Abstract
EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.
- Developers:
-
Jackson, Derek [1] ; Ucer, Emin [1] ; Kisacikoglu, John [1] ; Meintz, Andrew [1]
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Release Date:
- 2025-01-17
- Project Type:
- Closed Source
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)Primary Award/Contract Number:AC36-08GO28308
- Code ID:
- 150100
- Site Accession Number:
- NREL SWR-25-07
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Jackson, Derek, Ucer, Emin, Kisacikoglu, John, and Meintz, Andrew.
EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07].
Computer Software.
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO).
17 Jan. 2025.
Web.
doi:10.11578/dc.20250923.6.
Jackson, Derek, Ucer, Emin, Kisacikoglu, John, & Meintz, Andrew.
(2025, January 17).
EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07].
[Computer software].
https://doi.org/10.11578/dc.20250923.6.
Jackson, Derek, Ucer, Emin, Kisacikoglu, John, and Meintz, Andrew.
"EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]." Computer software.
January 17, 2025.
https://doi.org/10.11578/dc.20250923.6.
@misc{
doecode_150100,
title = {EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]},
author = {Jackson, Derek and Ucer, Emin and Kisacikoglu, John and Meintz, Andrew},
abstractNote = {EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.},
doi = {10.11578/dc.20250923.6},
url = {https://doi.org/10.11578/dc.20250923.6},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250923.6}},
year = {2025},
month = {jan}
}