TY - DATA
T1 - Agent-based model of short-term and long-term allocation of electric vehicle charging resources in Netlogo
PY - 2024/07/22
AU - Mylene van der Koogh
AU - Emile J.L. Chappin
AU - Zofia Lukszo
AU - Renee Heller
UR - 
DO - 10.4121/50c0329b-e9e2-4b52-ba4d-51657311f1b6.v1
KW - agent-based model
KW - resource allocation
KW - charging infrastructure
N2 - <p>The REVCID (Residential Electric Vehicle Charging Infrastructure Development) model is an agent-based model, built in NetLogo 6.4.0. It’s goal is to identify strengths and weaknesses of various roll-out strategies, taking into account residential demand, growth projections, equity and grid limitations.</p><p>Parameters from a real case study were used to initialize the model (identify-neighbourhoods.nls)&nbsp;&nbsp;and parameters should be adjusted to the area of interest when using the model. The netlogo procedures can be found in different files:</p><p><br></p><ul><li>globals.nls contains the global variables (parameters) used in the model</li><li>chargepoints-own.nls, transformators-own.nls and admins-own.nls is a list of the parameters within the chargepoint, transformer and policy-maker agents.</li><li>identify-neighborhoods.nls contains the statistics as derived from external data (such as EVdata and CBS, see references) for each of the 9 selected case study neighborhoods</li><li>set-parameters-grid.nls sets the charging speed of various charging modes</li><li>determine-peak.nls adjusts the occupancy rates based on whether the hour of the day is a peak hour, and adds a random chance for higher occupancy</li><li>grow-demand.nls sets the growth factor of the occupancy rate</li><li>set-values-for-bs.nls turns the output into reporters that can be saved as csv or table output when running the simulations in Behaviorspace</li><li>The nlogo file contains the entire model, interface and procedures. The nls files should be imported for the model to work.</li></ul><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p><p><br></p>
ER -