### International Journal of Mathematical, Engineering and Management Sciences

#### ISSN: 2455-7749

Optimal Network Reconfiguration with Distributed Generation and Electric Vehicle Charging Stations

#### Optimal Network Reconfiguration with Distributed Generation and Electric Vehicle Charging Stations

Surender Reddy Salkuti
Department of Railroad Electrical Systems, Woosong University, Daejeon, 34606, Republic of Korea.

;
Accepted on June 27, 2021

Abstract

This paper proposes an optimal network reconfiguration (ONR) by integrating the renewable energy (RE) based distributed generation (DG) resources, i.e., wind and solar photovoltaic (PV) modules, and electric vehicle charging stations (EVCS). The uncertainties related to renewable energy sources (RESs) are handled by using probability analysis. In this work, wind uncertainty is handled by using Weibull probability density function (PDF), and solar PV uncertainty is modeled by using Beta PDF. This paper also models the load of EVCSs. The ONR is a tool to operate distribution systems (DSs) at optimum cost/loss. In the literature, most of the ONR problems are solved as single objective type. This neccessiate the development of multi-objective based ONR problem and solved using the multi-objective algorithms by considering multiple objectives. Therefore in this paper, total cost of operation and power losses are considered as two objectives functions. The single objective-based ONR is solved using crow search algorithm (CSA) and multi-objective-based ONR is solved using multi-objective-based CSA. As the DS is unbalanced, the power flow for the unbalanced system will include the three-phase transformer. The ONR problem has been solved by considering 17 bus unbalanced and balanced DSs.

Keywords- Electric vehicles, Distribution system, Network reconfiguration, Renewable energy, Uncertainty.

Citation

Salkuti, S. R (2021). Optimal Network Reconfiguration with Distributed Generation and Electric Vehicle Charging Stations. International Journal of Mathematical, Engineering and Management Sciences, 6(4), 1174-1185. https://doi.org/10.33889/IJMEMS.2021.6.4.070.

Conflict of Interest

The authors declare that there is no conflict for this publication.

Acknowledgements

This research work was funded by “Woosong University’s Academic Research Funding -2021”.

References

Altun, T., Madani, R., Yadav, A.P., Nasir, A., & Davoudi, A. (2020). Optimal reconfiguration of dc networks. IEEE Transactions on Power Systems, 35(6), 4272-4284.

Amin, A., Tareen, W.U.K., Usman, M., Memon, K.A., Horan, B., Mahmood, A., & Mekhilef, S. (2020). An integrated approach to optimal charging scheduling of electric vehicles integrated with improved medium-voltage network reconfiguration for power loss minimization. Sustainability, 12(21), 1-15.

Asrari, A., Lotfifard, S., & Payam, M.S. (2016). Pareto dominance-based multiobjective optimization method for distribution network reconfiguration. IEEE Transactions on Smart Grid, 7(3), 1401-1410.

Babu, P.V.K., & Swarnasri, K. (2020). Multi-objective optimal allocation of electric vehicle charging stations in radial distribution system using teaching learning based optimization. International Journal of Renewable Energy Research, 10(1), 366-377.

Cui, Z., Bai, X., Li, P., Li, B., Cheng, J., Su, X., & Zheng, Y. (2020). Optimal strategies for distribution network reconfiguration considering uncertain wind power. CSEE Journal of Power and Energy Systems, 6(3), 662-671.

Diaz, P., Pérez-Cisneros, M., Cuevas, E., Avalos, O., Gálvez, J., Hinojosa, S., & Zaldivar, D. (2018). An improved crow search algorithm applied to energy problems. Energies, 11(3), 1-22.

Fu, Y.Y., & Chiang, H.D. (2018). Toward optimal multiperiod network reconfiguration for increasing the hosting capacity of distribution networks. IEEE Transactions on Power Delivery, 33(5), 2294-2304.

Gangwar, P., Mallick, A., Chakrabarti, S., & Singh, S.N. (2020). Short-term forecasting-based network reconfiguration for unbalanced distribution systems with distributed generators. IEEE Transactions on Industrial Informatics, 16(7), 4378-4389.

Huang, S., Wu, Q., Cheng, L., & Liu, Z. (2016). Optimal reconfiguration-based dynamic tariff for congestion management and line loss reduction in distribution networks. IEEE Transactions on Smart Grid, 7(3), 1295-1303.

Huang, Z., Fang, B., & Deng, J. (2020). Multi-objective optimization strategy for distribution network considering V2G-enabled electric vehicles in building integrated energy system. Protection and Control of Modern Power Systems, 5(7), 1-8.

Hussien, A.G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., & Chen, H. (2020). Crow search algorithm: theory, recent advances, and applications. IEEE Access, 8, 173548-173565.

Jung, C.M., & Salkuti, S.R. (2020). Optimal wind-thermal coordination scheduling considering reserve requirement. International Journal of Mathematical, Engineering and Management Sciences, 5(1), 108-119.

Kamruzzaman, M.D., Benidris, M., Elsaiah, S., & Tian, Y. (2020). A method for maximizing the hosting capacity to electric vehicles using feeder reconfiguration. In 2020 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1-5). IEEE. Montreal, QC, Canada.

