[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 10, Issue 4 (1-2022) ::
ieijqp 2022, 10(4): 38-46 Back to browse issues page
Self-scheduling of electric vehicles aggregator in the energy market based on TOU pricing plan
Atena Tazikeh lemeski , Reza Ebrahimi * 1, Alireza Zakariazadeh
Abstract:   (1338 Views)
In recent years, the issue of air pollution caused by greenhouse gas emissions and rising energy prices have contributed to developing and increasing the number of electric vehicles. Despite the many advantages of these vehicles, their increasing number and consequently their simultaneous charging in the distribution network can have destructive effects such as increased peak load, increased losses, unauthorized voltage drop, etc. On the other hand, managing the charging of vehicles by aggregators and using them as flexible loads and, if there is vehicle-to-grid (V2G) capability, as distributed generation units distributed across the distribution network can bring many financial and technical opportunities for the network. Accordingly, managing and planning the charging and discharging of these vehicles from the view point of network operators, aggregators, or vehicle owners in a centralized and decentralized manner are among the interesting topics that many articles have dealt with so far. This paper presents, a new solution for self-scheduling the charging and discharging of the private aggregator of electric vehicles to increase their profitability in the distribution network. Given the private ownership of the aggregator, it is obvious that the only factor influencing planning is cost reduction or profit enhancement, so its effect is unknown and/or negative on network indicators such as losses and voltage profiles. To solve this problem, a Time of Use (TOU) pricing model has been proposed by the Distribution Network Operator (DSO), so the aggregator plans to charge and discharge vehicles so that it can improve indicators such as losses and voltage profiles of the network in addition to be profitable. Density functions might have been used to include the uncertainty of vehicle drivers' behavior and to model the possible parameters related to him/her. Finally, the proposed approach is applied to a 33-bus test network by a genetic optimization algorithm using a private aggregator. The simulation results show that, in addition to maximizing the aggregator gain, the proposed method smoothes the network load curve, which reduces losses and improves voltage profile. It seems that in the probabilistic environment of vehicle behavior, the combination of TOU in private aggregator planning, which has led to an increase in their profits and at the same time in terms of the use of improved technical indicators, has not been studied yet.
 
