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Electric vehicles’ classification for the participation of retailers in Day-Ahead energy and reserve markets taking into account different uncertainties simultaneously
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Ramin Dehghani1    , Asghar Akbari Foroud * 1 |
| 1- Semnan University |
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Abstract: (1279 Views) |
Following deregulation in the electricity grids, power systems has faced new challenges in terms of diversification of generation units and demands types, which requires a more comprehensive management framework. For this purpose, several new players were introduced to resolve the challenges between generation and demand side. Among others, retailers are the one that play a crucial role by creating a link between electricity market operators and the consumers, seeking maximize profits and reduction of the costs of their customers. Electric vehicles (EVs), meanwhile, are among the bilateral consumers which retailers are able to both provide energy for as well as see as an energy sources for sales in Day-Ahead (DA) energy and reserve markets. Nevertheless, Retailers face several uncertainties regarding the physical characteristics of electric vehicles, the behavior of their owners, in addition to the uncertainties inherent in energy and reserve markets faced by any player. In order to optimal participation of retailers in those markets as well as to meeting the needs of electric vehicles, a two-stage optimization framework is presented in this paper. Vehicle clustering is also utilized to model all uncertainties simultaneously.
Thus the main contributions of this paper can be summarized as follows:
- A new method for classifying electric vehicles based on battery characteristics (such as battery capacity, charge and discharge rate, etc.) and owners' behaviors (availability at parking stations, arrival and departure times, initial charge state, etc.) is proposed. This clustering helps reduce the computational load by avoiding duplicate calculations.
- A novel model is presented for retailers to the participate in the reserve market using the capabilities of electric vehicles. Therefore, in this paper, retailers participate in the energy and reserve markets simultaneously using the potential of electric vehicles.
- A two-stage stochastic linear model has been introduced to consider most of the uncertainties with respect to the aggregation of the potential of EVs by the retailer to plan their participation in different electricity markets.
- Using the proposed optimization framework and vehicle classification, all uncertainties related to the initial charge of the EVs’ batteries, type and capacity of batteries, the expected final state of charge, the times of arrivals and departures of vehicles to / from parking lots, EVs’ battery charging and discharging rates, EVs’ battery efficiency, reserve market call status, as well as uncertainties related to DA energy and spinning reserve prices, and the number of EVs in parking lots are modeled simultaneously.
Finally, the model has been implemented in GAMS considering the option of the retailer participation as a seller in the energy and spinning reserve markets. It has been shown that if the retailer has the mentioned choice, (s)he can benefit from selling in both markets even if sell energy at a low price to electric vehicles in parking lots. |
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| Keywords: Electric vehicle, retailer, Day-Ahead energy market, Day-Ahead reserve market, two-stage optimization, Electric vehicles’ classification. |
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Full-Text [PDF 1321 kb]
(356 Downloads)
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Type of Study: Research |
