{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:08:02Z","timestamp":1725840482541},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"S3","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The transport sector is responsible for 25% of global CO<jats:sub>2<\/jats:sub>emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes in the local energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm to minimize the daily power load profile peak and optimally plan an electric bus depot. The algorithm accepts a bus depot schedule as input, and depending on the user input on optimization conditions, accounts for varying time granularity, preemption of the charging phase, vehicle-to-grid (V2G) charging capabilities and varying fleet size. The primary objective of this work is the analysis of the impact of each of these input conditions on the resulting minimized peak load. The results show that our optimization algorithm can reduce peak load by 83% on average. Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction. The results bear relevance for mobility planners to account for innovative fleet management options. Depot infrastructure costs can be minimized by optimally sizing the infrastructure needs, by relying on split-fleet management or V2G options.<\/jats:p>","DOI":"10.1186\/s42162-021-00174-4","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T16:03:19Z","timestamp":1631548999000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Peak load minimization of an e-bus depot: impacts of user-set conditions in optimization algorithms"],"prefix":"10.1186","volume":"4","author":[{"given":"Enrico","family":"Toniato","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prakhar","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stevan","family":"Marinkovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Verena","family":"Tiefenbeck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"174_CR1","unstructured":"2050 long-term strategy \u2014 Climate Action. https:\/\/ec.europa.eu\/clima\/policies\/strategies\/2050_en. Accessed 04 May 2021."},{"key":"174_CR2","unstructured":"ABB (2020) Hitachi ABB Power Grids. https:\/\/www.hitachiabb-powergrids.com\/ch\/en. Accessed 04 May 2021."},{"issue":"9","key":"174_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-019-0999-x","volume":"1","author":"JD Arango Castellanos","year":"2019","unstructured":"Arango Castellanos, JD, Dhanasekaran Velayutha Rajan H, Rohde A-K, Denhof D, Freitag M (2019) Design and simulation of a control algorithm for peak-load shaving using vehicle to grid technology. SN Appl Sci 1(9):1\u201312.","journal-title":"SN Appl Sci"},{"key":"174_CR4","unstructured":"Buses \u2014 ACEA - European Automobile Manufacturers\u2019 Association. https:\/\/www.acea.be\/automobile-industry\/buses. Accessed 04 May 2021."},{"key":"174_CR5","doi-asserted-by":"crossref","unstructured":"Clairand, J-M, Gonzalez M, Guerra-Ter\u00e1n P, Cede\u00f1o I, Escriv\u00e1 G (2020) The impact of charging electric buses on the power grid, 1\u20135.. IEEE Power & Energy Society General Meeting (PESGM).","DOI":"10.1109\/PESGM41954.2020.9282014"},{"issue":"1","key":"174_CR6","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/TPWRS.2009.2036481","volume":"25","author":"K Clement-Nyns","year":"2010","unstructured":"Clement-Nyns, K, Haesen E, Driesen J (2010) The impact of Charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25(1):371\u2013380.","journal-title":"IEEE Trans Power Syst"},{"key":"174_CR7","doi-asserted-by":"crossref","unstructured":"Clemente, M, Fanti MP, Ukovich W (2014) Smart Management of Electric Vehicles Charging Operations: The Vehicle-to-charging Station Assignment Problem, Vol. 19.","DOI":"10.3182\/20140824-6-ZA-1003.01061"},{"issue":"120","key":"174_CR8","doi-asserted-by":"publisher","first-page":"109618","DOI":"10.1016\/j.rser.2019.109618","volume":"1","author":"HS Das","year":"2020","unstructured":"Das, HS, Rahman MM, Li S, Tan CW (2020) Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renew Sust Energ Rev 1(120):109618.","journal-title":"Renew Sust Energ Rev"},{"issue":"3","key":"174_CR9","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s40565-017-0375-z","volume":"6","author":"N Erdogan","year":"2018","unstructured":"Erdogan, N, Erden F, Kisacikoglu M (2018) A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system. J Modern Power Syst Clean Energy 6(3):555\u2013566.","journal-title":"J Modern Power Syst Clean Energy"},{"key":"174_CR10","unstructured":"EWZ Electricity tariffs \u2014 Private customers \u2014 ewz. https:\/\/www.ewz.ch\/de\/private\/strom\/tarife\/tarifuebersicht.html. Accessed 04 May 2021."