The CVaR-based risk assessment of the electric vehicle's intelligent parking lots with energy storage devices including equalizers and fuel cell

Document Type : Original Article

Authors

1 Department of Electrical Engineering, University of Tabriz, Tabriz, Iran

2 Department of Electrical Engineering, University of Tabriz, Tabriz, Iran.

3 Department of Electrical Engineering, University of Bonab, Bonab, Iran

Abstract

The interest in utilizations of electric vehicles (EVs) causes to increase the penetration of intelligent parking lots (IPL). Also, vehicle-to-grid (V2G) technology next to grid-to-vehicle (G2V) technology improves the profitability of the IPLs. In addition to vehicle charge and discharge management, the IPLs can gain more profit by installing and accessing loads and resources. In this paper, the IPLs next to the hydrogen storage system (HSS) containing electrolysis, fuel cell and hydrogen storage tanks are considered to serve the loads in the existence of the upstream grid that is modeled through a scenario approach based on stochastic optimization. The uncertainties related to the electrical load, market price, vehicle's arrival and departure time, initial State-of-charge (SOC), and desired SOC of the car are considered in the proposed model. Besides, the financial risks related to the proposed hydrogen storage-based intelligent parking lot's uncertain parameters are modeled by the conditional value-at-risk (CVaR) method to get the risk-averse and risk-neutral strategies during the system operation horizon. The obtained results demonstrate that the uncertainties have a significant impact on smart parking operators' profitability, so considering uncertainty is a critical issue for parking lots. Risk results also represent that the variation of financial risk in the higher deviation of uncertain parameters is more than risk variation in lower deviations.

