Smart charging; Electric vehicle flexibility; Minimum state of charge; Robust optimisation; Day-ahead market; Intraday market
Abstract :
[en] Simultaneous charging of electric vehicles (EVs) increases peak demand, potentially causing higher electricity prices and increased procurement costs for charging, making EVs less economically appealing. Smart charging addresses this challenge by utilising EVs as flexible assets, adjusting their charging behaviour in response to both power system conditions and user requirements. In our paper, we take the perspective of an energy provider using smart charging algorithms to reduce their electricity procurement costs (EPC) by charging the EVs when the electricity prices are lower. However, EV usage uncertainties introduce variability in the flexibility EVs provide and subsequently impact the energy providers’ EPC when trading in electricity markets. Our paper considers uncertainties arising due to variable driving patterns and charging preferences. Within the charging preferences, we specifically focus on two charging preferences such as a minimum state of charge (SOCmin) requirement – the percentage of the battery up to which EV needs to be charged immediately at full power when connected to the charging point; and the frequency of EV connection to the charging point – how often EV users connect their EV to the charging point. We develop a flexibility model that quantifies the flexibility in terms of energy and power as a function of time. To calculate the energy provider’s EPC, we develop a scenario-based robust optimisation model, minimising the energy provider’s EPC while trading in German day-ahead and intraday markets. As expected, an increase in SOCmin requirements and a decrease in frequency of EV connections results in reduced EV flexibility and subsequently increases the EPC. However, our cost sensitivity analysis reveals that even with an 80 % SOCmin, EPC can be reduced by up to 33.5 % and 36.9 % for the years 2022 and 2023, respectively, compared to fully uncontrolled charging. When EVs offer full flexibility (0 % SOCmin), the cost reduction is only slightly higher, at around 43.6 % and 49.6 % for the years 2022 and 2023, respectively. Flexible EV charging, even with low flexibility, thus possesses high economic value, allowing energy providers to achieve substantial monetary gains with minimal impact on user convenience.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Computer science Management information systems Energy Social & behavioral sciences, psychology: Multidisciplinary, general & others
FNR - Luxembourg National Research Fund Fondation Enovos Creos Luxembourg S.A
Funding number :
13342933/Gilbert Fridgen
Funding text :
The authors gratefully acknowledge the Fondation Enovos under the aegis of the Fondation de Luxembourg in the framework of the philanthropic funding for the research project INDUCTIVE which is the initiator of this applied research. This research was funded in part by the Luxembourg National Research Fund (FNR) and PayPal, through the PEARL grant reference 13342933/Gilbert Fridgen. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission. The authors gratefully acknowledge the financial support of Creos Luxembourg under the research project FlexBeAn
International Energy Agency. Global EV outlook 2023: catching up with climate ambitions. Global EV outlook, 2023, OECD, 10.1787/cbe724e8-en https://www.oecd-ilibrary.org/energy/global-ev-outlook-2023_cbe724e8-en.
Cheung, W.M., A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid. Environ Sci Pollut Res 29 (2022), 77300–77310, 10.1007/s11356-022-21214-w.
Ajanovic, A., Haas, R., Electric vehicles: solution or new problem?. Environ Dev Sustain 20 (2018), 7–22, 10.1007/s10668-018-0190-3.
Fridgen, G., Thimmel, M., Weibelzahl, M., Wolf, L., Smarter charging: power allocation accounting for travel time of electric vehicle drivers. Transp Res D: Transp Environ, 97, 2021, 102916, 10.1016/j.trd.2021.102916.
Haupt, L., Schöpf, M., Wederhake, L., Weibelzahl, M., The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids. Appl Energy, 273, 2020, 115231, 10.1016/j.apenergy.2020.115231.
Pavić, I., Capuder, T., Kuzle, I., Value of flexible electric vehicles in providing spinning reserve services. Appl Energy 157 (2015), 60–74, 10.1016/j.apenergy.2015.07.070.
Raghavan, S.S., Impact of demand response on electric vehicle charging and day ahead market operations. 2016 IEEE power and energy conference at illinois (PECI), 2016, 1–7, 10.1109/PECI.2016.7459218.
