Optimal Transmission Congestion Management with V2G in Smart Grid

Amin Niaz Azari; Soodabeh Soleymani; Babak Mozafari; Ghazaleh Sarfi

doi:doi:10.11648/j.epes.20180702.11

| Peer-Reviewed

Optimal Transmission Congestion Management with V2G in Smart Grid

Amin Niaz Azari, Soodabeh Soleymani, Babak Mozafari, Ghazaleh Sarfi

Published in American Journal of Electrical Power and Energy Systems (Volume 7, Issue 2)

Received: 7 March 2018 Accepted: 29 March 2018 Published: 5 May 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The power system operators are looking for optimizing the power generating resources in the unit commitment problems considering the binding constraints. With the reconstruction in the power network structure, the increase in electricity price during some hours of day, and increase in fuel price, the utilities need to change their management paradigms. A smart grid can be a suitable choice for addressing these issues because they are able to continue working smartly. With the progress in the technology of batteries, power electronic devices, many well-known companies such as Toyota and Tesla have started producing electric and hybrid vehicles since 1990. Introducing electric vehicles to the power system provides unprecedented environmental and economic opportunities and at the same time new challenges to deal with for the system operators. The vehicle to grid (V2G) technology can enable the electric vehicles to inject energy to the grid in addition to its regular path of receiving energy from the grid. In this paper, the effect of the technology of V2G on the operation cost and LMP with considering the line congestion limits are investigated. To solve the optimization problem, a mixed integer linear programming (MILP) technique in the GAMS software is used. The proposed method is tested on the IEEE 6 bus system and the results are presented. This simulation shows that although the presence of electric vehicles has no significant effect on reducing or increasing of the operation cost in smart grid and may even reduce the operation cost in a certain number of EVs, due to their daily trips and shift from a bus to another bus, they act as a transmission line during the day and reduce the line congestion, resulting in a significant reduction in the local marginal price (LMP) in the peak load hours, and also increasing the security of the power system when the line capacity falls.

Published in	American Journal of Electrical Power and Energy Systems (Volume 7, Issue 2)
DOI	10.11648/j.epes.20180702.11
Page(s)	16-24
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

