Optimal location and size of electric vehicle charging and discharging stations in distribution networks with integrated distributed generations
Main Article Content
Abstract
Nowadays, the world is moving towards green energy vehicles and electric vehicles (EVs) are one of the chosen solutions. Vehicle-to-grid (V2G) technology is gradually gaining attention to support issues of performance optimization, energy fluctuations, reducing grid operating costs and bringing optimal efficiency to owners. Along with the rapid increase in the number of EVs, the deployment of effective electric vehicle charging station (EVCS) infrastructure is desirable. However, improper installation can cause many negative impacts on the grid and vice versa, especially EVCS applying V2G charging and discharging techniques. In this study, we propose a computational model to determine the optimal location and size of EVCS applying V2G technique in a distribution network integrating distributed generation sources (DG) with the goal of minimizing active power loss, using an improved method combining the firefly algorithm with the quantum-inspired evolutionary algorithm (QBFA) to find solutions for the problem. The solution results are simulated on a 33-node IEEE standard distribution network using Matlab software and compared with the original FA algorithm to evaluate and propose computational solutions to develop the EVCS system infrastructure in practice.
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Baran, M. E., & Wu, F. F. (1989). Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Transactions on Power delivery, 4(2), 1401-1407.
Bean, J. C., & Hadj-Alouane, A. B. (1992). A dual genetic algorithmfor bounded integer programs. Tech. Rep., University of Michigan, Kalamazoo, Mich, USA.
Boonluk, P., Siritaratiwat, A., Fuangfoo, P., & Khunkitti, S. (2020). Optimal siting and sizing of battery energy storage systems for distribution network of distribution system operators. Batteries, 6(4), 56.
Cazzola, P., Gorner, M., Schuitmaker, R., & Maroney, E. (2016). Global EV outlook 2016. International Energy Agency, France.
Coello, C. A. C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11-12), 1245-1287.
Chinnam, R. B., & Murat, A. E. (2016). Community-aware charging station network design for electrified vehicles in urban areas: Reducing congestion, emissions, improving accessibility, and promoting walking, bicycling, and use of public transportation (No. TRCLC 15-08). Western Michigan University. Transportation Research Center for Livable Communities.
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer methods in applied mechanics and engineering, 186(2-4), 311-338.
Deilami, S., Masoum, A. S., Moses, P. S., & Masoum, M. A. (2011). Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Transactions on smart grid, 2(3), 456-467.
Dixit, M., Kundu, P., & Jariwala, H. R. (2017). Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits. Engineering Science and Technology, an International Journal, 20(2), 482-493.
Farsadi, M., Sattarpour, T., & Nejadi, A. Y. (2015, November). Optimal placement and operation of BESS in a distribution network considering the net present value of energy losses cost. In 2015 9th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 434-439). IEEE.
Fister, I., Fister Jr, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and evolutionary computation, 13, 34-46.
Graham-Rowe, E., Gardner, B., Abraham, C., Skippon, S., Dittmar, H., Hutchins, R., & Stannard, J. (2012). Mainstream consumers driving plug-in battery-electric and plug-in hybrid electric cars: A qualitative analysis of responses and evaluations. Transportation Research Part A: Policy and Practice, 46(1), 140-153.
Hung, D. Q., & Mithulananthan, N. (2012, July). Alternative analytical approaches for renewable DG allocation for energy loss minimization. In 2012 IEEE Power and energy society general meeting (pp. 1-10). IEEE.
Islam, M. M., Mohamed, A., & Shareef, H. (2015, December). Optimal allocation of rapid charging stations for electric vehicles. In 2015 IEEE student conference on research and development (SCOReD) (pp. 378-383). IEEE.
Islam, M. M., Shareef, H., & Mohamed, A. (2018). Optimal location and sizing of fast charging stations for electric vehicles by incorporating traffic and power networks. IET Intelligent Transport Systems, 12(8), 947-957.
Joines, J. A., & Houck, C. R. (1994, June). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. In Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence (pp. 579-584). IEEE.
Liu, X., Zhang, Q., & Cui, S. (2012). Review of electric vehicle V 2 G technology. Diangong Jishu Xuebao(Transactions of China Electrotechnical Society), 27(2), 121-127.
Michalewicz, Z., & Janikow, C. Z. (1991). Genetic algorithms for numerical optimization. Statistics and Computing, 1, 75-91.
Matsuo, Y., Yanagisawa, A., & Yamashita, Y. (2013). A global energy outlook to 2035 with strategic considerations for Asia and Middle East energy supply and demand interdependencies. Energy Strategy Reviews, 2(1), 79-91.
Cheung, N. J., Ding, X. M., & Shen, H. B. (2014). Adaptive firefly algorithm: parameter analysis and its application. PloS one, 9(11), e112634.
Shaaban, M. F., Atwa, Y. M., & El-Saadany, E. F. (2012). PEVs modeling and impacts mitigation in distribution networks. IEEE Transactions on Power Systems, 28(2), 1122-1131.
Tapia, R. (1986). Engineering Optimization: Methods and Applications (GV Reklaitis, A. Ravindran and KM Ragsdell). SIAM Review, 28(2), 284. DOI:10.1137/1028097.
Singh, B. R., & Singh, O. (2012). Global trends of fossil fuel reserves and climate change in the 21st century (Vol. 8, pp. 167-192). chapter.
Teixeira, A. C. R., & Sodré, J. R. (2016). Simulation of the impacts on carbon dioxide emissions from replacement of a conventional Brazilian taxi fleet by electric vehicles. Energy, 115, 1617-1622.
Yang, X. S., & He, X. (2013). Firefly algorithm: recent advances and applications. International journal of swarm intelligence, 1(1), 36-50.
Yong, J. Y., Ramachandaramurthy, V. K., Tan, K. M., & Mithulananthan, N. (2015). Bi-directional electric vehicle fast charging station with novel reactive power compensation for voltage regulation. International Journal of Electrical Power & Energy Systems, 64, 300-310.
Zitouni, F., Harous, S., & Maamri, R. (2021). A novel quantum firefly algorithm for global optimization. Arabian journal for science and engineering, 46(9), 8741-8759.