A coordinated EV Charging Scheduling Containing PV System

MURAT AKIL, Emrah Dokur, Ramazan Bayindir

Abstract


The two main reasons for the increase in carbon emissions are the use of fossil fuel resources in the transportation and energy sector. It is possible to reduce these emissions significantly by expanding Electric Vehicles (EVs) in the transportation sector and renewable energy sources (RES) in electric power generation. While the adoption of EVs is still struggling for various reasons, such as battery costs and reduced range, rising fuel prices combined with government policy sanctions and incentives are increasing the need for EVs. The increased penetration of EVs on the grid is likely to pose a very complex operational problem. Therefore, this penetration can result in overloading of the infrastructure equipment in the distribution system and a power outage. This study focuses on the coordinated charge scheduling for EVs with a photovoltaic (PV) system as one of the Renewable energy sources for seamless integration of EVs into the grid. In this paper, charge scheduling of EVs has been made by considering the EV battery state of energy (SoE) value. Mixed Integer Linear programming (MILP) technique is used for the charge scheduling model of EVs. Thus, the charge scheduling of EVs is made within the allowable limits in the grid. It is also a systematic reference work in the proposed approach because of the load balancing of the EVs with the power supplied from the PV system.

Keywords


Scheduling, monte carlo simulation, PV system, coordinated charging, load balancing

Full Text:

PDF

References


S. Gherairi, “Zero-Emission Hybrid Electric System: Estimated Speed to Prioritize Energy Demand for Transport Applications,” Int. J. Smart grid, vol. 3, no. 4, 2019, doi: 10.20508/ijsmartgrid.v3i4.76.g65.

B. Li, M. C. Kisacikoglu, C. Liu, N. Singh and M. Erol-Kantarci, "Big Data Analytics for Electric Vehicle Integration in Green Smart Cities," in IEEE Communications Magazine, vol. 55, no. 11, pp. 19-25, Nov. 2017, doi: 10.1109/MCOM.2017.1700133.

M. U. Ali, A. Zafar, S. H. Nengroo, S. Hussain, M. J. Alvi, and H. J. Kim, Towards a smarter battery management system for electric vehicle applications: A critical review of lithium-ion battery state of charge estimation, Energies, vol. 12, no. 3. 2019.

A. Amin, K. Tareen, M. Usman, H. Ali, I. Bari, Horan, B., and A. Mahmood, “A review of optimal charging strategy for electric vehicles under dynamic pricing schemes in the distribution charging network,” Sustain., vol. 12, no. 23, pp. 1–28, 2020, doi: 10.3390/su122310160.

F. Lo Franco, M. Ricco, R. Mandrioli, and G. Grandi, “Electric vehicle aggregate power flow prediction and smart charging system for distributed renewable energy self-consumption optimization,” Energies, vol. 13, no. 18, 2020, doi: 10.3390/en13195003.

M. Akil, E. Dokur, and R. Bayindir, “The SOC based dynamic charging coordination of EVs in the PV-penetrated distribution network using real-world data,” Energies, vol. 14, no. 24, pp. 1–19, 2021, doi: 10.3390/en14248508.

Y.W. Chung, "Electric Vehicle–Smart Grid Integration: Load Modeling, Scheduling, and Cyber Security", PhD Dissertation, Dept. of Mechanical Engineering, University of California, Los Angeles, USA, 2020.

M. Akil, E. Dokur, R. Bayindir, "Smart coordination of predictive load balancing for residential electric vehicles based on EMD-Bayesian optimised LSTM", IET Renewable Power Generation, 1– 17 (2022). https:doi.org10.1049rpg2.12572

M. Akil, E. Dokur and R. Bayindir, "Impact of Electric Vehicle Charging Profiles in Data-Driven Framework on Distribution Network," 2021 9th International Conference on Smart Grid (icSmartGrid), 2021, pp. 220-225, doi: 10.1109/icSmartGrid52357.2021.9551247.

M. Akil, E. Dokur and R. Bayindir, "A Systematic Data-driven Analysis of Electric Vehicle Electricity Consumption with Wind Power Integration," 2021 10th International Conference on Renewable Energy Research and Application (ICRERA), 2021, pp. 397-401, doi: 10.1109/ICRERA52334.2021.9598483.

Z. Zhang, H. Shi, R. Zhu, H. Zhao, and Y. Zhu, “Research on electric vehicle charging load prediction and charging mode optimization,” Arch. Electr. Eng., vol. 70, no. 2, pp. 399–414, 2021, doi: 10.24425/aee.2021.136992.

J. Zhu, Z. Yang, M. Mourshed, Guo, Y. Zhou, Y. Chang, Y. Wei and F. Shengzhong , “Electric vehicle charging load forecasting: A comparative study of deep learning approaches,” Energies, vol. 12, no. 14, pp. 1–19, 2019, doi: 10.3390/en12142692.

B. C. Harris and M. E. Webber, "An empirically-validated methodology to simulate electricity demand for electric vehicle charging." Applied Energy, 181:126-172, 2014.

Y. Xiang, Y. Wang, S. Xia and F. Teng, "Charging Load Pattern Extraction for Residential Electric Vehicles: A Training-Free Nonintrusive Method," in IEEE Transactions on Industrial Informatics, vol. 17, no. 10, pp. 7028-7039, Oct. 2021, doi: 10.1109/TII.2021.3060450.