EV, Line Congestion, V2G, Unit Commitment

References

[1]	S. M. R. Tousi and S. Aznavi, “Performance optimization of a STATCOM based on cascaded multi-level converter topology using multi-objective Genetic Algorithm,” in Electrical Engineering (ICEE), 2015 23rd Iranian Conference on, 2015, pp. 1688–1693.
[2]	S. Aznavy, A. Deihimi, “Determining Switching Angles And Dc Voltages In Cascaded Multilevel Inverter For Optimizing Thd In Different Output Voltage Levels Using Genetic Algorithm”, in 6th International Conference on Technical and Physical Problems of Power Engineering, pp. 449-453, September 2010.
[3]	C. Quinn, et al, "The effect of communication architecture on the availability, reliability and economics of plug-in hybrid electric vehicle-to-grid ancillary services", Journal of Power Sources, vol. 195, pp. 1500-1509, 2010.
[4]	I. Niazazari, H. A. Abyaneh, M. J. Farah, F. Safaei, and H. Nafisi, “Voltage profile and power factor improvement in PHEV charging station using a probabilistic model and flywheel,” 2014 19th Conference on Electrical Power Distribution Networks (EPDC), pp. 100-105, 2014.
[5]	S. Han. and K. Sezaki "Development of an optimal vehicle-to-grid aggregator for frequency regulation," IEEE Transactions on Smart Grid, vol. 1, pp. 65-72, 2010.
[6]	S. L. Andersson, "Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany," Energy Policy, vol. 38, pp. 2751-2762, 2010.
[7]	Y. Lee, A. Khaligh, andA. Emadi, “Advanced integrated bi-directional AC/DC and DC/DC converter for plug-in hybrid electric vehicles,”IEEE Trans. Veh. Technol., vol. 58, no. 8, pp. 3970–3980, Oct. 2009.
[8]	S. M. Lukic, J. Cao, R. C. Bansal, R. Rodriguez, and A. Emadi, “Energy storage systems for automotive applications,” IEEE Trans. Ind. Electron., vol. 55, no. 6, pp. 2258–2267, Jun 2008.
[9]	M. Korpas, R. Hildrum, and A. T. Holen, “Operation and sizing of energy storage for wind power plants in a market system,” in Proc.14th PSCC, Sevilla, Spain, Jun. 2002.
[10]	J. R. Pillai and B. Bak-Jensen, "Integration of vehicle-to-grid in the Western Danish power system," IEEE Transactions on Sustainable Energy, vol. 2, pp. 12-19, 2011.
[11]	W. Kempton and J. Tomic "Vehicle-to-grid power fundamentals: Calculating capacity and net revenue," Journal of Power Sources, vol. 144, pp. 268-279, 2005.
[12]	I. Niazazari and H. Livani, “Disruptive event classification using pmu data in distribution networks,” arXiv preprint arXiv: 1703.09800, 2017.
[13]	M. A. Chitsazan, A. M Trzynadlowski, “State estimation of IEEE 14 bus with interphase power controller using WLS method”, Energy Conversion Congress and Exposition (ECCE), 2016 IEEE, pp. 1-5, Sep. 2016.
[14]	V. Sarfi, I. Niazazari, and H. Livani, "Multiobjective fireworks optimization framework for economic emission dispatch in microgrids." North American Power Symposium (NAPS), 2016, pp. 1-6, Nov. 2016.
[15]	A Ghasemkhani, L Yang, “Reinforcement Learning based Pricing for Demand Response,” in Communications Workshops (ICC Workshops), 2018 IEEE International Conference on. IEEE, 2018.
[16]	A. Ghasemkhani, A. Anvari-Moghaddam, J. M. Guerrero, and B. Bak-Jensen, “An Efficient Multi-objective Approach for Designing of Communication Interfaces in Smart Grids,” Proceedings of IEEE PES Innovative Smart Grid Technologies, Ljubljana, Slovenia (ISGT Europe 2016), Oct. 2016.
[17]	A. Forooghi Nematollahi, A. Dadkhah, O. Asgari Gashteroodkhani, and B. Vahidi, "Optimal sizing and siting of DGs for loss reduction using an iterative-analytical method," Journal of Renewable and Sustainable Energy, vol. 8, no. 5, p. 055301, 2016.
[18]	V. Sarfi and H. Livani, “A novel multi-objective security-constrained power management for isolated microgrids in all-electric ships,” 2017 IEEE Electric Ship Technologies Symposium (ESTS), 2017.
[19]	O. A. Gashteroodkhani, B. Vahidi, and A. Zaboli, "Time-time matrix z-score vector-based fault analysis method for series-compensated transmission lines," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 25, pp. 2647-2659, 2017.
[20]	V. Sarfi, H. Livani, and L. Yliniemi, “A Novel Multi-Objective Economic-Emission Dispatch in Microgrids” IEEE Power Engineering Society General Meeting, Chicago, Illinios, July 2017.
[21]	M. A. Chitsazan, M. Sami Fadali, Amanda K. Nelson, A. M Trzynadlowski, “Wind speed forecasting using an echo state network with nonlinear output functions”, American Control Conference (ACC), 2017 IEEE, pp. 5306-5311, May. 2017.
[22]	J. Wang, M. Shahidehpour, Z. Li, “Security Constrained Unit Commitment With Volatile Wind Power Generation”, IEEE Transactions on Power Systems, vol. 23, NO. 3, August 2008.
[23]	M. Khodayar and M. Shahidehpour, “Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC” IEEE Transactions on Smart Grid, Vol. 3, No. 3, September 2012.
[24]	A. Saber and G. Venayagamoorthy, “Plug-in vehicles and renewable energy sources for cost and emission reduction,” IEEE Trans. Ind. Electron, 2010.
[25]	C. Roe, A. P. Meliopoulos, J. Meisel, and T. Overbye, “Power system level impacts of plug-in hybrid electric vehicles using simulation data,”in Proc. IEEE Energy 2030, Atlanta, GA, Nov. 17–18, 2008.
[26]	J. Tomic and W. Kempton, “Using fleets of electric-drive vehicles for grid support,” J. Power Sources, vol. 168, no. 2, pp. 459–468, Jun. 2007.
[27]	GAMS Documentation [Online]. Available: http://www.gams.com/docs/document.htm.

Cite This Article

Plain Text BibTeX RIS

APA Style

Amin Niaz Azari, Soodabeh Soleymani, Babak Mozafari, Ghazaleh Sarfi. (2018). Optimal Transmission Congestion Management with V2G in Smart Grid. American Journal of Electrical Power and Energy Systems, 7(2), 16-24. https://doi.org/10.11648/j.epes.20180702.11

Copy | Download

ACS Style

Amin Niaz Azari; Soodabeh Soleymani; Babak Mozafari; Ghazaleh Sarfi. Optimal Transmission Congestion Management with V2G in Smart Grid. Am. J. Electr. Power Energy Syst. 2018, 7(2), 16-24. doi: 10.11648/j.epes.20180702.11

