{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:40:48Z","timestamp":1773218448846,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T00:00:00Z","timestamp":1738454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T00:00:00Z","timestamp":1738454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"DOI":"10.1186\/s42162-024-00445-w","type":"journal-article","created":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T13:59:11Z","timestamp":1738504751000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["EVLearn: extending the cityLearn framework with electric vehicle simulation"],"prefix":"10.1186","volume":"8","author":[{"given":"Tiago","family":"Fonseca","sequence":"first","affiliation":[]},{"given":"Luis Lino","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Bernardo","family":"Cabral","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Severino","sequence":"additional","affiliation":[]},{"given":"Kingsley","family":"Nweye","sequence":"additional","affiliation":[]},{"given":"Dipanjan","family":"Ghose","sequence":"additional","affiliation":[]},{"given":"Zoltan","family":"Nagy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,2]]},"reference":[{"key":"445_CR1","doi-asserted-by":"publisher","unstructured":"Pagani GA, Aiello M (Jun. 2013) The Power Grid as a complex network: a survey. Physica A 392(11):2688\u20132700. https:\/\/doi.org\/10.1016\/J.PHYSA.2013.01.023","DOI":"10.1016\/J.PHYSA.2013.01.023"},{"key":"445_CR2","doi-asserted-by":"publisher","unstructured":"Zhao X, Hu H, Yuan H, Chu X (Sep. 2023) How does adoption of electric vehicles reduce carbon emissions? Evidence from China. Heliyon 9(9):e20296. https:\/\/doi.org\/10.1016\/J.HELIYON.2023.E20296","DOI":"10.1016\/J.HELIYON.2023.E20296"},{"key":"445_CR3","doi-asserted-by":"publisher","unstructured":"Aydogan H (2024) Electric Vehicles and Renewable Energy. J Phys Conf Ser 2777(1). https:\/\/doi.org\/10.1088\/1742-6596\/2777\/1\/012007","DOI":"10.1088\/1742-6596\/2777\/1\/012007"},{"key":"445_CR4","unstructured":"Alternative Fuels Data Center Emissions from Electric Vehicles. Accessed: Nov. 05, 2024. [Online]. Available: https:\/\/afdc.energy.gov\/vehicles\/electric-emissions"},{"key":"445_CR5","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1016\/j.joule.2021.03.005","volume":"5","author":"M Victoria","year":"2021","unstructured":"Victoria M et al (2021) Solar photovoltaics is ready to power a sustainable future. Joule 5:1041\u20131056. https:\/\/doi.org\/10.1016\/j.joule.2021.03.005","journal-title":"Joule"},{"key":"445_CR6","unstructured":"Outlook for emissions reductions \u2013 Global EV Outlook 2024 \u2013 Analysis - IEA. Accessed: Nov. 05, 2024. [Online]. Available: https:\/\/www.iea.org\/reports\/global-ev-outlook-2024\/outlook-for-emissions-reductions"},{"key":"445_CR7","unstructured":"Vehicle-Grid Integration Activites Accessed: Nov. 04, 2024. [Online]. Available: https:\/\/www.cpuc.ca.gov\/vgi\/"},{"key":"445_CR8","doi-asserted-by":"publisher","unstructured":"B\u00f3dis K, Kougias I, J\u00e4ger-Waldau A, Taylor N, Szab\u00f3 S (Oct. 2019) A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union. Renew Sustain Energy Rev 114:109309. https:\/\/doi.org\/10.1016\/J.RSER.2019.109309","DOI":"10.1016\/J.RSER.2019.109309"},{"key":"445_CR9","doi-asserted-by":"publisher","unstructured":"Surana K, Jordaan SM (2019) The climate mitigation opportunity behind global power transmission and distribution, Nature Climate Change 2019 9:9, vol. 9, no. 9, pp. 660\u2013665, Aug. https:\/\/doi.org\/10.1038\/s41558-019-0544-3","DOI":"10.1038\/s41558-019-0544-3"},{"key":"445_CR10","doi-asserted-by":"publisher","unstructured":"Sheha M, Mohammadi K, Powell K (Sep. 2020) Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles. Energy Convers Manag 220:113102. https:\/\/doi.org\/10.1016\/J.ENCONMAN.2020.113102","DOI":"10.1016\/J.ENCONMAN.2020.113102"},{"key":"445_CR11","doi-asserted-by":"publisher","unstructured":"Zhong J, Bollen M, R\u00f6nnberg S (2021) Towards a 100% renewable energy