Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

References

• A. Azizivahed, H. Lotfi, M. J. Ghadi, S. Ghavidel, L. Li and J. Zhang, “Dynamic feeder reconfiguration in automated distribution network integrated with renewable energy sources with respect to the economic aspect,” in 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 2019. IEEE, pp. 2666–2671.
   Google Scholar
• H. Lotfi, “Multi-Objective Network Reconfiguration and Allocation of Capacitor Units in Radial Distribution System Using an Enhanced Artificial Bee Colony Optimization,” Electric Power Compon. Syst., vol. 49, no. 13–14, pp. 1130–1142, 2022.
   Web of Science ®Google Scholar
• D. S. Rani, N. Subrahmanyam and M. Sydulu, “Multi-objective invasive weed optimization–an application to optimal network reconfiguration in radial distribution systems,” Int. J. Elect. Power Energy Syst., vol. 73, pp. 932–942, 2015. DOI: 10.1016/j.ijepes.2015.06.020.
   Web of Science ®Google Scholar
• F. Alonso, D. Oliveira and A. Z. de Souza, “Artificial immune systems optimization approach for multiobjective distribution system reconfiguration,” IEEE Trans. Power Syst., vol. 30, no. 2, pp. 840–847, 2015. DOI: 10.1109/TPWRS.2014.2330628.
   Web of Science ®Google Scholar
• V. Roberge, M. Tarbouchi and F. Okou, “New encoding based on the minimum spanning tree for distribution feeder reconfiguration using a genetic algorithm,” 2016 International Conference on Electrical and Information Technologies (ICEIT), IEEE, pp. 16–21, 2016. DOI: 10.1109/EITech.2016.7519574.
   Google Scholar
• A. Parizad, H. Baghaee, A. Yazdani and G. Gharehpetian, “Optimal distribution systems reconfiguration for short circuit level reduction using PSO algorithm,” 2018 IEEE Power and Energy Conference at Illinois (PECI), IEEE, pp. 1–6, 2018. DOI: 10.1109/PECI.2018.8334976.
   Google Scholar
• A. Landeros, S. Koziel and M. F. Abdel-Fattah, “Distribution network reconfiguration using feasibility-preserving evolutionary optimization,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 3, pp. 589–598, 2019. DOI: 10.1007/s40565-018-0480-7.
   Web of Science ®Google Scholar
• R. Pegado, Z. Ñaupari, Y. Molina and C. Castillo, “Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO,” Electric Power Syst. Res., vol. 169, pp. 206–213, 2019. DOI: 10.1016/j.epsr.2018.12.030.
   Web of Science ®Google Scholar
• J. Olamaei, T. Niknam and S. B. Arefi, “Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm,” Energy Procedia, vol. 14, pp. 304–311, 2012. DOI: 10.1016/j.egypro.2011.12.934.
   Google Scholar
• M. R. Narimani, A. A. Vahed, R. Azizipanah-Abarghooee and M. Javidsharifi, “Enhanced gravitational search algorithm for multi-objective distribution feeder reconfiguration considering reliability, loss and operational cost,” IET Gener. Transm. Distrib., vol. 8, no. 1, pp. 55–69, 2014. DOI: 10.1049/iet-gtd.2013.0117.
   Web of Science ®Google Scholar
• A. Roosta, H.-R. Eskandari and M.-H. Khooban, “Optimization of radial unbalanced distribution networks in the presence of distribution generation units by network reconfiguration using harmony search algorithm,” Neural Comput. Appl., vol. 31, no. 11, pp. 7095–7109, 2019. DOI: 10.1007/s00521-018-3507-0.
   Web of Science ®Google Scholar
• E. Mahboubi-Moghaddam, M. R. Narimani, M. H. Khooban, A. Azizivahed and M. Javid Sharifi, “Multi-objective distribution feeder reconfiguration to improve transient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations,” Int. J. Elect. Power Energy Syst., vol. 76, pp. 35–43, 2016. DOI: 10.1016/j.ijepes.2015.09.007.
   Web of Science ®Google Scholar
• J. Siahbalaee, N. Rezanejad and G. B. Gharehpetian, “Reconfiguration and DG Sizing and Placement Using Improved Shuffled Frog Leaping Algorithm,” Electric Power Compon. Syst., vol. 47, no. 16-17, pp. 1475–1488, 2020. DOI: 10.1080/15325008.2019.1689449.