},{"issue":"5","key":"174_CR11","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1109\/TSG.2015.2394489","volume":"6","author":"JF Franco","year":"2015","unstructured":"Franco, JF, Rider MJ, Romero R (2015) A Mixed-Integer Linear Programming Model for the Electric Vehicle Charging Coordination Problem in Unbalanced Electrical Distribution Systems. IEEE Trans Smart Grid 6(5):2200\u20132210.","journal-title":"IEEE Trans Smart Grid"},{"issue":"8","key":"174_CR12","doi-asserted-by":"publisher","first-page":"2060","DOI":"10.3390\/en11082060","volume":"11","author":"Y Gao","year":"2018","unstructured":"Gao, Y, Guo S, Ren J, Zhao Z, Ehsan A, Zheng Y (2018) An electric bus power consumption model and optimization of charging scheduling concerning multi-external factors. Energies 11(8):2060.","journal-title":"Energies"},{"key":"174_CR13","unstructured":"Hitachi ABB Power GridsGrid-eMotion TM Fleet Factsheet Charging more with less. https:\/\/library.e.abb.com\/public\/5c0f96dd943e499a9e395c81282a8a09\/Grid_eMotion_Fleet_Factsheet_200713_jb2.pdf?x-sign=uCzk67qvbtXoXoi9ZCDIYnpannpWZTu7QRaKc+nu7O7vn5CTZChQv2EVxhs1oyyU. Accessed 26 April 2021."},{"issue":"4","key":"174_CR14","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1504\/IJEHV.2019.102862","volume":"11","author":"A Houbbadi","year":"2019","unstructured":"Houbbadi, A, Bouton T, Redondo Iglesias E, Pelissier S, Trigui R (2019) A quadratic programming based optimisation to manage electric bus fleet charging. Int J Electr Hybrid Veh 11(4):289.","journal-title":"Int J Electr Hybrid Veh"},{"key":"174_CR15","doi-asserted-by":"crossref","unstructured":"Hu, J, You S, \u00d8stergaard J, Lind M, Wu QW (2011) Optimal charging schedule of an electric vehicle fleet In: 2011 46th International Universities\u2019 Power Engineering Conference (UPEC) VDE, 1\u20136.","DOI":"10.1109\/UPEC.2012.6398568"},{"key":"174_CR16","unstructured":"IEA (2020) Data and statistics, explore energy data by category, indicator, country or region. https:\/\/www.iea.org\/data-and-statistics?country=WORLD&fuel=CO2emissions&indicator= CO2BySector. Accessed 04 May 2021."},{"issue":"9","key":"174_CR17","first-page":"1748","volume":"9","author":"A Jahic","year":"2019","unstructured":"Jahic, A, Eskander M, Schulz D (2019) Charging schedule for load peak minimization on large-scale electric bus depots. Appl Sci (Switzerland) 9(9):1748.","journal-title":"Appl Sci (Switzerland)"},{"key":"174_CR18","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1016\/j.trpro.2019.07.191","volume":"40","author":"M Janovec","year":"2019","unstructured":"Janovec, M, Koh\u00e1ni M (2019) Exact approach to the electric bus fleet scheduling. Transp Res Procedia 40:1380\u20131387.","journal-title":"Transp Res Procedia"},{"key":"174_CR19","doi-asserted-by":"crossref","unstructured":"Korkas, CD, Baldi S, Michailidis P, Kosmatopoulos EB (2017) A cognitive stochastic approximation approach to optimal charging schedule in electric vehicle stations In: 2017 25th Mediterranean Conference on Control and Automation (MED) IEEE, 484\u2013489.","DOI":"10.1109\/MED.2017.7984164"},{"issue":"6","key":"174_CR20","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1109\/JSAC.2012.120704","volume":"30","author":"I Koutsopoulos","year":"2012","unstructured":"Koutsopoulos, I, Tassiulas L (2012) Optimal control policies for power demand scheduling in the smart grid. IEEE J Sel Areas Commun 30(6):1049\u20131060.","journal-title":"IEEE J Sel Areas Commun"},{"key":"174_CR21","doi-asserted-by":"crossref","unstructured":"Mhaisen, N, Fetais N, Massoud A (2020) Real-Time Scheduling for Electric Vehicles Charging\/Discharging Using Reinforcement Learning In: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 1\u20136.. IEEE.","DOI":"10.1109\/ICIoT48696.2020.9089471"},{"issue":"1","key":"174_CR22","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.1016\/j.ifacol.2017.08.1493","volume":"50","author":"SP Nageshrao","year":"2017","unstructured":"Nageshrao, SP, Jacob J, Wilkins S (2017) Charging cost optimization for EV buses using neural network based energy predictor. IFAC-PapersOnLine 50(1):5947\u20135952.","journal-title":"IFAC-PapersOnLine"},{"key":"174_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen, VL, Tran-Quoc T, Bacha S, Nguyen B (2014) Charging strategies to minimize the peak load for an electric vehicle fleet In: IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society IEEE, 3522\u20133528.","DOI":"10.1109\/IECON.2014.7049022"},{"issue":"2","key":"174_CR24","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1109\/61.216865","volume":"8","author":"S Rahman","year":"1993","unstructured":"Rahman, S, Shrestha GB (1993) An investigation into the impact of electric vehicle load on the electric utility distribution system. IEEE Trans Power Deliv 8(2):591\u2013597.","journal-title":"IEEE Trans Power Deliv"},{"key":"174_CR25","doi-asserted-by":"crossref","unstructured":"Ramabhotla, S, Bayne S, Giesselmann M (2016) Operation and Maintenance Cost Optimization in the Grid Connected Mode of Microgrid. 