M. Lahariya, D. F. Benoit, and C. Develder, “Synthetic data generator for electric vehicle charging sessions: modeling and evaluation using real-world data,” Energies, vol. 13, no. 6, 2020, doi: 10.3390/en13164211.

J. J. Mies, J. R. Helmus, and R. van den Hoed, “Estimating the charging profile of individual charge sessions of Electric Vehicles in The Netherlands,” World Electr. Veh. J., vol. 9, no. 2, 2018, doi: 10.3390/wevj9020017.

C. Ion and C. Marinescu, "Optimal Charging Scheduling of Electrical Vehicles in a Residential Microgrid based on RES," 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), 2019, pp. 397-400, doi: 10.1109ICRERA47325.2019.8996966.

Z. Wei, Y. Li, Y. Zhang and L. Cai, "Intelligent Parking Garage EV Charging Scheduling Considering Battery Charging Characteristic," in IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2806-2816, March 2018, doi: 10.1109/TIE.2017.2740834.

M. Honarmand, A. Zakariazadeh, and S. Jadid, “Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization,” J. Franklin Inst., vol. 352, no. 2, pp. 449–467, 2015, doi: 10.1016/j.jfranklin.2014.01.019.

S. M. B. Sadati, J. Moshtagh, M. Shafie-khah, and J. P. S. Catalão, “Smart distribution system operational scheduling considering electric vehicle parking lot and demand response programs,” Electr. Power Syst. Res., vol. 160, pp. 404–418, 2018, doi: 10.1016/j.epsr.2018.02.019.

?. ?engör, A. K. Ereno?lu, O. Erdinç, A. Ta?c?karao?lu and J. P. S. Catalão, "Optimal Coordination of EV Charging through Aggregators under Peak Load Limitation Based DR Considering Stochasticity," 2018 International Conference on Smart Energy Systems and Technologies (SEST), 2018, pp. 1-6, doi: 10.1109SEST.2018.8495696.

Y. Lu, Y. Li, D. Xie, E. Wei, X. Bao, H. Chen and X. Zhong, “The application of improved random forest algorithm on the prediction of electric vehicle charging load,” Energies, vol. 11, no. 11, 2018, doi: 10.3390/en11113207.

M. Sedighizadeh, A. Mohammadpour, and S. M. M. Alavi, “A daytime optimal stochastic energy management for EV commercial parking lots by using approximate dynamic programming and hybrid big bang big crunch algorithm,” Sustain. Cities Soc., vol. 45, no. July 2018, pp. 486–498, 2019, doi: 10.1016/j.scs.2018.12.016.

M. E. Shayan, “The Biomass Supply Chain Network Auto-Regressive Moving Average Algorithm,” International Journal of Smart grid, vol. 5, no. 1, 2021, doi: 10.20508/ijsmartgrid.v5i1.153.g135.

A. Arab, F. Lakdja, Y. A. Gherbi, and F. Z. Gherbi, “Impact of Incremental Piecewise Linear Cost/Benefit Functions on DC-OPF Based Deregulated Electricity Markets,” Int. J. Renew. Energy Res., vol. 12, no. 2, pp. 703–711, 2022, doi: 10.20508/ijrer.v12i2.12769.g8458.

T. Harighi, R. Bayindir, and E. Hossain, “Overviewing Quality of Electric Vehicle Charging Stations’ Service Evaluation,” International Journal of Smart grid, vol. 2, no. 1, pp. 40–48, 2018, doi: 10.20508/ijsmartgrid.v2i1.11.g14.

P. Geetha and S. Usha, “Critical Review and Analysis of Solar Powered Electric Vehicle Charging Station,” International Journal of Renewable Energy Research., vol. 12, no. 1, pp. 581–600, 2022, doi: 10.20508/ijrer.v12i1.12821.g8443.

A. Rautiainen, S. Repo, P. Jarventausta, A. Mutanen, K. Vuorilehto, and K. Jalkanen, ‘‘Statistical charging load modeling of PHEVs in electricity distribution networks using national travel survey data,’’ IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1650–1659, Dec. 2012.

N. H. Tehrani and P. Wang, ‘‘Probabilistic estimation of plug-in elec- tric vehicles charging load profile,’’ Electr. Power Syst. Res., vol. 124, pp. 133–143, Jul. 2015.

U.S. Department of Transportation Federal Highway Administration. National Household Travel Survey; U.S. Department of Transportation Federal Highway Administration: Washington, DC, USA, 2009.

M. Akil, E. Dokur, and R. Bayindir, “Modeling and evaluation of SOC-based coordinated EV charging for power management in a distribution system,” Turkish J. Electr. Eng. Comput. Sci., pp. 678–694, 2021, doi: 10.3906/elk-2105-100

G. Abdelaal, M. I. Gilany, M. Elshahed, H. M. Sharaf, A. Fotouh, and E. Gharably, “A Smart On-Line Centralized Coordinated Charging Strategy in Residential Distribution Networks,” Int. J. Renew. Energy Res. (IJRER),2021, vol. 11, no. 2, pp. 523–534, 2021.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v6i3.252.g240

Refbacks

  • There are currently no refbacks.


www.ijsmartgrid.com; www.ijsmartgrid.org

ilhcol@gmail.com; ijsmartgrid@nisantasi.edu.tr

Online ISSN: 2602-439X

Publisher: ilhami COLAK (istanbul Nisantasi Univ)

Cited in Google Scholar and CrossRef