Article number: 38
Keywords: Electric Vehicle Aggregator, Charging and Discharging Management, TOU Pricing, Energy Market
Full-Text [PDF 1180 kb]   (210 Downloads)    
Type of Study: Research |
Received: 2021/02/13 | Accepted: 2021/12/5 | Published: 2021/12/2
References
1. [1] D. Liu, L. Wang, W. Wang, H. Li, M. Liu and X. Xu, "Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game," in IEEE Access, vol. 9, pp. 92473-92482, 2021. [DOI:10.1109/ACCESS.2021.3091725]
2. [2] Hashemi, B., Shahabi, M., Teimourzadeh-Baboli, P., "Stochastic-Based Optimal Charging Strategy for Plug-In Electric Vehicles Aggregator Under Incentive and Regulatory Policies of DSO," in IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3234-3245, April 2019. [DOI:10.1109/TVT.2019.2900931]
3. [3] Moradi, M. H., Abedini, M., Hosseinian, M., "Improving operation constraints of microgrid using PHEVs and renewable energy sources,"Renewable Energy,Volume 83,pp. 543-552, 2015. [DOI:10.1016/j.renene.2015.04.064]
4. [4] Mukherjee, J. C., Gupta, A., "Distributed Charge Scheduling of Plug-In Electric Vehicles Using Inter-Aggregator Collaboration," in IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 331-341, Jan. 2017. [DOI:10.1109/TSG.2016.2515849]
5. [5] Fan, H., Duan, C., Zhang, C., Jiang, L., Mao, C., Wang, D., "ADMM-Based Multiperiod Optimal Power Flow Considering Plug-In Electric Vehicles Charging," in IEEE Transactions on Power Systems, vol. 33, no. 4, pp. 3886-3897, July 2018. [DOI:10.1109/TPWRS.2017.2784564]
6. [6] Chung, H., Li, W., Yuen, C., Wen, C., Crespi, N., "Electric Vehicle Charge Scheduling Mechanism to Maximize Cost Efficiency and User Convenience," in IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3020-3030, May 2019. [DOI:10.1109/TSG.2018.2817067]
7. [7] Gan, L., Topcu, U., Low, S. H., "Optimal decentralized protocol for electric vehicle charging," in IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 940-951, May 2013. [DOI:10.1109/TPWRS.2012.2210288]
8. [8] Clement-Nyns, K., Haesen, E., Driesen, J., "The impact of chargingplug-in hybrid electric vehicles on a residential distribution grid," IEEETrans. Power Syst., vol. 25, no. 1, pp. 371-380, Feb. 2010. [DOI:10.1109/TPWRS.2009.2036481]
9. [9] D. Liu, L. Wang, W. Wang, H. Li, M. Liu and X. Xu, "Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game," in IEEE Access, vol. 9, pp. 92473-92482, 2021. [DOI:10.1109/ACCESS.2021.3091725]
10. [10] Chen, N., Quek, T. Q. S., Tan, C. W., "Optimal charging of electric vehicles in smart grid: Characterization and valley-filling algorithms," in Proc. IEEE 3rd Int. Conf. Smart Grid Commun. (SmartGridComm), Tainan, Taiwan, pp. 13-18, Nov. 2012. [DOI:10.1109/SmartGridComm.2012.6485952]
11. [11] Fan, Z., "A distributed demand response algorithm and its application to PHEV charging in smart grids," IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1280-1290, Sep. 2012. [DOI:10.1109/TSG.2012.2185075]
12. [12] Wen, C. K., Chen, J. C., Teng, J. H., Ting, P.,"Decentralized plug-in electric vehicle charging selection algorithm in power systems," IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1779-1789, Dec. 2012. [DOI:10.1109/TSG.2012.2217761]
13. [13] Kamankesh, H. R., Agelidis, V. G., Kavousi-Fard, A., "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Volume 100, pp. 285-297, 2016. [DOI:10.1016/j.energy.2016.01.063]
14. [14] Tan, J., Wang, L., "Integration of Plug-in Hybrid Electric Vehicles into Residential Distribution Grid Based on Two-Layer Intelligent Optimization," in IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1774-1784, July 2014. [DOI:10.1109/TSG.2014.2313617]
15. [15] Hashemi, B., Shahabi, M., Teimourzadeh-Baboli, P., "A Novel Charging Plan for PEVs Aggregator Based on Combined Market and Network Driven Approach," in International Journal of Smart Electrical Engineering, Vol.7, No.2, Spring 2018.
16. [16] Moghaddam, S. Z., Akbari, T., "Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach," Energy, vol. 151, pp. 478- 489, May 2018. [DOI:10.1016/j.energy.2018.03.074]
17. [17] Mohiti, M., Monsef, H., Lesani, H., "A decentralized robust model for coordinated operation of smart distribution network and electric vehicle aggregators," International Journal of Electrical Power & Energy Systems, Volume 104, Pages 853-867, 2019. [DOI:10.1016/j.ijepes.2018.07.054]
18. [18] Alipour, M., Mohammadi-Ivatloo, B., Moradi-Dalvand, M., Zare, K., "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, vol. 118, pp. 1168-1179, Jan. 2017. [DOI:10.1016/j.energy.2016.10.141]
19. [19] Santos, A., McGuckin, N., Nakamoto, H, Y., Gray,D., Liss, S., "Summary of travel trends: 2009 national household travel survey," Tech. Rep., 2011.


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

tazikeh lemeski A, Ebrahimi R, Zakariazadeh A. Self-scheduling of electric vehicles aggregator in the energy market based on TOU pricing plan. ieijqp. 2022; 10 (4) :38-46
URL: http://ieijqp.ir/article-1-812-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 4 (1-2022) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
Persian site map - English site map - Created in 0.06 seconds with 29 queries by YEKTAWEB 4419