SoC) of vehicles in an EV fleet. A Monte Carlo technique was implemented by sampling the charging parameters of importance to create an uncoordinated charging load time series. Coordinated charging was modeled as a controllable load within the PCM to represent the influence of the EV fleet on the system’s electricity price. The charging models were integrated as EV loads in a simple 5-bus system to demonstrate their usefulness. Polaris Systems Optimization’s PCM power system optimizer (PSO) was employed to show the effect of the EVs on one day of operation in the 5-bus power system, yielding interesting and valid results and showing the effectiveness of the charging models.

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Sensitivity analysis of electric vehicle impact on low-voltage distribution grids

Cited by 36 publications

References 9 publications

Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

Review of Charging Load Modeling Strategies for Electric Vehicles: a Comparison of Grid-to-Vehicle Probabilistic Approaches

Integrating Electric Vehicles into Power System Operation Production Cost Models

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