Eldeeb, H.H., Faddel, S., Mohammed, O.A., Multi-objective optimization technique for the operation of grid tied PV powered EV charging station. Electr Power Syst Res 164 (2018), 201–211, 10.1016/j.jpgr.2018.08.004.
Daina, N., Sivakumar, A., Polak, J.W., Modelling electric vehicles use: a survey on the methods. Renew Sustain Energy Rev 68 (2017), 447–460, 10.1016/j.rser.2016.10.005.
Iversen, E.B., Morales, J.M., Madsen, H., Optimal charging of an electric vehicle using a Markov decision process. Appl Energy 123 (2014), 1–12, 10.1016/j.apenergy.2014.02.003.
Li, T., Tao, S., He, K., Lu, M., Xie, B., Yang, B., et al. V2G multi-objective dispatching optimization strategy based on user behavior model. Front Energy Res, 9, 2021 https://www.frontiersin.org/articles/10.3389/fenrg.2021.739527.
Al-Awami, A.T., Sortomme, E., Coordinating vehicle-to-grid services with energy trading. IEEE Trans Smart Grid 3 (2012), 453–462, 10.1109/TSG.2011.2167992 http://ieeexplore.ieee.org/document/6075307/.
Pavić, I., Pandžić, H., Capuder, T., Electric vehicle aggregator as an automatic reserves provider under uncertain balancing Energy procurement. IEEE Trans Power Syst 38 (2023), 396–410, 10.1109/TPWRS.2022.3160195.
Kim, J., Oh, H., Robust operation scheme of EV charging facility with uncertain user behavior. IEEE transactions on industrial informatics, 2023, 1–11, 10.1109/TII.2023.3240752.
Delmonte, E., Kinnear, N., Jenkins, B., Skippon, S., What do consumers think of smart charging? Perceptions among actual and potential plug-in electric vehicle adopters in the United Kingdom. Energy Res Soc Sci, 60, 2020, 101318, 10.1016/j.erss.2019.101318.
Libertson, F., Requesting control and flexibility: exploring Swedish user perspectives of electric vehicle smart charging. Energy Res Soc Sci, 92, 2022, 102774, 10.1016/j.erss.2022.102774.
Marxen, H., Ansarin, M., Chemudupaty, R., Fridgen, G., Empirical evaluation of behavioral interventions to enhance flexibility provision in smart charging. Transp Res D: Transp Environ, 123, 2023, 103897, 10.1016/j.trd.2023.103897.
Ensslen, A., Ringler, P., Dörr, L., Jochem, P., Zimmermann, F., Fichtner, W., Incentivizing smart charging: modeling charging tariffs for electric vehicles in German and French electricity markets. Energy Res Soc Sci 42 (2018), 112–126, 10.1016/j.erss.2018.02.013.
Fridgen, G., Häfner, L., König, C., Sachs, T., Providing utility to utilities: the value of information systems enabled flexibility in electricity consumption. J Assoc Inf Syst 17 (2016), 537–563, 10.17705/1jais.00434 http://aisel.aisnet.org/jais/vol17/iss8/1/.
Chemudupaty, R., Ansarin, M., Bahmani, R., Fridgen, G., Marxen, H., Pavić, I., Impact of minimum energy requirement on electric vehicle charging costs on spot markets. 2023 IEEE Belgrade PowerTech, Belgrade, Serbia, 2023, IEEE, 01–06, 10.1109/PowerTech55446.2023.10202936 https://ieeexplore.ieee.org/document/10202936/.
Franke, T., Krems, J.F., Understanding charging behaviour of electric vehicle users. Transp Res F: Traffic Psychol Behav 21 (2013), 75–89, 10.1016/j.trf.2013.09.002.
Kostopoulos, E.D., Spyropoulos, G.C., Kaldellis, J.K., Real-world study for the optimal charging of electric vehicles. Energy Rep 6 (2020), 418–426, 10.1016/j.egyr.2019.12.008.
Gaete-Morales, C., Kramer, H., Schill, W.-P., Zerrahn, A., An open tool for creating battery-electric vehicle time series from empirical data, emobpy. Sci Data, 8, 2021, 152, 10.1038/s41597-021-00932-9 http://www.nature.com/articles/s41597-021-00932-9.
