{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T23:50:02Z","timestamp":1776815402975,"version":"3.51.2"},"reference-count":6,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Boysen TU Dresden","award":["1"],"award-info":[{"award-number":["1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>This dataset includes multiple files related to optimization of electric vehicles to minimize overloading in low voltage grids by varying the locations available to charge the EVs. The data include lognormally sampled hourly sorted scenarios across 11 charging locations for a stochastics-based Monte Carlo simulation. This simulation runs through 2 million scenarios based on actual probabilities to incorporate most possible situations. It also includes samples from normally distributed household electricity use scenarios based on agent-based modeling. The article includes the test grid parameters for simulation, which were used to create a benchmark grid in DigSilent Powerfactory software, as well as intermediate outputs defining worst case scenarios when electric vehicles were charged and results from three different optimization approaches involving a reduction in voltage drops, cable overloading and total line losses. The outputs from the benchmark grid were used to train a machine learning algorithm, the weights and codes for which are also attached. This trained network acted as the grid for subsequent iterative optimization procedures. Outputs are presented as a comparison between pre-optimization and post-optimization scenarios. The above dataset and procedure were repeated while varying the number of EVs between 0 and 100 in increments of 20, data for which are also attached. The data article supports a related submission titled \u201cMinimization of Overloading Caused by Electric Vehicle (EV) Charging in Low Voltage Networks\u201d.<\/jats:p>","DOI":"10.3390\/data5040102","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T09:44:53Z","timestamp":1603964693000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Data for Heuristic Optimization of Electric Vehicles\u2019 Charging Configuration Based on Loading Parameters"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9326-1271","authenticated-orcid":false,"given":"Sajjad","family":"Haider","sequence":"first","affiliation":[{"name":"Boysen-TU Dresden-Research Training Group, Electrical Power Supply, Technical University of Dresden, 01069 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Schegner","sequence":"additional","affiliation":[{"name":"Electrical Power Supply, Technical University of Dresden, 01069 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pflugradt, N., and Muntwyler, U. (2020, September 11). Using Behavior Simulation to Synthesize Electromobility Charging Profiles. Available online: http:\/\/proceedings.ises.org\/paper\/eurosun2018\/eurosun2018-0161-Pflugradt.pdf.","DOI":"10.18086\/eurosun2018.11.09"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.egypro.2017.07.365","article-title":"Synthesizing residential load profiles using behavior simulation","volume":"122","author":"Pflugradt","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_3","unstructured":"(2020, October 29). Matlab Documentation. Available online: https:\/\/www.mathworks.com\/help\/matlab\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.jclepro.2017.02.150","article-title":"Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany","volume":"150","author":"Kaschub","year":"2017","journal-title":"J. Clean. Prod."},{"key":"ref_5","unstructured":"Ensslen, A., Jochem, J., Sch\u00e4uble, J., Babrowski, S., and Fichtner, W. (2013, January 15\u201318). User Acceptance of Electric Vehicles in the French-German Transnational Context: Results out of the french-german fleet test cross-border mobility for electric vehicles (CROME). Proceedings of the 13th WCTR, Rio de Jeneiro, Brazil."},{"key":"ref_6","unstructured":"Strunz, K., Abbasi, E., Abbey, C., Andrieu, C., Gao, F., Gaunt, T., and Iravani, R. (2009). Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources, CIGRE."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/4\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:30:27Z","timestamp":1760178627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/4\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,29]]},"references-count":6,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["data5040102"],"URL":"https:\/\/doi.org\/10.3390\/data5040102","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,29]]}}}