electricity generation system in Sweden, Renew Energy, vol. 171, pp. 812\u2013824, Jun. https:\/\/doi.org\/10.1016\/J.RENENE.2021.02.153","DOI":"10.1016\/J.RENENE.2021.02.153"},{"key":"445_CR12","doi-asserted-by":"publisher","unstructured":"Dileep G (Feb. 2020) A survey on smart grid technologies and applications. Renew Energy 146:2589\u20132625. https:\/\/doi.org\/10.1016\/J.RENENE.2019.08.092","DOI":"10.1016\/J.RENENE.2019.08.092"},{"key":"445_CR13","doi-asserted-by":"publisher","unstructured":"Antonopoulos I et al (Sep. 2020) Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review. Renew Sustain Energy Rev 130:109899. https:\/\/doi.org\/10.1016\/J.RSER.2020.109899","DOI":"10.1016\/J.RSER.2020.109899"},{"key":"445_CR14","doi-asserted-by":"publisher","unstructured":"Bergaentzl\u00e9 C, Jensen IG, Skytte K, Olsen OJ (2019) Electricity grid tariffs as a tool for flexible energy systems: A Danish case study, Energy Policy, vol. 126, pp. 12\u201321, Mar. https:\/\/doi.org\/10.1016\/J.ENPOL.2018.11.021","DOI":"10.1016\/J.ENPOL.2018.11.021"},{"key":"445_CR15","doi-asserted-by":"publisher","unstructured":"Fonseca T, Ferreira LL, Landeck J, Klein L, Sousa P, Ahmed F (Nov. 2022) Flexible loads scheduling algorithms for renewable Energy communities. Energies 2022 15(23):8875. https:\/\/doi.org\/10.3390\/EN15238875","DOI":"10.3390\/EN15238875"},{"key":"445_CR16","doi-asserted-by":"publisher","unstructured":"Fonseca T, Ferreira LL, Klein L, Landeck J, Sousa P Flexigy Smart-grid Archit, https:\/\/doi.org\/10.5220\/0010918400003118","DOI":"10.5220\/0010918400003118"},{"key":"445_CR17","doi-asserted-by":"publisher","unstructured":"Ravi SS, Aziz M (2022) Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives, Energies 2022, Vol. 15, Page 589, vol. 15, no. 2, p. 589, Jan. https:\/\/doi.org\/10.3390\/EN15020589","DOI":"10.3390\/EN15020589"},{"key":"445_CR18","doi-asserted-by":"publisher","unstructured":"Ghatikar G, Alam MS (2023) Technology and economics of electric vehicle power transfer: insights for the automotive industry, Energy Informatics, vol. 6, no. 1, pp. 1\u201320, Dec. https:\/\/doi.org\/10.1186\/S42162-023-00300-4\/TABLES\/4","DOI":"10.1186\/S42162-023-00300-4\/TABLES\/4"},{"key":"445_CR19","doi-asserted-by":"publisher","unstructured":"Hoekstra A (Jun. 2019) The underestimated potential of Battery Electric Vehicles to reduce emissions. Joule 3(6):1412\u20131414. https:\/\/doi.org\/10.1016\/J.JOULE.2019.06.002","DOI":"10.1016\/J.JOULE.2019.06.002"},{"key":"445_CR20","doi-asserted-by":"publisher","DOI":"10.7799\/1363870","author":"M Muratori","year":"2017","unstructured":"Muratori M (2017) Impact of uncoordinated plug-in electric vehicle charging on residential power demand - supplementary data. Jun. https:\/\/doi.org\/10.7799\/1363870","journal-title":"Jun"},{"key":"445_CR21","doi-asserted-by":"publisher","unstructured":"Skouras TA, Gkonis PK, Ilias CN, Trakadas PT, Tsampasis EG, Zahariadis TV (2019) Electrical Vehicles: Current State of the Art, Future Challenges, and Perspectives, Clean Technologies 2020, Vol. 2, Pages 1\u201316, vol. 2, no. 1, pp. 1\u201316, Dec. https:\/\/doi.org\/10.3390\/CLEANTECHNOL2010001","DOI":"10.3390\/CLEANTECHNOL2010001"},{"key":"445_CR22","doi-asserted-by":"crossref","unstructured":"Gonzalez Venegas F, Petit M, Perez Y (2021) Active integration of electric vehicles into distribution grids. barriers and frameworks for flexibility services","DOI":"10.1016\/j.rser.2021.111060"},{"key":"445_CR23","doi-asserted-by":"publisher","unstructured":"Qiu D, Wang Y, Hua W, Strbac G (Mar. 2023) Reinforcement learning for electric vehicle applications in power systems:a critical review. Renew Sustain Energy Rev 173:113052. https:\/\/doi.org\/10.1016\/J.RSER.2022.113052","DOI":"10.1016\/J.RSER.2022.113052"},{"key":"445_CR24","doi-asserted-by":"publisher","unstructured":"Nweye K, Liu B, Stone P, Nagy Z (Nov. 2022) Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings. Energy AI 