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

Abstract

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

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

“…Statistical methods for EV load modeling are used to understand and predict how EV load demand varies as a function of the factors described in Section 3. These methods leverage statistical techniques, such as probabilistic distributions and stochastic processes, to analyze historical data and generate demand models that can aid in EV charging infrastructure planning and grid management [19,24].…”

“…Stochastic methods are based on stochastic process theory, which means that they consider the randomness and variability of events over time [19]. These methods may include event simulation and modeling of evolving systems with random components.…”

“…However, the authors in [5] combine artificial neural networks, recurrent neural networks, and short-and long-term memories to model EV loads, using a dataset from a total of 20,562 random transactions at a charging station. Accordingly, each method requires varied initial data, where different factors can be considered and classified according to criteria, such as the temporal/spatial dimension or the nature of the input data, as discussed in [15,19]. In this overview, data influencing the modeling of EV load patterns are addressed.…”

Assessing the Impacts of Electric Vehicle Penetration in Curaçao’s Power Network

“…There are several approaches for modeling the penetration levels of EVs in power distribution networks. These modeling methodologies can be categorized into three main groups: deterministic approaches, data-driven approaches, and uncertainty-based approaches [15]. Regarding the first group, the authors in [16] use measurement-based load modeling approaches to estimate the load model for EV fast-charging stations.…”

Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization

“…Electrical distribution networks are the largest part of an electrical system and are responsible for providing electricity to all the end users at medium-and low-voltage levels (Rupolo et al, 2019, Rupolo et al, 2020, Hernández et al, 2021. They usually have a radial configuration, with only one central power source, i.e., a distribution substation (Devabalaji et al, 2015, Zuluaga-Ríos et al, 2021.…”