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References

1. Kikusato, H., et al., Electric vehicle charge–discharge management for utilization of photovoltaic by coordination between home and grid energy management systems. IEEE Transactions on Smart Grid, 2018. 10(3): p. 3186-3197.
2. Raslavicius, L., et al., Electric vehicles challenges and opportunities: Lithuanian review. Renewable and Sustainable Energy Reviews, 2015. 42: p. 786-800.
3. Jabeen, F., et al. Electric vehicle battery charging behaviour: findings from a driver survey. in Proceedings of the Australasian Transport Research Forum. 2013.
4. Badawy, M.O. and Y. Sozer. Power flow management of a grid tied PV-battery powered fast electric vehicle charging station. in 2015 IEEE Energy Conversion Congress and Exposition (ECCE). 2015. IEEE.
5. Gago, R.G., S.F. Pinto, and J.F. Silva. G2V and V2G electric vehicle charger for smart grids. in 2016 IEEE International Smart Cities Conference (ISC2). 2016. IEEE.
6. Rezaee, S., E. Farjah, and B. Khorramdel, Probabilistic analysis of plug-in electric vehicles impact on electrical grid through homes and parking lots. IEEE Transactions on Sustainable Energy, 2013. 4(4): p.1024-1033.
7. Monteiro, V., J. Pinto, and J.L. Afonso, Operation modes for the electric vehicle in smart grids and smart homes: Present and proposed modes. IEEE Transactions on Vehicular Technology, 2015. 65(3): p. 1007-1020.
8. Kori, S., VEHICLE-TO-GRID POWER IMPLEMENTATION: FROM STABILIZING THE GRID TO SUPPORTING LARGE-SCALE RENEWABLE ENERGY. Journal Current Science, 2017. 18(12).
9. Gür, T.M., Review of electrical energy storage technologies, materials and systems: challenges and prospects for large-scale grid storage. Energy & Environmental Science, 2018. 11(10): p. 2696-2767.
10. Jannati, J. and D. Nazarpour, Optimal energy management of the smart parking lot under demand response program in the presence of the electrolyser and fuel cell as hydrogen storage system. Energy Conversion and Management, 2017. 138: p. 659-669.
11. Huang, Z., et al., Modeling and multi-objective optimization of a stand alone PV-hydrogen-retired EV battery hybrid energy system. Energy Conversion and Management, 2019. 181: p. 80-92.
12. Dawood, F., G. Shafiullah, and M. Anda, Stand-alone microgrid with 100% renewable energy: A case study with hybrid solar PV-battery-hydrogen. Sustainability, 2020. 12(5): p. 2047.
13. Marzoghi, A.F., et al., Interval multi-objective optimization of hydrogen storage based intelligent parking lot of electric vehicles under peak demand management. Journal of Energy Storage, 2020. 27: p. 101123
14. Aghajani, S. and M. Kalantar, Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach. Energy, 2017. 139: p. 422-432.
15. Thomas, D., et al., An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes. Applied Energy, 2020. 260: p. 114314.
16. Ioakimidis, C.S., et al., Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot. Energy, 2018. 148: p. 148-158.
17. M. Ahrabi, M. Abedi, H. Nafisi, M. A. Mirzaei, B. Mohammadi-Ivatloo, and M. Marzband, “Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach,” Int. J. Electr. Power Energy Syst., vol. 125, p. 106546, Feb. 2021.
18. Hemmati M, Abapour M, Mohammadi-Ivatloo B. Optimal scheduling of smart Microgrid in presence of battery swapping station of electrical vehicles. InElectric Vehicles in Energy Systems 2020 (pp. 249-267). Springer, Cham.
19. Brady, J. and M. O’Mahony, Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data. Sustainable Cities and Society, 2016. 26: p. 203-216.
20. Mirzaei, M.J., A. Kazemi, and O. Homaee, Real-world based approach for optimal management of electric vehicles in an intelligent parking lot considering simultaneous satisfaction of vehicle owners and parking operator (Retraction of vol 76, pg 345, 2014). Energy, 2015. 85: p. 687-687.
21. Honarmand, M., A. Zakariazadeh, and S. Jadid, Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization. Journal of the Franklin Institute, 2015. 352(2): p. 449-467.
22. Liu, J., et al., An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles. Journal of Energy Storage, 2020. 27: p. 101057.
23. Mirzaei MA, Hemmati M, Zare K, Abapour M, Mohammadi-Ivatloo B, Marzband M, Anvari-Moghaddam A. A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets. International Journal of Electrical Power & Energy Systems. 2020 Dec 1;123:106293.
24. M. Agabalaye-Rahvar, A. Mansour-Saatloo, M. A. Mirzaei, B. Mohammadi-Ivatloo, K. Zare, and A. Anvari-Moghaddam, “Robust Optimal Operation Strategy for a Hybrid Energy System Based on GasFired Unit, Power-to-Gas Facility and Wind Power in Energy Markets,” Energies, vol. 13, no. 22, p. 6131, 2020.
25. M. Zare Oskouei, M. A. Mirzaei, B. Mohammadi-Ivatloo, M. Shafiee, M. Marzband, and A. Anvari-Moghaddam, “A hybrid robust-stochastic approach to evaluate the profit of a multi-energy retailer in trilayer energy markets,” Energy, vol. 214, p. 118948, Jan. 2021, doi:10.1016/j.energy.2020.118948.
