{"data":{"id":"10.4121/50c0329b-e9e2-4b52-ba4d-51657311f1b6","type":"dois","attributes":{"doi":"10.4121/50c0329b-e9e2-4b52-ba4d-51657311f1b6","prefix":"10.4121","suffix":"50c0329b-e9e2-4b52-ba4d-51657311f1b6","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"van der Koogh, Mylene","nameType":"Personal","givenName":"Mylene","familyName":"van der Koogh","nameIdentifiers":[{"nameIdentifier":"0000-0001-6057-6800","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Chappin, Emile J.L.","nameType":"Personal","givenName":"Emile J.L.","familyName":"Chappin","nameIdentifiers":[{"nameIdentifier":"0000-0002-8529-4241","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Lukszo, Zofia","nameType":"Personal","givenName":"Zofia","familyName":"Lukszo","nameIdentifiers":[{"nameIdentifier":"0000-0002-6643-3699","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Heller, Renee","givenName":"Renee","familyName":"Heller","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Agent-based model of short-term and long-term allocation of electric vehicle charging resources in Netlogo"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2024,"subjects":[{"subject":"Environmentally Sustainable Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"8898"},{"subject":"Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"88"},{"subject":"agent-based model"},{"subject":"resource allocation"},{"subject":"charging infrastructure"}],"contributors":[{"name":"TU Delft, Faculty Of Technology, Policy And Management, Department Of Engineering, Systems And Services","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"Amsterdam University Of Applied Sciences, Faculty Of Technology, Energy \u0026 Innovation","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2024-07-22","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.trpro.2023.11.017","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://www.cbs.nl/-/media/_pdf/2022/35/toelichting-variabelen-kwb-2022.pdf","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.evdata.nl/data-delen/","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":[],"formats":["Netlogo (.nlogo, .nls)"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution Non Commercial 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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.Parameters from a real case study were used to initialize the model (identify-neighbourhoods.nls)  and parameters should be adjusted to the area of interest when using the model. The netlogo procedures can be found in different files:\u003cbr\u003eglobals.nls contains the global variables (parameters) used in the modelchargepoints-own.nls, transformators-own.nls and admins-own.nls is a list of the parameters within the chargepoint, transformer and policy-maker agents.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 neighborhoodsset-parameters-grid.nls sets the charging speed of various charging modesdetermine-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 occupancygrow-demand.nls sets the growth factor of the occupancy rateset-values-for-bs.nls turns the output into reporters that can be saved as csv or table output when running the simulations in BehaviorspaceThe nlogo file contains the entire model, interface and procedures. The nls files should be imported for the model to work.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"Future Charging","funderName":"NWO 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