Copy | Download

AMA Style

Amin Niaz Azari, Soodabeh Soleymani, Babak Mozafari, Ghazaleh Sarfi. Optimal Transmission Congestion Management with V2G in Smart Grid. Am J Electr Power Energy Syst. 2018;7(2):16-24. doi: 10.11648/j.epes.20180702.11

Copy | Download

@article{10.11648/j.epes.20180702.11,
  author = {Amin Niaz Azari and Soodabeh Soleymani and Babak Mozafari and Ghazaleh Sarfi},
  title = {Optimal Transmission Congestion Management with V2G in Smart Grid},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {7},
  number = {2},
  pages = {16-24},
  doi = {10.11648/j.epes.20180702.11},
  url = {https://doi.org/10.11648/j.epes.20180702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20180702.11},
  abstract = {The power system operators are looking for optimizing the power generating resources in the unit commitment problems considering the binding constraints. With the reconstruction in the power network structure, the increase in electricity price during some hours of day, and increase in fuel price, the utilities need to change their management paradigms. A smart grid can be a suitable choice for addressing these issues because they are able to continue working smartly. With the progress in the technology of batteries, power electronic devices, many well-known companies such as Toyota and Tesla have started producing electric and hybrid vehicles since 1990. Introducing electric vehicles to the power system provides unprecedented environmental and economic opportunities and at the same time new challenges to deal with for the system operators. The vehicle to grid (V2G) technology can enable the electric vehicles to inject energy to the grid in addition to its regular path of receiving energy from the grid. In this paper, the effect of the technology of V2G on the operation cost and LMP with considering the line congestion limits are investigated. To solve the optimization problem, a mixed integer linear programming (MILP) technique in the GAMS software is used. The proposed method is tested on the IEEE 6 bus system and the results are presented. This simulation shows that although the presence of electric vehicles has no significant effect on reducing or increasing of the operation cost in smart grid and may even reduce the operation cost in a certain number of EVs, due to their daily trips and shift from a bus to another bus, they act as a transmission line during the day and reduce the line congestion, resulting in a significant reduction in the local marginal price (LMP) in the peak load hours, and also increasing the security of the power system when the line capacity falls.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Optimal Transmission Congestion Management with V2G in Smart Grid
AU  - Amin Niaz Azari
AU  - Soodabeh Soleymani
AU  - Babak Mozafari
AU  - Ghazaleh Sarfi
Y1  - 2018/05/05
PY  - 2018
N1  - https://doi.org/10.11648/j.epes.20180702.11
DO  - 10.11648/j.epes.20180702.11
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 16
EP  - 24
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20180702.11
AB  - The power system operators are looking for optimizing the power generating resources in the unit commitment problems considering the binding constraints. With the reconstruction in the power network structure, the increase in electricity price during some hours of day, and increase in fuel price, the utilities need to change their management paradigms. A smart grid can be a suitable choice for addressing these issues because they are able to continue working smartly. With the progress in the technology of batteries, power electronic devices, many well-known companies such as Toyota and Tesla have started producing electric and hybrid vehicles since 1990. Introducing electric vehicles to the power system provides unprecedented environmental and economic opportunities and at the same time new challenges to deal with for the system operators. The vehicle to grid (V2G) technology can enable the electric vehicles to inject energy to the grid in addition to its regular path of receiving energy from the grid. In this paper, the effect of the technology of V2G on the operation cost and LMP with considering the line congestion limits are investigated. To solve the optimization problem, a mixed integer linear programming (MILP) technique in the GAMS software is used. The proposed method is tested on the IEEE 6 bus system and the results are presented. This simulation shows that although the presence of electric vehicles has no significant effect on reducing or increasing of the operation cost in smart grid and may even reduce the operation cost in a certain number of EVs, due to their daily trips and shift from a bus to another bus, they act as a transmission line during the day and reduce the line congestion, resulting in a significant reduction in the local marginal price (LMP) in the peak load hours, and also increasing the security of the power system when the line capacity falls.
VL  - 7
IS  - 2
ER  -

Copy | Download

Author Information

Amin Niaz Azari

Power Distribution Company of Semnan, Semnan, Iran
Soodabeh Soleymani

Electrical Department, Islamic Azad University, Science & Research Branch, Tehran, Iran
Babak Mozafari

Electrical Department, Islamic Azad University, Science & Research Branch, Tehran, Iran
Ghazaleh Sarfi