Taiebat, M., Stolper, S., Xu, M., Widespread range suitability and cost competitiveness of electric vehicles for ride-hailing drivers. Appl Energy, 319, 2022, 119246, 10.1016/j.apenergy.2022.119246.
Nourinejad, M., Chow, J.Y.J., Roorda, M.J., Equilibrium scheduling of vehicle-to-grid technology using activity based modelling. Transp Res Part C: Emerging Technol 65 (2016), 79–96, 10.1016/j.trc.2016.02.001.
Müller, M., Biedenbach, F., Reinhard, J., Development of an integrated simulation model for load and mobility profiles of private households. Energies, 13, 2020, 3843, 10.3390/en13153843 https://www.mdpi.com/1996-1073/13/15/3843.
Wulff, N., Miorelli, F., Gils, H.C., Jochem, P., Vehicle energy consumption in Python (VencoPy): presenting and demonstrating an open-source tool to calculate electric vehicle charging flexibility. Energies, 14, 2021, 4349, 10.3390/en14144349 https://www.mdpi.com/1996-1073/14/14/4349.
Kelly, J.C., MacDonald, J.S., Keoleian, G.A., Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics. Appl Energy 94 (2012), 395–405, 10.1016/j.apenergy.2012.02.001.
Gjelaj, M., Arias, N.B., Traeholt, C., Hashemi, S., Multifunctional applications of batteries within fast-charging stations based on EV demand-prediction of the users’ behaviour. J Eng 2019 (2019), 4869–4873, 10.1049/joe.2018.9280 https://onlinelibrary.wiley.com/doi/10.1049/joe.2018.9280.
Ayyadi, S., Maaroufi, M., Optimal framework to maximize the workplace charging station owner profit while compensating electric vehicles users. Math Probl Eng 2020 (2020), 1–12, 10.1155/2020/7086032 https://www.hindawi.com/journals/mpe/2020/7086032/.
Jin, Y., Yu, B., Seo, M., Han, S., Optimal aggregation design for massive V2G participation in Energy market. IEEE Access 8 (2020), 211794–211808, 10.1109/ACCESS.2020.3039507.
Li, X., Wang, Z., Zhang, L., Sun, F., Cui, D., Hecht, C., et al. Electric vehicle behavior modeling and applications in vehicle-grid integration: an overview. Energy, 268, 2023, 126647, 10.1016/j.energy.2023.126647.
Rassaei, F., Soh, W.-S., Chua, K.-C., A statistical modelling and analysis of residential electric vehicles’ charging demand in smart grids. 2015 IEEE power & energy society innovative smart grid technologies conference (ISGT), 2015, IEEE, Washington, DC, USA, 1–5, 10.1109/ISGT.2015.7131894 http://ieeexplore.ieee.org/document/7131894/.
Shepero, M., Munkhammar, J., Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data. Appl Energy 231 (2018), 1089–1099, 10.1016/j.apenergy.2018.09.175.
Sokorai, P., Fleischhacker, A., Lettner, G., Auer, H., Stochastic modeling of the charging behavior of electromobility. World Electr Veh J, 9(3), 2018, 44, 10.3390/wevj9030044 https://www.mdpi.com/2032-6653/9/3/44.
Wang, D., Gao, J., Li, P., Wang, B., Zhang, C., Saxena, S., Modeling of plug-in electric vehicle travel patterns and charging load based on trip chain generation. J Power Sources 359 (2017), 468–479, 10.1016/j.jpowsour.2017.05.036.
Yi, T., Zhang, C., Lin, T., Liu, J., Research on the spatial-temporal distribution of electric vehicle charging load demand: a case study in China. J Clean Prod, 242, 2020, 118457, 10.1016/j.jclepro.2019.118457.
Su, J., Lie, T.T., Zamora, R., Modelling of large-scale electric vehicles charging demand: a New Zealand case study. Electr Power Syst Res 167 (2019), 171–182, 10.1016/j.jpgr.2018.10.030.
Wang, Z., Jochem, P., Fichtner, W., A scenario-based stochastic optimization model for charging scheduling of electric vehicles under uncertainties of vehicle availability and charging demand. J Clean Prod, 254, 2020, 119886, 10.1016/j.jclepro.2019.119886.
