Optimal demand response program design using a three phase optimization algorithm in electric vehicle charge/discharge application

Document Type : Research Paper

Authors

1 Department of Electrical Engineering, Malekan Branch, Islamic Azad University, Malekan, Iran

2 Engineering Faculty, Near East University, 99138 Nicosia, North Cyprus, Mersin 10, Turkey

3 Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul Aydin University, Istanbul, Turkey

Abstract

In this paper, the effects of distributed generation resources and demand response programs on the placement of charging or discharging stations are investigated. The effectiveness of an optimal exploitation approach is evaluated. pivotal factors of optimal charge/discharge power in stations are a combination of technical and economic parameters. The technical parameters contain minimization of network losses, voltage loss reduction in feeders, smoothing network load curve and harmonic elimination. The placement of stations and charge/discharge power were considered the most effective economic parameters. In other words, the minimization of charge/discharge operations results in cost reduction in purchasing power. A price-based demand-response program is considered to manage loads on the customer side and smooth the load curve. meta-heuristic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and imperialist are considered to find an optimal solution. This study is simulated on an IEEE standard 69-bus network. Using a conventional hybrid algorithm shows that the problem of station replacement and charge/discharge program can be solved optimally. Moreover, the effects of an increased number of stations and a disturbance in charge/discharge capacity are examined.

Keywords

[1] J.A. Domınguez-Navarro, R. Dufo-Lopez, J.M. Yusta-Loyo, J.S. Artal-Sevil, and J.L. Bernal-Agustın, Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems, Int. J. Electric. Power Energy Syst. 105 (2019), 46–58.
[2] O. Hafez and K. Bhattacharya, Optimal design of electric vehicle charging stations considering various energy resources, Renew. Energy 107 (2017), 576–589.
[3] M. Islam, H. Shareef, and A. Mohamed, Optimal siting and sizing of rapid charging station for electric vehicles considering Bangi city road network in Malaysia, Turk. J. Electric. Engin. Comput. Sci. 24 (2016), no. 5, 3933–3948.
[4] X. Jiang, J. Wang, Y. Han, and Q. Zhao, Coordination dispatch of electric vehicles charging/discharging and renewable energy resources power in microgrid, Procedia Comput. Sci. 107 (2017), 157–163.
[5] P.R.C. Mendes, L.V. Isorna, C. Bordons, and J.E. Normey-Rico, Energy management of an experimental microgrid coupled to a V2g system, J. Power Sources 327 (2016), 702–713.
[6] M.J. Mirzaei, A. Kazemi, and O. Homaee, A probabilistic approach to determine optimal capacity and location of electric vehicles parking lots in distribution networks, IEEE Trans. Ind. Inf. 12 (2015), no. 5, 1963–1972.
[7] G.R.C. Mouli, P. Bauer, and M. Zeman, System design for a solar powered electric vehicle charging station for workplaces, Appl. Energy 168 (2016), 434–443.
[8] R. Sabzehgar, M.A. Kazemi, M. Rasouli, and P. Fajri, Cost optimization and reliability assessment of a microgrid with large-scale plug-in electric vehicles participating in demand response programs, Int. J. Green Energy 17 (2020), no. 2, 127–136.
[9] S. Tabatabaee, S.S. Mortazavi, and T. Niknam, Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources, Energy 121 (2017), 480–490.
Volume 14, Issue 9
September 2023
Pages 169-180
  • Receive Date: 18 April 2021
  • Revise Date: 14 May 2021
  • Accept Date: 30 June 2021