Data From: Power Distribution System Tools for Analyzing Impacts of Projected Electric Vehicle Load Growth Using GridLab-D
Portland State University. Department of Electrical and Computer Engineering
Electric power distribution, Electric power consumption, Electric vehicles -- Energy consumption, Smart power grids
These files support research on the development of electric vehicle (EV) load growth modeling and analysis tools for distribution systems. These tools help researchers and distribution engineers better understand the impacts EV growth will have on distribution systems. Such studies can help a utility take appropriate action to enhance grid stability and reliability. Three analysis tools were developed for evaluating impacts of EVs on grid infrastructure. These tools were developed for use in the GridLAB-D modeling environment and were written using Python 3.8.
Three analysis tools were developed. The first tool notifies a user of voltage violations. The second tool identifies conductor overloads. The third alerts the user of transformer overloads. These tools have been evaluated using the IEEE 13 node test feeder coupled with typical household load profiles within GridLAB-D. Using these tools, users can evaluate the impacts EV loads have on distribution systems, specifically transformer overloading, voltage violations, and the overload of conductors. These tools can help utility distribution planners prepare appropriate responses for anticipated EV load growth.
The dataset supports a Master of Science (M.S.) in Electrical and Computer Engineering thesis, Power Distribution System Tools for Analyzing Impacts of Projected Electric Vehicle Load Growth using GridLab-D.
The thesis associated with this dataset may be accessed at https://archives.pdx.edu/ds/psu/35664.
Thesis Advisor: Robert Bass, Ph.D
This work is marked with CC0 1.0 Universal
Alomani, Shahad, "Data From: Power Distribution System Tools for Analyzing Impacts of Projected Electric Vehicle Load Growth using GridLab-D" (2021). Electrical and Computer Engineering Datasets. 1. https://doi.org/10.15760/ece-data.01
Summer - Households.zip (731 kB)
Winter - Households and EVs.zip (1055 kB)
Winter - Households.zip (1036 kB)
README.txt (1 kB)
Base_Case_Summer_Final_Version.glm (560 kB)
Base_Case_Wintter_Final_Version.glm (560 kB)
Loading_Summer_output_20.glm (569 kB)
Loading_Summer_output_40.glm (569 kB)
Loading_Summer_output_60.glm (569 kB)
Loading_Summer_output_80.glm (569 kB)
Loading_Summer_output_100.glm (559 kB)
Loading_Winter_output_20.glm (571 kB)
Loading_Winter_output_40.glm (571 kB)
Loading_Winter_output_60.glm (569 kB)
Loading_Winter_output_80.glm (571 kB)
Loading_Winter_output_100.glm (559 kB)
Current_Violation.py (1 kB)
Transformer_Overloading.py (5 kB)
Voltage_Violation.py (2 kB)
The archive contains three file types. Model configurations are found within .glm files. Data analysis scripts are written in Python, .py files. Input data are archived as .csv files. These .csv files are contained within .zip files.
Input Data - Load Profiles