Received: 2022/03/4 | Accepted: 2022/05/31 | Published: 2022/11/1
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1. Angenendt, G., Merten, M., Zurmühlen S. and Sauer, DU. "Evaluation of the effects of frequency restoration reserves market participation with photovoltaic battery energy storage systems and power-to-heat coupling." Applied Energy, vol.260, 2020:114186. [ DOI:10.1016/j.apenergy.2019.114186] 2. Bai, Y., Chou, L. and Zhang, W., "Industrial innovation characteristics and spatial differentiation of smart grid technology in China based on patent mining," J. Energy Storage, vol. 43, p. 103289, Nov. 2021, doi: 10.1016/j.est.2021.103289. [ DOI:10.1016/j.est.2021.103289] 3. Chen Y. and Chang, J. M. "Fair demand response with electric vehicles for the cloud based energy management service, "IEEE Transactions on Smart Grid, vol. 9, no. 1, pp. 458-468, Jan. 2018, doi:10.1109/TSG.2016.2609738. [ DOI:10.1109/TSG.2016.2609738] 4. Conejo, A. J., Carrión, M. and Morales, J. M. "Decision making under uncertainty in electricity markets" Springer, vol. 1, 2010. [ DOI:10.1007/978-1-4419-7421-1_1] 5. Khojasteh, M. "Multi-objective energy procurement strategy of electricity retail companies based on normalized normal constraint methodology," Int. J. Electr. Power Energy Syst., vol. 135, p. 107281, Feb. 2022, doi: 10.1016/J.IJEPES.2021.107281. [ DOI:10.1016/j.ijepes.2021.107281] 6. Khojasteh, M. and Jadid, S. "Reliability-constraint energy acquisition strategy for electricity retailers." International Journal of Electrical Power & Energy Systems.Vol. 101, pp. 223-233, 2018, doi: 10.1016/j.ijepes.2018.03.032. [ DOI:10.1016/j.ijepes.2018.03.032] 7. Khalkhali, H. and Hosseinian, SH. "Multi-stage stochastic framework for simultaneous energy management of slow and fast charge electric vehicles in a restructured smart parking lot." International Journal of Electrical Power & Energy Systems, vol.116, 2020:105540. [ DOI:10.1016/j.ijepes.2019.105540] 8. Kaur, K., Singh M. and Kumar, N. "Multi objective optimization for frequency support using electric vehicles: an aggregator-based Hierarchical control mechanism" IEEE Systems Journal, vol. 13, no. 1, pp.771-782, March 2019, doi:10.1109/JSYST.2017.2771948. [ DOI:10.1109/JSYST.2017.2771948] 9. Kiaee, M., Cruden A. and Sarkh, S. "Estimation of cost savings from participation of electric vehicles in vehicle to grid (V2G) schemes," Journal of Modern Power Systems and Clean Energy, vol. 3, pp. 249-258, 2015, doi: 10.1007/s40565-015-0130-2. [ DOI:10.1007/s40565-015-0130-2] 10. Nasouri-Gilvaei M. and Baghramian, A. "A two-stage stochastic framework for an electricity retailer considering demand response and uncertainties using a hybrid clustering technique," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 43, p. 541-558, 2019, doi: 10.1007/s40998-018-0150-9. [ DOI:10.1007/s40998-018-0150-9] 11. Liu, J., Chen, X., Xiang, Y., Huo, D. and Liu, J. "Optimal planning and investment benefit analysis of shared energy storage for electricity retailers," Int. J. Electr. Power Energy Syst., vol. 126, 2021, doi: 10.1016/j.ijepes.2020.106561. [ DOI:10.1016/j.ijepes.2020.106561] 12. Nojavan, S., Nourollahi, R., Pashaei-Didani, H. and Zare, K. "Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange." International Journal of Electrical Power & Energy Systems, Vol. 105, pp. 237-248, 2019, doi: 10.1016/j.ijepes.2018.08.041. [ DOI:10.1016/j.ijepes.2018.08.041] 13. Norouzi, M., Aghaei, J., Pirouzi, S., Niknam, and T. Fotuhi-Firuzabad, M. "Flexibility pricing of integrated unit of electric spring and EVs parking in microgrids," Energy, vol. 239, p. 122080, Jan. 2022, doi: 10.1016/J.ENERGY. 