Weiller, C., Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States. Energy Policy 39 (2011), 3766–3778, 10.1016/j.enpol.2011.04.005.
Frendo, O., Graf, J., Gaertner, N., Stuckenschmidt, H., Data-driven smart charging for heterogeneous electric vehicle fleets. Energy AI, 1, 2020, 100007, 10.1016/j.egyai.2020.100007.
Funke, S.A., Plotz, P., Wietschel, M., Invest in fast-charging infrastructure or in longer battery ranges? A cost-efficiency comparison for Germany. Appl Energy 235 (2019), 888–899, 10.1016/j.apenergy.2018.10.134.
Fridgen, G., Mette, P., Thimmel, M., The value of information exchange in electric vehicle charging. Thirty fifth international conference on information systems, Auckland 2014, 2014, 16 https://aisel.aisnet.org/icis2014/proceedings/ConferenceTheme/4/.
Okur, O., Heijnen, P., Lukszo, Z., Aggregator's business models in residential and service sectors: a review of operational and financial aspects. Renew Sustain Energy Rev, 139, 2021, 110702, 10.1016/j.rser.2020.110702.
Schücking, M., Jochem, P., Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets. Appl Energy, 293, 2021, 116649, 10.1016/j.apenergy.2021.116649.
Pavić, I., Capuder, T., Kuzle, I., A comprehensive approach for maximizing flexibility benefits of electric vehicles. IEEE Syst J 12 (2018), 2882–2893, 10.1109/JSYST.2017.2730234 https://ieeexplore.ieee.org/abstract/document/8002578.
Ding, Z., Lu, Y., Zhang, L., Lee, W.-J., Chen, D., A stochastic resource-planning scheme for PHEV charging station considering energy portfolio optimization and price-responsive demand. IEEE Trans Ind Appl 54 (2018), 5590–5598, 10.1109/TIA.2018.2851205 https://ieeexplore.ieee.org/abstract/document/8399523.
Xu, Z., Hu, Z., Song, Y., Wang, J., Risk-averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty. IEEE Trans Smart Grid 8 (2017), 96–105, 10.1109/TSG.2015.2477101 https://ieeexplore.ieee.org/abstract/document/7275175.
Astero, P., Evens, C., Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets. Electr Power Syst Res, 190, 2021, 106667, 10.1016/j.jpgr.2020.106667.
Balram, P., Tuan Le, A., Bertling Tjernberg, L., Stochastic programming based model of an electricity retailer considering uncertainty associated with electric vehicle charging. 2013 10th International conference on the European Energy Market (EEM), 2013, 1–8, 10.1109/EEM.2013.6607404 https://ieeexplore.ieee.org/abstract/document/6607404 iSSN: 2165-4093.
Sánchez-Martín, P., Lumbreras, S., Alberdi-Alén, A., Stochastic programming applied to EV charging points for energy and reserve service markets. IEEE Trans Power Syst 31 (2016), 198–205, 10.1109/TPWRS.2015.2405755.
Silva, P., Osorio, G., Gough, M., Santos, S., Home-Ortiz, J., Shafie-Khah, M., et al. Two-stage optimal operation of smart homes participating in competitive electricity markets. 2021 IEEE international conference on environment and electrical engineering and 2021 IEEE industrial and commercial power systems Europe (EEEIC / I&CPS Europe), 2021, IEEE, Bari, Italy, 1–6, 10.1109/EEEIC/ICPSEurope51590.2021.9584775 https://ieeexplore.ieee.org/document/9584775/.
Liu, Z., Wu, Q., Ma, K., Shahidehpour, M., Xue, Y., Huang, S., Two-stage optimal scheduling of electric vehicle charging based on transactive control. IEEE Trans Smart Grid 10 (2019), 2948–2958, 10.1109/TSG.2018.2815593 https://ieeexplore.ieee.org/document/8315146/.
Sun, X.A., Conejo, A.J., Robust optimization in electric energy systems. Volume 313 of international series in operations research & management science, 2021, Springer International Publishing, Cham, 10.1007/978-3-030-85128-6 https://link.springer.com/10.1007/978-3-030-85128-6.