Kavousi-Fard, A., Niknam, T., & Fotuhi-Firuzabad, M. (2015). Stochastic reconfiguration and optimal coordination of V2G plug-in electric vehicles considering correlated wind power generation. IEEE Transactions on Sustainable Energy, 6(3), 822-830.

Kavousi-Fard, A., Rostami, M.A., & Niknam, T. (2015). Reliability-oriented reconfiguration of vehicle-to-grid networks. IEEE Transactions on Industrial Informatics, 11(3), 682-691.

Kianmehr, E., Nikkhah, S., Vahidinasab, V., Giaouris, D., & Taylor, P.C. (2019). A resilience-based architecture for joint distributed energy resources allocation and hourly network reconfiguration. IEEE Transactions on Industrial Informatics, 15(10), 5444-5455.

Koutsoukis, N.C., Siagkas, D.O., Georgilakis, P.S., & Hatziargyriou, N.D. (2017). Online reconfiguration of active distribution networks for maximum integration of distributed generation. IEEE Transactions on Automation Science and Engineering, 14(2), 437-448.

Lee, C., Liu, C., Mehrotra, S., & Bie, Z. (2015). Robust distribution network reconfiguration. IEEE Transactions on Smart Grid, 6(2), 836-842.

Li, Z., Jazebi, S., & León, F. (2017). Determination of the optimal switching frequency for distribution system reconfiguration. IEEE Transactions on Power Delivery, 32(4), 2060-2069.

Liu, Y., Li, J., & Wu, L. (2019). Coordinated optimal network reconfiguration and voltage regulator/der control for unbalanced distribution systems. IEEE Transactions on Smart Grid, 10(3), 2912-2922.

Meraihi, Y., Gabis, A.B., Ramdane-Cherif, A., & Acheli, D. (2020). A comprehensive survey of crow search algorithm and its applications. Artificial Intelligence Review, 54, 2669-2716.

Movahediyan, Z., & Askarzadeh, A. (2018). Multi-objective optimization framework of a photovoltaic-diesel generator hybrid energy system considering operating reserve. Sustainable Cities and Society, 41, 1-12.

Mozafar, M.R., Moradi, M.H., & Amini, M.H. (2017). A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm. Sustainable Cities and Society, 32, 627-637.

Naidu, K., Muhammad, M.A., Mokhlis, H., Sufyan, M., & Amin, A. (2019). Optimal coordination of EV charging with network reconfiguration. In 2019 AIP Conference Proceedings (Vol. 2129, No. 1, p. 020094). AIP. Jawa Barat, Indonesia.

Nick, M., Cherkaoui, R., & Paolone, M. (2018). Optimal planning of distributed energy storage systems in active distribution networks embedding grid reconfiguration. IEEE Transactions on Power Systems, 33(2), 1577-1590.

Nobahari, H., & Bighashdel, A. (2017). MOCSA: a multi-objective crow search algorithm for multi-objective optimization. In 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) (pp. 60-65). IEEE. Kerman, Iran.

Pamshetti, V.B., Singh, S., & Singh, S.P. (2020). Combined impact of network reconfiguration and volt-var control devices on energy savings in the presence of distributed generation. IEEE Systems Journal, 14(1), 995-1006.

Peng, Q., Tang, Y., & Low, S.H. (2015). Feeder reconfiguration in distribution networks based on convex relaxation of opf. IEEE Transactions on Power Systems, 30(4), 1793-1804.

Raju, G.K.V., & Bijwe, P.R. (2008). Efficient reconfiguration of balanced and unbalanced distribution systems for loss minimisation. IET Generation, Transmission & Distribution, 2(1), 7-12.

Reddy, S.S., Abhyankar, A.R., & Bijwe. P.R. (2011). Reactive power price clearing using multi-objective optimization. Energy, 36(5), 3579-3589.

Rizk-Allah, R.M., Hassanien, A.E., & Slowik, A. (2020). Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization. Neural Computing and Applications, 32, 13715-13746.

Rostami, M.A., Kavousi-Fard, A., & Niknam, T. (2015). Expected cost minimization of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration. IEEE Transactions on Industrial Informatics, 11(2), 388-397.

Sadeghian, O., Nazari-Heris, M., Abapoue, M., Taheri, S.S., & Zare, K. (2019). Improving reliability of distribution networks using plug-in electric vehicles and demand response. Journal of Modern Power Systems and Clean Energy, 7(5), 1189-1199.

Salkuti, S.R. (2021). Multi-objective based optimal network reconfiguration using crow search algorithm. International Journal of Advanced Computer Science and Applications, 12(3), 86-95.

Samman, M.A., Mokhlis, H., Mansor, N.N., Mohamad, H., Suyono, H., & Sapari, N.M. (2020). Fast optimal network reconfiguration with guided initialization based on a simplified network approach. IEEE Access, 8, 11948-11963.

Wu, H., Dong, P., & Liu, M. (2020). Distribution network reconfiguration for loss reduction and voltage stability with random fuzzy uncertainties of renewable energy generation and load. IEEE Transactions on Industrial Informatics, 16(9), 5655-5666.