This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
This study presents a stochastic bidding strategy for electrical vehicle charging stations (EVCSs) to participate in frequency containment reserves (FCRs) markets. To achieve this, the study starts by developing deterministic models to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle. These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation and frequency containment reserve for disturbances. These deterministic models will be combined with historical data of charging records in EVCS to develop a methodology to calculate the probability density functions of the FCR profiles. Finally, the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, are estimated by performing a stochastic optimisation. The proposed methodology is evaluated by using empirical charging data of public EVCS in the Helsinki area.
References
-
-
1)
-
5. Wu, D., Zeng, H., Lu, C., et al: ‘Two-stage energy management for office buildings with workplace EV charging and renewable energy’, IEEE Trans. Transp. Electrif., 2017, 3, (1), pp. 225–237.
-
2)
-
16. Schäuble, J., Kaschub, T., Ensslen, A., et al: ‘Generating electric vehicle load profiles from empirical data of three EV fleets in southwest Germany’, J. Clean. Prod., 2017, 150, (1), pp. 253–266.
-
3)
-
24. Hasanpor Divshali, P., Evens, C.: ‘Behaviour analysis of electrical vehicle flexibility based on large-scale charging data’. IEEE PES Powertech 2019, Milan, Italy, 2019.
-
4)
-
18. Wang, S., Xue, G., Ping, C., et al: ‘The application of forecasting algorithms on electric vehicle power load’. 2018 IEEE Int. Conf. on Mechatronics and Automation (ICMA), Changchun, People's Republic of China, 2018, pp. 1371–1375.
-
5)
-
1. Hasanpor Divshali, P., Choi, B.J.: ‘Electrical market management considering power system constraints in smart distribution grids’, Energies, 2016, 9, (6), pp. 1–30.
-
6)
-
13. Korolko, N., Sahinoglu, Z., Nikovski, D.: ‘Modeling and forecasting self-similar power load due to EV fast chargers’, IEEE Trans. Smart Grid, 2016, 7, (3), pp. 1620–1629.
-
7)
-
14. Chung, Y.W., Khaki, B., Chu, C., et al: ‘Electric vehicle user behavior prediction using hybrid kernel density estimator’. 2018 Int. Conf. on Probabilistic Methods Applied to Power Systems, PMAPS 2018 – Proc., Boise, ID, USA, 2018.
-
8)
-
3. Hasanpor Divshali, P., Choi, B.J., Liang, H.: ‘Multi-agent transactive energy management system considering high levels of renewable energy source and electric vehicles’, IET Gener. Transm. Distrib., 2017, 11, (15), pp. 3713–3721.
-
9)
-
20. Darby, S., Forsström, J, Sarah Higginson, E.: ‘REALVALUE d6.3: MARKET DESIGN & BUSINESS MODELS REPORT’, 2017.
-
10)
-
25. FINGRID: ‘Application instruction for the maintenance of frequency controlled reserves’, 2018.
-
11)
-
6. Van Der Meer, D., Mouli, G.R.C., Mouli, G.M.E., et al: ‘Energy management system with PV power forecast to optimally charge EVs at the workplace’, IEEE Trans. Ind. Inf., 2018, 14, (1), pp. 311–320.
-
12)
-
7. Zhou, Y., Li, Z., Wu, X.: ‘The multiobjective based large-scale electric vehicle charging behaviours analysis’, Complexity, 2018, 2018, pp. 1–16.
-
13)
-
14)
-
8. Harris, C.B., Webber, M.E.: ‘An empirically-validated methodology to simulate electricity demand for electric vehicle charging’, Appl. Energy., 2014, 126, (1), pp. 172–181.
-
15)
-
11. Amini, M.H., Karabasoglu, O., Ilić, M.D., et al: ‘ARIMA-based demand forecasting method considering probabilistic model of electric vehicles’ parking lots’. IEEE Power and Energy Society General Meeting, Denver, CO, USA, 2015.
-
16)
-
12. Amini, M.H., Kargarian, A., Karabasoglu, O.: ‘ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation’, Electr. Power Syst. Res., 2016, 140, pp. 378–390.
-
17)
-
22. FINGRID: ‘The technical requirements and the prequalification process of frequency containment reserves (FCR)’, 2019.
-
18)
-
10. Su, S., Zhao, H., Zhang, H., et al: ‘Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning’. 2017 19th Int. Conf. on Intelligent System Application to Power Systems, ISAP 2017, San Antonio, TX, USA, September 2017.
-
19)
-
9. Pflaum, P., Alamir, M., Lamoudi, M.Y.: ‘Probabilistic energy management strategy for EV charging stations using randomized algorithms’, IEEE Trans. Control Syst. Technol., 2018, 26, (3), pp. 1099–1106.
-
20)
-
26. FINGRID: ‘Frequency – historical data – datasets – Fingridin avoin data’. .
-
21)
-
17. Louie, H.M.: ‘Time-series modeling of aggregated electric vehicle charging station load’, Electr. Power Compon. Syst., 2017, 45, (14), pp. 1498–1511.
-
22)
-
23)
-
23. FINGRID: ‘Yearly market agreement for frequency containment reserves’, 2019.
-
24)
-
2. Hasanpor Divshali, P., Choi, B.J., Liang, H., et al: ‘Transactive demand side management programs in smart grids with high penetration of EVs’, Energies, 2017, 10, (10), pp. 1–18.
-
25)
-
4. Islam, M.S., Mithulananthan, N., Hung, D.Q.: ‘A day-ahead forecasting model for probabilistic EV charging loads at business premises’, IEEE Trans. Sustain. Energy, 2018, 9, (2), pp. 741–753.
-
26)
-
21. Merino, J., Gómez, I., Elena Turienzo, E.: ‘Smartnet project D1.1: ancillary service provision by RES and DSM connected at distribution level in the future power system’, 2016.
-
27)
-
15. Xydas, E., Marmaras, C., Cipcigan, L.M., et al: ‘A data-driven approach for characterising the charging demand of electric vehicles: A UK case study’. Appl. Energy, 2016, 162, (1), pp. 763–771.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2019.0906
Related content
content/journals/10.1049/iet-gtd.2019.0906
pub_keyword,iet_inspecKeyword,pub_concept
6
6