10:100202. https:\/\/doi.org\/10.1016\/J.EGYAI.2022.100202","DOI":"10.1016\/J.EGYAI.2022.100202"},{"key":"445_CR25","doi-asserted-by":"publisher","unstructured":"V\u00e1zquez-Canteli JR, Nagy Z (Feb. 2019) Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl Energy 235:1072\u20131089. https:\/\/doi.org\/10.1016\/J.APENERGY.2018.11.002","DOI":"10.1016\/J.APENERGY.2018.11.002"},{"key":"445_CR26","unstructured":"Vazquez-Canteli JR, Dey S, Henze G, Nagy Z (2020) CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management, Dec. Accessed: Jan. 31, 2024. [Online]. Available: https:\/\/arxiv.org\/abs\/2012.10504v1"},{"key":"445_CR27","doi-asserted-by":"publisher","unstructured":"V\u00e1zquez-Canteli JR, K\u00e4mpf J, Henze G, Nagy Z (2019) CityLearn v1.0: An OpenAI gym environment for demand response with deep reinforcement learning, BuildSys - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 356\u2013357, Nov. 2019, https:\/\/doi.org\/10.1145\/3360322.3360998","DOI":"10.1145\/3360322.3360998"},{"key":"445_CR28","doi-asserted-by":"publisher","unstructured":"Nweye K et al (Nov. 2024) CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities. J Build Perform Simul 1\u201322. https:\/\/doi.org\/10.1080\/19401493.2024.2418813","DOI":"10.1080\/19401493.2024.2418813"},{"key":"445_CR29","doi-asserted-by":"publisher","unstructured":"Scharnhorst P et al (2021) Energym: A Building Model Library for Controller Benchmarking, Applied Sciences Vol. 11, Page 3518, vol. 11, no. 8, p. 3518, Apr. 2021, https:\/\/doi.org\/10.3390\/APP11083518","DOI":"10.3390\/APP11083518"},{"key":"445_CR30","doi-asserted-by":"publisher","unstructured":"Blum D et al (2021) Sep., Building optimization testing framework (BOPTEST) for simulation-based benchmarking of control strategies in buildings, J Build Perform Simul, vol. 14, no. 5, pp. 586\u2013610, https:\/\/doi.org\/10.1080\/19401493.2021.1986574","DOI":"10.1080\/19401493.2021.1986574"},{"key":"445_CR31","doi-asserted-by":"publisher","unstructured":"Crawley DB et al (Apr. 2001) EnergyPlus: creating a new-generation building energy simulation program. Energy Build 33(4):319\u2013331. https:\/\/doi.org\/10.1016\/S0378-7788(00)00114-6","DOI":"10.1016\/S0378-7788(00)00114-6"},{"key":"445_CR32","unstructured":"Fritzson PA (2004) Principles of object-oriented modeling and simulation with Modelica 2.1, Wiley-IEEE Press, p. 897"},{"key":"445_CR33","doi-asserted-by":"publisher","unstructured":"Blair N et al (2014) System Advisor Model, SAM 2014.1.14: General Description, NREL Report No. TP-6A20-61019, National Renewable Energy Laboratory, Golden, CO, no. February, p. 13, Feb. https:\/\/doi.org\/10.2172\/1126294","DOI":"10.2172\/1126294"},{"key":"445_CR34","unstructured":"Buyya R, Murshed M GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing"},{"key":"445_CR35","doi-asserted-by":"publisher","unstructured":"Thurner L et al (2017) Sep., pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems, IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 6510\u20136521, https:\/\/doi.org\/10.1109\/TPWRS.2018.2829021","DOI":"10.1109\/TPWRS.2018.2829021"},{"key":"445_CR36","doi-asserted-by":"publisher","unstructured":"Restrepo M, Morris J, Kazerani M, Canizares CA (Jan. 2018) Modeling and testing of a bidirectional smart charger for distribution System EV Integration. IEEE Trans Smart Grid 9(1):152\u2013162. https:\/\/doi.org\/10.1109\/TSG.2016.2547178","DOI":"10.1109\/TSG.2016.2547178"},{"key":"445_CR37","doi-asserted-by":"publisher","unstructured":"Metais MO, Jouini O, Perez Y, Berrada J, Suomalainen E (Jan. 2022) Too much or not enough? Planning electric vehicle charging infrastructure: a review of modeling options. Renew Sustain Energy Rev 153:111719. https:\/\/doi.org\/10.1016\/j.rser.2021.111719","DOI":"10.1016\/j.rser.2021.111719"},{"key":"445_CR38","doi-asserted-by":"publisher","unstructured":"Qian K, Zhou C, Allan M, Yuan Y (May 2011) Modeling of load demand due to EV battery charging in distribution systems. IEEE Trans Power Syst 