   Web of Science ®Google Scholar
• V. Fathi, H. Seyedi and B. M. Ivatloo, “Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties,” Int Trans. Electr. Energ. Syst., vol. 30, no. 5, pp. e12346, 2020. DOI: 10.1002/2050-7038.12346.
   Web of Science ®Google Scholar
• M. Sedighizadeh, M. Dakhem, M. Sarvi and H. H. Kordkheili, “Optimal reconfiguration and capacitor placement for power loss reduction of distribution system using improved binary particle swarm optimization,” Int. J. Energy Environ. Eng., vol. 5, no. 1, pp. 3, 2014. DOI: 10.1186/2251-6832-5-3.
   Google Scholar
• M. R. Babu, C. V. Kumar and S. Anitha, “Simultaneous reconfiguration and optimal capacitor placement using adaptive whale optimization algorithm for radial distribution system,” J. Electr. Eng. Technol., vol. 16, no. 1, pp. 181–190, 2021. DOI: 10.1007/s42835-020-00593-5.
   Web of Science ®Google Scholar
• A. N. Hussain, W. K. Shakir Al-Jubori and H. F. Kadom, “Hybrid design of optimal capacitor placement and reconfiguration for performance improvement in a radial distribution system,” J. Eng., vol. 2019, pp. 1–15, 2019. DOI: 10.1155/2019/1696347.
   Web of Science ®Google Scholar
• R. E. Ramli, M. Awad and R. A. Jabr, “Ordinal optimization for optimal Capacitor Placement and network reconfiguration in radial distribution networks,” 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 1712–1717, 2012.
   Google Scholar
• M. Sedighizadeh and R. Bakhtiary, “Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the Hybrid Big Bang–Big Crunch algorithm in the fuzzy framework,” Ain Shams Eng. J., vol. 7, no. 1, pp. 113–129, 2016. DOI: 10.1016/j.asej.2015.11.018.
   Web of Science ®Google Scholar
• H. Lotfi, “Multi‐objective energy management approach in distribution grid integrated with energy storage units considering the demand response program,” Int. J. Energy Res., vol. 44, no. 13, pp. 10662–10681, 2020. DOI: 10.1002/er.5709.
   Web of Science ®Google Scholar
• H. Lotfi and R. Ghazi, “Optimal participation of demand response aggregators in reconfigurable distribution system considering photovoltaic and storage units,” J. Ambient Intell. Human Comput., vol. 12, no. 2, pp. 2233–2255, 2021. DOI: 10.1007/s12652-020-02322-2.
   Web of Science ®Google Scholar
• H. Lotfi, R. Ghazi and M. Bagher Naghibi-Sistani, “Multi-objective dynamic distribution feeder reconfiguration along with capacitor allocation using a new hybrid evolutionary algorithm,” Energy Syst., vol. 11, no. 3, pp. 779–809, 2020. DOI: 10.1007/s12667-019-00333-3.
   Google Scholar
• M. Abdelaziz, “Distribution network reconfiguration using a genetic algorithm with varying population size,” Electric Power Syst. Res., vol. 142, pp. 9–11, 2017. DOI: 10.1016/j.epsr.2016.08.026.
   Web of Science ®Google Scholar
• E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, “GSA: a gravitational search algorithm,” Inform. sci., vol. 179, no. 13, pp. 2232–2248, 2009. DOI: 10.1016/j.ins.2009.03.004.
   Web of Science ®Google Scholar
• J. G. Digalakis and K. G. Margaritis, “On benchmarking functions for genetic algorithms,” Int. J. Comput. Math., vol. 77, no. 4, pp. 481–506, 2001. DOI: 10.1080/00207160108805080.
   Web of Science ®Google Scholar
• H. Lotfi, R. Ghazi and M. B. Naghibi Sistani, “Multi-Objective Distribution Feeder Reconfiguration Considering Reliability in the Presence of Distributed Generators,” Electric Power Compon. Syst., vol. 50, no. 8, pp. 426–442, 2022. DOI: 10.1080/15325008.2022.2134509.
   Web of Science ®Google Scholar
• T. Niknam, “An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective distribution feeder reconfiguration,” Energy Conver. Manage., vol. 50, no. 8, pp. 2074–2082, 2009. DOI: 10.1016/j.enconman.2009.03.029.
   Web of Science ®Google Scholar
• J.-P. Chiou, C.-F. Chang and C.-T. Su, “Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems,” IEEE Trans. Power Syst., vol. 20, no. 2, pp. 668–674, 2005. DOI: 10.1109/TPWRS.2005.846096.
   Web of Science ®Google Scholar