1. [1] Azizivahed, A., et al. New energy management approach in distribution systems considering energy storages. in 2017 20th International Conference on Electrical Machines and Systems (ICEMS). 2017. IEEE. [ DOI:10.1109/ICEMS.2017.8056133] 2. [2] Narimani, M.R., et al., Enhanced gravitational search algorithm for multi-objective distribution feeder reconfiguration considering reliability, loss and operational cost. IET Generation, Transmission & Distribution, 2014. 8(1): p. 55-69. [ DOI:10.1049/iet-gtd.2013.0117] 3. [3] Azizivahed, A., et al., A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration. Energy, 2017. 138: p. 355-373. [ DOI:10.1016/j.energy.2017.07.102] 4. [4] Abdelaziz, M. Distribution network reconfiguration using a genetic algorithm with varying population size. Elec Power Syst Res, 2017. 142: p.9-11. [ DOI:10.1016/j.epsr.2016.08.026] 5. [5] Guan, W., Tan, Y., Zhang, H., Song, J. Distribution system feeder reconfguration considering different model of DG sources. Int J Electr Power Energy Syst, 2015. 68: p.210-221. [ DOI:10.1016/j.ijepes.2014.12.023] 6. [6] T. Lv and Q. Ai, Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources. Applied Energy, 2016.163: p.408-422. [ DOI:10.1016/j.apenergy.2015.10.179] 7. [7] Azizivahed, A., et al., A new bi-objective approach to energy management in distribution networks with energy storage systems. IEEE Transactions on Sustainable Energy, 2018. 9(1): p. 56-64. [ DOI:10.1109/TSTE.2017.2714644] 8. [8] Azizivahed, A., et al., Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems. Energy, 2018. 147: p. 896-914. [ DOI:10.1016/j.energy.2018.01.111] 9. [9] Lotfi, H., R. Ghazi, and M. B. Naghibi-Sistani, Multi-objective dynamic distribution feeder reconfiguration along with capacitor allocation using a new hybrid evolutionary algorithm. Energy Systems, 2019: p. 1-31. [ DOI:10.1007/s12667-019-00333-3] 10. [10] Ameli, A., et al., A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems. International Journal of Electrical Power & Energy Systems, 2017. 85: p. 200-211. [ DOI:10.1016/j.ijepes.2016.09.008] 11. [11] Eberhart, R. and J. Kennedy. A new optimizer using particle swarm theory. in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. 1995. Ieee. 12. [12] Eusuff, M., K. Lansey, and F. Pasha, Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Engineering optimization, 2006. 38(2): p. 129-154. [ DOI:10.1080/03052150500384759] 13. [13] Niknam, T., et al., Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET generation, transmission & distribution, 2012. 6(6): p. 515-527. [ DOI:10.1049/iet-gtd.2011.0851] 14. [14] Enayatifar, R., et al., MOICA: A novel multi-objective approach based on imperialist competitive algorithm. Applied Mathematics and Computation, 2013. 219(17): p. 8829-8841. [ DOI:10.1016/j.amc.2013.03.099] 15. [15] Ahrari, A., M. Shariat-Panahi, and A.A. Atai, GEM: a novel evolutionary optimization method with improved neighborhood search. Applied Mathematics and Computation, 2009. 210(2): p. 376-386. [ DOI:10.1016/j.amc.2009.01.009]
|