Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption
This repository accompanies the paper:
S. Powell, G. V. Cezar, L. Min, I. Azevedo and R. Rajagopal, "Charging Infrastructure Access and Operation to Reduce the Grid Impacts of Deep Electric Vehicle Adoption".
This code is also available at: https://github.com/Stanford-Sustainable-Systems-Lab.
Contact:
- siobhan (dot) powell (at) stanford (dot) edu
- ramr (at) stanford (dot) edu
- iazevedo (at) stanford (dot) edu
In this paper we study the generation-level grid impacts of deep electric vehicle adoption in WECC, the Western US, comparing the effects of charging infrastructure access and conventional charging control.
Please cite this code:
Siobhan Powell, Gustavo Vianna Cezar, Liang Min, Ines Azevedo & Ram Rajagopal. (2021). SiobhanPowell/speech-grid-impact: (v1.0.0). Zenodo. doi: 10.5281/zenodo.5789549
The code is divided into two folders, one containing all the electric vehicle (EV) charging model and one containing the grid model. The EV charging model was run first to generate a range of charging demand scenarios. Then, the grid model was used to quantify the grid impacts for each case.
Each subfolder has an extended README.md describing its contents and use.
We have posted the model objects and charging profiles publicly to accompany the paper. This repository contains instructions and sample code to show how you could use them to run other scenarios. Please contact the authors with any questions.
Data: https://data.mendeley.com/datasets/y872vhtfrc/2.
Please cite this data set:
Powell, Siobhan; Cezar, Gustavo Vianna; Min, Liang; Azevedo, Ines; Rajagopal, Ram (2021), “SPEECh Model for Study on Grid Impacts of Charging Infrastructure Access”, Mendeley Data, V1, doi: 10.17632/y872vhtfrc.2
The raw input data used for this analysis cannot be shared publicly.
This project was supported by EV50 at Stanford, University. More information about the program is available here: https://energy.stanford.edu/bitsandwatts/research/ev-flagship-project.
The authors would like to thank Noel Crisostomo and Matt Alexander with the California Energy Commission for their continuing support, insights, and guidance. The authors would like to thank their many colleagues at Stanford, SLAC, and beyond for their most helpful feedback, discussion, and support. The authors would like to thank ChargePoint for providing data to support this research.
This work was funded by the California Energy Commission under grant EPC-17-020, by the National Science Foundation through a CAREER award (#1554178), and through funding from the Stanford Bits and Watts EV50 Initiative with support from Volkswagen. SLAC National Accelerator Laboratory is operated for the US Department of Energy by Stanford University under Contract DE-AC02-76SF00515.