2016 IEEE Green Technol Conf (GreenTech) IEEE:56\u201361.","DOI":"10.1109\/GreenTech.2016.18"},{"key":"174_CR26","doi-asserted-by":"crossref","unstructured":"Ranjan, A, Khargonekar P, Sahni S (2014) Offline preemptive scheduling of power demands to minimize peak power in smart grids. IEEE Symp Comput Commun (ISCC) IEEE:1\u20136.","DOI":"10.1109\/ISCC.2014.6912525"},{"issue":"3","key":"174_CR27","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1109\/TPWRS.2010.2086083","volume":"26","author":"N Rotering","year":"2011","unstructured":"Rotering, N, Ilic M (2011) Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets. IEEE Trans Power Syst 26(3):1021\u20131029.","journal-title":"IEEE Trans Power Syst"},{"key":"174_CR28","doi-asserted-by":"crossref","unstructured":"\u0160kugor, B, Deur J (2014) Dynamic programming-based optimization of electric vehicle fleet charging. 2014 IEEE Int Electr Veh Conf (IEVC) IEEE:1\u20138.","DOI":"10.1109\/IEVC.2014.7056171"},{"key":"174_CR29","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.energy.2015.03.057","volume":"92","author":"B \u0160kugor","year":"2015","unstructured":"\u0160kugor, B, Deur J (2015) Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model. Energy 92:456\u2013465.","journal-title":"Energy"},{"key":"174_CR30","unstructured":"Sundstr\u00f6m, O, Binding C (2010) Optimization Methods to Plan the Charging of Electric Vehicle Fleets. Proc Int Conf Control Commun Power Eng:28\u201329."},{"issue":"2","key":"174_CR31","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1109\/TPWRS.2016.2585202","volume":"32","author":"W Tang","year":"2017","unstructured":"Tang, W, Zhang YJ (2017) A Model Predictive Control Approach for Low-Complexity Electric Vehicle Charging Scheduling: Optimality and Scalability. IEEE Trans Power Syst 32(2):1050\u20131063.","journal-title":"IEEE Trans Power Syst"},{"key":"174_CR32","doi-asserted-by":"crossref","unstructured":"Turker, H, Colak I (2018) Optimal Peak Shaving with Vehicle-to-Grid Capability of Electric Vehicles in Smart Grids In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) IEEE, 1483\u20131488.","DOI":"10.1109\/ICRERA.2018.8566962"},{"key":"174_CR33","unstructured":"Valckx, T, Cator EA, Heres J (2019) Stochastic effects of customers on peak loads in a power distribution grid. Radboud University Nijmegen (Master\u2019s thesis)."},{"key":"174_CR34","first-page":"45","volume":"2018-Decem","author":"G Wang","year":"2019","unstructured":"Wang, G, Xie X, Zhang F, Liu Y, Zhang D (2019) BCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets. Proc Real-Time Syst Symp 2018-Decem:45\u201355.","journal-title":"Proc Real-Time Syst Symp"},{"issue":"8","key":"174_CR35","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1109\/TNNLS.2016.2526615","volume":"27","author":"S Xie","year":"2016","unstructured":"Xie, S, Zhong W, Xie K, Yu R, Zhang Y (2016) Fair Energy Scheduling for Vehicle-to-Grid. IEEE Trans Neural Netw Learn Syst 27(8):1697\u20131707.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"174_CR36","unstructured":"Xu, J, Wong VWS (2011) An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm) IEEE, 279\u2013284."},{"key":"174_CR37","doi-asserted-by":"publisher","first-page":"101376","DOI":"10.1109\/ACCESS.2019.2929626","volume":"7","author":"S Yang","year":"2019","unstructured":"Yang, S, Zhang S, Ye J (2019) A Novel Online Scheduling Algorithm and Hierarchical Protocol for Large-Scale EV Charging Coordination. IEEE Access 7:101376\u2013101387.","journal-title":"IEEE Access"},{"issue":"4","key":"174_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/a10040122","volume":"10","author":"S Yaw","year":"2017","unstructured":"Yaw, S, Mumey B (2017) Scheduling non-preemptible jobs to minimize peak demand. Algorithms 10(4):1\u201311.","journal-title":"Algorithms"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-021-00174-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42162-021-00174-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-021-00174-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T08:15:46Z","timestamp":1725783346000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-021-00174-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":38,"journal-issue":{"issue":"S3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["174"],"URL":"https:\/\/doi.org\/10.1186\/s42162-021-00174-4","relation":{},"ISSN":["2520-8942"],"issn-type":[{"type":"electronic","value":"2520-8942"}],"subject":[],"published":{"date-parts":[[2021,9]]},"assertion":[{"value":"13 September 2021","order":1,"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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"23"}}