26. Mirzaei MA, Hemmati M, Zare K, Mohammadi-Ivatloo B, Abapour M, Marzband M, Farzamnia A. Two-stage robust-stochastic electricity market clearing considering mobile energy storage in rail transportation. IEEE Access. 2020 Jun 26;8:121780-94.
27. Jannati, J. and D. Nazarpour, Multi-objective scheduling of electric vehicles intelligent parking lot in the presence of hydrogen storage system under peak load management. Energy, 2018. 163: p. 338-350.
28. M. N. Heris et al., “Evaluation of hydrogen storage technology in risk-constrained stochastic scheduling of multi-carrier energy systems considering power, gas and heating network constraints,” Int. J. Hydrogen Energy, vol. 45, no. 55, pp. 30129–30141, Nov. 2020, doi:10.1016/j.ijhydene.2020.08.090.
29. A. Mansour-Saatloo, M. A. Mirzaei, B. Mohammadi-Ivatloo, and K. Zare, “A Risk-Averse Hybrid Approach for Optimal Participation of Power-toHydrogen Technology-Based Multi-Energy Microgrid in Multi-Energy Markets,” Sustain. Cities Soc., vol. 63, p. 102421, Dec. 2020, doi:10.1016/j.scs.2020.102421.
30. Hemmati M, Mohammadi-Ivatloo B, Ghasemzadeh S, Reihani E. Riskbased optimal scheduling of reconfigurable smart renewable energy based microgrids. International Journal of Electrical Power & Energy Systems. 2018 Oct 1;101:415-28.
31. Hemmati M, Mohammadi-Ivatloo B, Abapour M, Anvari-Moghaddam A. Optimal chance-constrained scheduling of reconfigurable microgrids considering islanding operation constraints. IEEE Systems Journal. 2020 Jan 29;14(4):5340-9.
32. Dolatabadi, A. and B. Mohammadi-Ivatloo, Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response. Applied Thermal Engineering, 2017. 123: p. 40-49.
33. Santos, D.M., C.A. Sequeira, and J.L. Figueiredo, Hydrogen production by alkaline water electrolysis. Química Nova, 2013. 36(8): p. 1176-1193.
34. Mohseni, S. and S.M. Moghaddas-Tafreshi, A multi-agent system for optimal sizing of a cooperative self-sustainable multi-carrier microgrid. Sustainable cities and society, 2018. 38: p. 452-465.
35. Wang, S. and S.P. Jiang, Prospects of fuel cell technologies. National Science Review, 2017. 4(2): p. 163-166.
36. Habib, S., M. Kamran, and U. Rashid, Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks–a review. Journal of Power Sources, 2015. 277: p. 205-214.
37. Habib, S. and M. Kamran. A novel vehicle-to-grid technology with constraint analysis-a review. in 2014 International Conference on Emerging Technologies (ICET). 2014. IEEE.
38. Guille, C. and G. Gross, A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy policy, 2009. 37(11): p. 4379-4390.
39. Dolatabadi, A., M. Jadidbonab, and B. Mohammadi-ivatloo, Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach. IEEE Transactions on Sustainable Energy, 2018. 10(1): p. 438-448.
40. Soroudi, A. and A. Keane, Risk averse energy hub management considering plug-in electric vehicles using information gap decision theory, in Plug in electric vehicles in smart grids. 2015, Springer. p. 107-127.
41. Gazijahani, F.S. and J. Salehi, Integrated DR and reconfiguration scheduling for optimal operation of microgrids using Hong’s point estimate method. International Journal of Electrical Power & Energy Systems, 2018. 99: p. 481-492.
42. Tafreshi, S.M.M., et al., A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid. Renewable and Sustainable Energy Reviews, 2016. 66: p. 934-947.
43. Shahverdi, M. and S. Moghaddas-Tafreshi. Operation optimization of fuel cell power plant with new method in thermal recovery using particle swarm algorithm. in 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. 2008. IEEE.
44. Li, P., Z. Zhou, and R. Shi. Probabilistic optimal operation management of microgrid using point estimate method and improved bat algorithm. in 2014 IEEE PES General Meeting| Conference & Exposition. 2014. IEEE.
45. Roustai, M., et al., A scenario-based optimization of Smart Energy Hub operation in a stochastic environment using conditional-value-at-risk. Sustainable cities and society, 2018. 39: p. 309-316.
46. Sadati, S.M.B., et al., Bi-level model for operational scheduling of a distribution company that supplies electric vehicle parking lots. Electric Power Systems Research, 2019. 174: p. 105875.
47. Razipour, R., S.-M. Moghaddas-Tafreshi, and P. Farhadi, Optimal management of electric vehicles in an intelligent parking lot in the presence of hydrogen storage system. Journal of Energy Storage, 2019. 22: p. 144-152
Journal of Energy Management and Technology
Volume 6, Issue 1
March 2022
Pages 1-8

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History

  • Receive Date: 11 February 2021
  • Revise Date: 08 April 2021
  • Accept Date: 12 April 2021

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