Marasciuolo, F., Orozco, C., Dicorato, M., Borghetti, A., Forte, G., Chance-constrained calculation of the reserve Service provided by EV charging station clusters in Energy communities. IEEE transactions on industry applications, vol. 59, 2023, 4700–4709, 10.1109/TIA.2023.3264965 https://ieeexplore.ieee.org/document/10094006.
Jiao, F., Ji, C., Zou, Y., Zhang, X., Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV. Appl Energy, 304, 2021, 117881, 10.1016/j.apenergy.2021.117881.
Korolko, N., Sahinoglu, Z., Robust optimization of EV charging schedules in unregulated electricity markets. IEEE Trans Smart Grid 8 (2017), 149–157, 10.1109/TSG.2015.2472597 http://ieeexplore.ieee.org/document/7244227/.
Zeng, B., Dong, H., Sioshansi, R., Xu, F., Zeng, M., Bilevel robust optimization of electric vehicle charging stations with distributed energy resources. IEEE transactions on industry applications, vol. 56, 2020, 5836–5847, 10.1109/TIA.2020.2984741 https://ieeexplore.ieee.org/abstract/document/9055167 conference Name: IEEE Transactions on Industry Applications.
Morales, J.M., Conejo, A.J., Madsen, H., Pinson, P., Zugno, M., Clearing the day-ahead market with a high penetration of stochastic production. Integrating renewables in electricity markets, vol. 205, 2014, Springer US, Boston, MA, 57–100, 10.1007/978-1-4614-9411-9_3.
Herberz, M., Hahnel, U.J.J., Brosch, T., Counteracting electric vehicle range concern with a scalable behavioural intervention. Nat Energy 7 (2022), 503–510, 10.1038/s41560-022-01028-3 https://www.nature.com/articles/s41560-022-01028-3.
Werner, J., Risk aversion. Macmillan, P., (eds.) The new palgrave dictionary of economics, 2008, Palgrave Macmillan UK, London, 1–6, 10.1057/978-1-349-95121-5_2741-1.
Lavieri, P.S., Oliveira, G.J.M.D., Electric vehicle charging consumer survey: insights report. Technical report, 2021, Univeristy of Melbourne Researchers, 10.13140/RG.2.2.29120.87043.
Mandev, A., Plötz, P., Sprei, F., Tal, G., Empirical charging behavior of plug-in hybrid electric vehicles. Appl Energy, 321, 2022, 119293, 10.1016/j.apenergy.2022.119293.
Dodson, T., Slater, S., Electric vehicle charging behaviour study: final report for national grid ESO. Technical report, 2019, Element Energy Limited https://hohoho.sustainability.com/contentassets/553cd40a6def42b196e32e4d70e149a1/ev-charging-behaviour-study.pdf.
Wu, J., Hu, J., Ai, X., Zhang, Z., Hu, H., Multi-time scale energy management of electric vehicle model-based prosumers by using virtual battery model. Appl Energy, 251, 2019, 113312, 10.1016/j.apenergy.2019.113312.
Xu, B., Arjmandzadeh, Z., Parametric study on thermal management system for the range of full (Tesla Model S)/ compact-size (Tesla Model 3) electric vehicles. Energy Convers Manag, 278, 2023, 116753, 10.1016/j.enconman.2023.116753.
Triviño, A., González-González, J.M., Aguado, J.A., Wireless power transfer technologies applied to electric vehicles: a review. Energies, 14(6), 2021, 1547, 10.3390/en14061547 https://www.mdpi.com/1996-1073/14/6/1547.
Sears, J., Roberts, D., Glitman, K., A comparison of electric vehicle level 1 and level 2 charging efficiency. 2014 IEEE conference on technologies for sustainability (SusTech), 2014, 255–258, 10.1109/SusTech.2014.7046253 https://ieeexplore.ieee.org/document/7046253.
Fishbein, M., Ajzen, I., Belief, attitude, intention and behaviour: an introduction to theory and research, 27, 1975, Addison-Wesley.
EPEX, Home | EPEX SPOT; 2024. https://www.epexspot.com/en.
ACER. ACER's final assessment of the EU wholesale electricity market design. Technical report, 2022, ACER https://www.acer.europa.eu/Publications/Final_Assessment_EU_Wholesale_Electricity_Market_Design.pdf.