Electrical Department, Iran University of Science and Technology, Tehran, Iran

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Amin Niaz Azari, Soodabeh Soleymani, Babak Mozafari, Ghazaleh Sarfi. (2018). Optimal Transmission Congestion Management with V2G in Smart Grid. American Journal of Electrical Power and Energy Systems, 7(2), 16-24. https://doi.org/10.11648/j.epes.20180702.11

Copy | Download

ACS Style

Amin Niaz Azari; Soodabeh Soleymani; Babak Mozafari; Ghazaleh Sarfi. Optimal Transmission Congestion Management with V2G in Smart Grid. Am. J. Electr. Power Energy Syst. 2018, 7(2), 16-24. doi: 10.11648/j.epes.20180702.11

Copy | Download

AMA Style

Amin Niaz Azari, Soodabeh Soleymani, Babak Mozafari, Ghazaleh Sarfi. Optimal Transmission Congestion Management with V2G in Smart Grid. Am J Electr Power Energy Syst. 2018;7(2):16-24. doi: 10.11648/j.epes.20180702.11

Copy | Download

@article{10.11648/j.epes.20180702.11,
  author = {Amin Niaz Azari and Soodabeh Soleymani and Babak Mozafari and Ghazaleh Sarfi},
  title = {Optimal Transmission Congestion Management with V2G in Smart Grid},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {7},
  number = {2},
  pages = {16-24},
  doi = {10.11648/j.epes.20180702.11},
  url = {https://doi.org/10.11648/j.epes.20180702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20180702.11},
  abstract = {The power system operators are looking for optimizing the power generating resources in the unit commitment problems considering the binding constraints. With the reconstruction in the power network structure, the increase in electricity price during some hours of day, and increase in fuel price, the utilities need to change their management paradigms. A smart grid can be a suitable choice for addressing these issues because they are able to continue working smartly. With the progress in the technology of batteries, power electronic devices, many well-known companies such as Toyota and Tesla have started producing electric and hybrid vehicles since 1990. Introducing electric vehicles to the power system provides unprecedented environmental and economic opportunities and at the same time new challenges to deal with for the system operators. The vehicle to grid (V2G) technology can enable the electric vehicles to inject energy to the grid in addition to its regular path of receiving energy from the grid. In this paper, the effect of the technology of V2G on the operation cost and LMP with considering the line congestion limits are investigated. To solve the optimization problem, a mixed integer linear programming (MILP) technique in the GAMS software is used. The proposed method is tested on the IEEE 6 bus system and the results are presented. This simulation shows that although the presence of electric vehicles has no significant effect on reducing or increasing of the operation cost in smart grid and may even reduce the operation cost in a certain number of EVs, due to their daily trips and shift from a bus to another bus, they act as a transmission line during the day and reduce the line congestion, resulting in a significant reduction in the local marginal price (LMP) in the peak load hours, and also increasing the security of the power system when the line capacity falls.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Optimal Transmission Congestion Management with V2G in Smart Grid
AU  - Amin Niaz Azari
AU  - Soodabeh Soleymani
AU  - Babak Mozafari
AU  - Ghazaleh Sarfi
Y1  - 2018/05/05
PY  - 2018
N1  - https://doi.org/10.11648/j.epes.20180702.11
DO  - 10.11648/j.epes.20180702.11
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 16
EP  - 24
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20180702.11
AB  - The power system operators are looking for optimizing the power generating resources in the unit commitment problems considering the binding constraints. With the reconstruction in the power network structure, the increase in electricity price during some hours of day, and increase in fuel price, the utilities need to change their management paradigms. A smart grid can be a suitable choice for addressing these issues because they are able to continue working smartly. With the progress in the technology of batteries, power electronic devices, many well-known companies such as Toyota and Tesla have started producing electric and hybrid vehicles since 1990. Introducing electric vehicles to the power system provides unprecedented environmental and economic opportunities and at the same time new challenges to deal with for the system operators. The vehicle to grid (V2G) technology can enable the electric vehicles to inject energy to the grid in addition to its regular path of receiving energy from the grid. In this paper, the effect of the technology of V2G on the operation cost and LMP with considering the line congestion limits are investigated. To solve the optimization problem, a mixed integer linear programming (MILP) technique in the GAMS software is used. The proposed method is tested on the IEEE 6 bus system and the results are presented. This simulation shows that although the presence of electric vehicles has no significant effect on reducing or increasing of the operation cost in smart grid and may even reduce the operation cost in a certain number of EVs, due to their daily trips and shift from a bus to another bus, they act as a transmission line during the day and reduce the line congestion, resulting in a significant reduction in the local marginal price (LMP) in the peak load hours, and also increasing the security of the power system when the line capacity falls.
VL  - 7
IS  - 2
ER  -

Copy | Download