2021.122080. [ DOI:10.1016/j.energy.2021.122080] 14. Nourollahi, R., Tabar, VS., Zadeh, SG. And Akbari-Dibavar, A. "A hybrid optimization approach to analyze the risk-constrained operation of a residential hybrid energy system incorporating responsive loads." Computers and Chemical Engineering, vol.157, 2022:107603. [ DOI:10.1016/j.compchemeng.2021.107603] 15. Osório, GJ., Lotfi, M., Gough, M., Javadi, M. Espassandim, HMD., Shafie-khah, M. et al. "Modeling an electric vehicle parking lot with solar rooftop participating in the reserve market and in ancillary services provision." Journal of Cleaner Production, vol.318, 2021:128503. [ DOI:10.1016/j.jclepro.2021.128503] 16. Pak, O., Galbreth, M. and Ferguson, M. "Retailer strategies to encourage reduced packaging adoption," Journal of Cleaner Production, vol.354, 131318 2022. [ DOI:10.1016/j.jclepro.2022.131318] 17. Pavić, I., Capuder T. and Kuzle, I. "A Comprehensive Approach for Maximizing Flexibility Benefits of Electric Vehicles" IEEE Systems Journal, vol. 12, no. 3, pp.2882-2893, Sept. 2018, doi:10.1109/JSYST.2017.2730234. [ DOI:10.1109/JSYST.2017.2730234] 18. Pérez-Díaz, JI., Guisández, I., Chazarra M. and Helseth, A. "Medium-term scheduling of a hydropower plant participating as a price-maker in the automatic frequency restoration reserve market." Electric Power Systems Research, vol.185, 2020: 106399. [ DOI:10.1016/j.epsr.2020.106399] 19. Shafie-Khah, M., Moghaddam, M. P., Sheikh-El-Eslami, M. K. and Catalão, J. P. S. "Optimised performance of plug-in electric vehicle aggregator in energy and reserve markets" Energy Conversion and Management. Vol. 97, pp. 393-408, 2015, doi: 10.1016/j.enconman.2015.03.074. [ DOI:10.1016/j.enconman.2015.03.074] 20. Sekizaki, S., Ichiro, N. and Tomohiro, H. "Impact of retailer and consumer behavior on voltage in distribution network under liberalization of electricity retail market, "Electrical Engineering in Japan, vol. 194, no. 4, pp. 27-41, 2016, doi:10.1002/eej.22743. [ DOI:10.1002/eej.22743] 21. Wang, Z. H., Qi, L., Zhang, Y. and Liu, Z. "A trade-credit-based incentive mechanism for a risk-averse retailer with private information," Comput. Ind. Eng., vol. 154, 2021, doi: 10.1016/j.cie.2021.107101. [ DOI:10.1016/j.cie.2021.107101] 22. Yang, H., Zhang, S., Qiu, J., Qiu, D., Lai M. and Dong, Z. "CVaR- constrained optimal bidding of electric vehicle aggregators in day-ahead and real-time markets, "IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp.2555-2565, Oct. 2017, doi: 10.1109/TII.2017.2662069. [ DOI:10.1109/TII.2017.2662069] 23. Zeynali, S., Rostami, N., Ahmadian, A. and Elkamel, A. "Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model," Sustain. Cities Soc., vol. 74, p. 103154, Nov. 2021, doi: 10.1016/J.SCS.2021.103154. [ DOI:10.1016/j.scs.2021.103154] 24. https://www.nordpoolgroup.com/Market-data1#/nordic/table. 25. "National Household Travel Survey," 2009 [Online]. Available: http://nhts.ornl.gov. 26. تازیکه آ، ابراهیمی ر و ذکریازاده ع. "خودبرنامهریزی تجمیع کننده خودروهای الکتریکی در بازار انرژی براساس طرح قیمتگذاری TOU." نشریه کیفیت و بهره وری صنعت برق ایران. ۱۴۰۰; ۱۰ (۴) :۴۶-۳۸. 27. حاجی¬آبادی م، قنبری ح و صمدی م. "بررسی تحلیلی و آماری اثر حضور جمع¬کننده خودروهای الکتریکی بر رفتار تصادفی LMP با کمک تجزیه ساختاری قیمت برق." نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران ۱۳۹۹; ۹ (۴) :۱۲-۱. 28. رشیدی¬زاده ه، واحدی¬پور م و نجفی ح. "ارائه یک مدل دوسطحی برای برنامه¬ریزی تجمیع¬گر خودروهای الکتریکی در فضای رقابتی با در نظر گرفتن عدم قطعیت¬ها." نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران ۱۳۹۷; ۷ (۱) :۸۳-۶۸.
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Dehghani R, Akbari Foroud . Electric vehicles’ classification for the participation of retailers in Day-Ahead energy and reserve markets taking into account different uncertainties simultaneously. ieijqp 2022; 11 (4) :48-62 URL: http://ieijqp.ir/article-1-886-en.html
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