26(2):802\u2013810. https:\/\/doi.org\/10.1109\/TPWRS.2010.2057456","DOI":"10.1109\/TPWRS.2010.2057456"},{"key":"445_CR39","doi-asserted-by":"publisher","unstructured":"Zhang C, Li K, McLoone S, Yang Z (2014) Battery modelling methods for electric vehicles - A review, 2014 European Control Conference, ECC pp. 2673\u20132678, Jul. 2014, https:\/\/doi.org\/10.1109\/ECC.2014.6862541","DOI":"10.1109\/ECC.2014.6862541"},{"key":"445_CR40","doi-asserted-by":"publisher","unstructured":"Rotas R, Iliadis P, Nikolopoulos N, Rakopoulos D, Tomboulides A (May 2024) Dynamic battery modeling for Electric Vehicle Applications. Batteries 2024 10(6):188. https:\/\/doi.org\/10.3390\/BATTERIES10060188","DOI":"10.3390\/BATTERIES10060188"},{"key":"445_CR41","doi-asserted-by":"publisher","unstructured":"Benabdelaziz K, Maaroufi M (2016) Battery dynamic energy model for use in electric vehicle simulation, Proceedings of International Renewable and Sustainable Energy Conference, IRSEC 2016, pp. 927\u2013932, Jul. 2017, https:\/\/doi.org\/10.1109\/IRSEC.2016.7983906","DOI":"10.1109\/IRSEC.2016.7983906"},{"key":"445_CR42","doi-asserted-by":"publisher","unstructured":"Pedersen TB, Siksnys L, Neupane B (2018) Modeling and Managing Energy Flexibility Using FlexOffers, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018, Dec. 2018, https:\/\/doi.org\/10.1109\/SMARTGRIDCOMM.2018.8587605","DOI":"10.1109\/SMARTGRIDCOMM.2018.8587605"},{"key":"445_CR43","doi-asserted-by":"publisher","unstructured":"Calearo L, Marinelli M, Ziras C (Nov. 2021) A review of data sources for electric vehicle integration studies. Renew Sustain Energy Rev 151:111518. https:\/\/doi.org\/10.1016\/J.RSER.2021.111518","DOI":"10.1016\/J.RSER.2021.111518"},{"key":"445_CR44","doi-asserted-by":"publisher","unstructured":"Amara-Ouali Y, Goude Y, Massart P, Poggi JM, Yan H (Apr. 2021) A review of Electric Vehicle load Open Data and models. Energies 2021 14(8):2233. https:\/\/doi.org\/10.3390\/EN14082233","DOI":"10.3390\/EN14082233"},{"key":"445_CR45","unstructured":"Cabral B, Fonseca T, Sousa C, Ferreira LL (2024) FlexiGen: Stochastic Dataset Generator for Electric Vehicle Charging Energy Flexibility, Nov. Accessed: Nov. 12, 2024. [Online]. Available: https:\/\/arxiv.org\/abs\/2411.07040v1"},{"key":"445_CR46","unstructured":"Brockman G et al (2016) OpenAI Gym, Jun. Accessed: Apr. 02, 2024. [Online]. Available: https:\/\/arxiv.org\/abs\/1606.01540v1"},{"key":"445_CR47","doi-asserted-by":"publisher","unstructured":"Nagy Z, V\u00e1zquez-Canteli JR, Dey S, Henze G (2021) The citylearn challenge 2021, in Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New York, NY, USA: ACM, Nov. pp. 218\u2013219. https:\/\/doi.org\/10.1145\/3486611.3492226","DOI":"10.1145\/3486611.3492226"},{"key":"445_CR48","unstructured":"Fonseca TCC, Multi-Agent A (2026) Reinforcement Learning Approach to Integrate Flexible Assets into Energy Communities, Oct. Accessed: Mar. 27, 2024. [Online]. Available: https:\/\/recipp.ipp.pt\/handle\/10400.22\/24068"},{"key":"445_CR49","doi-asserted-by":"publisher","unstructured":"Vazquez-Canteli JR, Henze G, Nagy Z (2020) MARLISA: Multi-Agent Reinforcement Learning with Iterative Sequential Action Selection for Load Shaping of Grid-Interactive Connected Buildings, BuildSys - Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 170\u2013179, Nov. 2020, https:\/\/doi.org\/10.1145\/3408308.3427604","DOI":"10.1145\/3408308.3427604"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00445-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42162-024-00445-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00445-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T13:59:14Z","timestamp":1738504754000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-024-00445-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,2]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["445"],"URL":"https:\/\/doi.org\/10.1186\/s42162-024-00445-w","relation":{},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,2]]},"assertion":[{"value":"23 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16"}}