Abstract
The penetration of plug-in electric vehicles (PEVs) has increased in the transportation sector in the last few years and it has increased the uncertain load in the power sector. In order to analyze the impact on the power grid and plan infrastructure, modeling of PEV load profiles is required. Determining realistic PEV load profiles is challenging due to the involvement of serval uncertainties and complex interdependencies among different factors and to date, there are no benchmark load profiles of PEVs. In this paper, realistic and ready-to-use load profiles for PEVs are developed by considering vehicle mobility, charging infrastructure, and the market share of PEVs. Firstly, the U.S. National Household Travel Survey (NHTS) data is filtered to remove vehicles with unrealistic, duplicate, and missing data. Secondly, a set of relevant parameters is extracted to estimate different features of PEVs, such as arrival time, departure time, and daily mileage. Then, all the commercially available PEVs are grouped into four clusters using the K-means algorithm. Finally, the per unit (per PEV) load profiles are estimated using the information of the available PEVs in the market, charging levels in the residential sector, and features extracted in the previous step. A large set of scenarios are considered for each PEV cluster in determining the load profiles. The pre-unit profiles estimated in this study are ready-to-use for researchers and planners in the PEV industry and are realistic due to consideration of different relevant factors and a large traveling database of vehicles. The developed per-unit load profiles are used to estimate and analyze the PEV load profiles of the top four countries with the highest penetration percentage of PEVs.
Highlights
This paper developed benchmark plug-in electric vehicles (PEVs) load profiles to save the time of researchers, policymakers, and planners associated with the PEV industry
Per-unit PEV load profiles are estimated in this study considering different realistic factors and underlying uncertainties
The developed profiles are based on different parameters related to PEVs, charging infrastructure, and traveling pattern of vehicle drivers and a huge database (NHTS) has been utilized
Summary
Alyami: Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data year. This further emphasizes that a unified model for PEV load estimation is required. A mechanism for sharing power among PEVs during system outages is proposed in [26], where probabilistic load models of EVs based on historical data are utilized All of these studies discussed in the previous paragraph have used different models, data sets, and complexity levels for estimating EV loads. This paper developed benchmark PEV load profiles to save the time of researchers, policymakers, and planners associated with the PEV industry. The obtained PEV load profiles are ready-to-use and compact for analysis and planning of PEVs across different time horizons for researchers and policymakers
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