Free Access
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S2083 - S2124
Published online 02 March 2021
  • A.Y. Abdelaziz, E.S. Ali and S.M. Abd Elazim, Optimal sizing and locations of capacitors in radial distribution systems via flower pollination optimization algorithm and power loss index. Eng. Sci. Technol. Int. J. 19 (2016) 610–618. [Google Scholar]
  • A.Y. Abdelaziz, E.S. Ali and S.M. Abd Elazim, Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems. Electr. Power Energy Syst. 78 (2016) 207–214. [Google Scholar]
  • H. Arasteh, M.S. Sepasian and V. Vahidinasab, An aggregated model for coordinated planning and reconfiguration of electric distribution networks. Energy 94 (2016) 786e798. [Google Scholar]
  • A. Azizivahed, H. Narimani, E. Naderi, M. Fathi and M.R. Narimani, A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration. Energy 138 (2017) 355–373. [Google Scholar]
  • A. Bayat, Uniform voltage distribution based constructive algorithm for optimal reconfiguration of electric distribution networks. Electr. Power Syst. Res. 104 (2013) 146–155. [Google Scholar]
  • A. Bayat, A. Bagheri and R. Noroozian, Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method. Electr. Power Energy Syst. 77 (2016) 360–371. [Google Scholar]
  • R. Čadenović, D. Jakus, P. Sarajčev and J. Vasilj, Optimal distribution network reconfiguration through integration of cycle-break and genetic algorithms. Energies 11 (2018) 1278. [Google Scholar]
  • W. Cao, J. Wu, N. Jenkins, C. Wang and T. Green, Operating principle of Soft Open Points for electrical distribution network operation. Appl. Energy 164 (2016) 245–257. [Google Scholar]
  • W. Cao, J. Wu, N. Jenkins, C. Wang and T. Green, Benefits analysis of Soft Open Points for electrical distribution network operation. Appl. Energy 165 (2016) 36–47. [Google Scholar]
  • M. Cavlovic, Challenges of optimizing the integration of distributed generation into the distribution network. In: 2011 8th International Conference on the European Energy Market (EEM). IEEE (2011) 419–426. [Google Scholar]
  • G. Chicco and A. Mazza, Assessment of optimal distribution network reconfiguration results using stochastic dominance concepts. Sustainable Energy Netw. Netw. 9 (2017) 75–79. [Google Scholar]
  • H. de Faria, M.G.C. Resende and D. Ernst, A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem. J. Heuristics 23 (2017) 533–550. [Google Scholar]
  • E.J. de Oliveira, G.J. Rosseti, L.W. de Oliveira, F.V. Gomes and W. Peres, New algorithm for reconfiguration and operating procedures in electric distribution systems. Electr. Power Energy Syst. 57 (2014) 129–134. [Google Scholar]
  • K.R. Devabalaji and K. Ravi, Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm. Ain Shams Eng. J. 7 (2016) 959–971. [Google Scholar]
  • D.-L. Duan, X.-D. Ling, X.-Y. Wu and B. Zhong, Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm. Int J. Electr. Power Energy Syst. 64 (2015) 88–89. [Google Scholar]
  • A. Escalera, B. Hayes and M. Prodanović, A survey of reliability assessment techniques for modern distribution networks. Renew. Sustainable Energy Rev. 91 (2018) 344–357. [Google Scholar]
  • M. Esmaeili, M. Sedighizadeh and M. Esmaili, Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty. Energy 103 (2016) 86e99. [Google Scholar]
  • H. Fathabadi, Power distribution network reconfiguration for power loss minimization using novel dynamic fuzzy c-means (dFCM) clustering based ANN approach. Electr. Power Energy Syst. 78 (2016) 96–107. [Google Scholar]
  • A. Fathy, M. El-Arini and O. El-Baksawy, An efficient methodology for optimal reconfiguration of electric distribution network considering reliability indices via binary particle swarm gravity search algorithm. Neural Comput. Appl. 30 (2017) 2843–2858. [Google Scholar]
  • S. Ganesh and R. Kanimozhi, Meta-heuristic technique for network reconfiguration in distribution system with photovoltaic and D-STATCOM. IET Gener. Transm. Distrib. 12 (2018) 4524–4535. [Google Scholar]
  • S. Ghasemi, Balanced and unbalanced distribution networks reconfiguration considering reliability indices. Ain Shams Eng. J. 9 (2018) 1567–1579. [Google Scholar]
  • S. Ghasemi and J. Moshtagh, A novel codification and modified heuristic approaches for optimal reconfiguration of distribution networks considering losses cost and cost benefit from voltage profile improvement. Appl. Soft Comput. 25 (2014) 360–368. [Google Scholar]
  • F. Glover and G.A. Kochenberger, Handbook of Metaheuristics. Kluwer Academic Publihers (2003). [Google Scholar]
  • N. Gupta, A. Swarnkar and K.R. Niazi, Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms. Electr. Power Energy Syst. 54 (2014) 664–671. [Google Scholar]
  • H. Hamour, S. Kamel, L. Nasrat and J. Yu, Distribution network reconfiguration using augmented grey wolf optimization algorithm for power loss minimization. In: International Conference on Innovative Trends in Computer Engineering (ITCE– 2019), Aswan, Egypt, 2–4 February. IEEE (2019). [Google Scholar]
  • Q. Hao, Z. Gao, X. Bai and M. Cao, Two-level reconfiguration algorithm of branch exchange and variable neighborhood search for active distribution network. Syst. Sci. Control Eng.: Open Access J. 6 (2018) 109–117. [Google Scholar]
  • A.M. Imran and M. Kowsalya, A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm. Electr. Power Energy Syst. 62 (2014) 312–322. [Google Scholar]
  • F. Iqbal, M. Tauseef Khan and A. Shahzad Siddiqui, Optimal placement of DG and DSTATCOM for loss reduction and voltage profile improvement. Alexandria Eng. J. 57 (2018) 755–765. [Google Scholar]
  • H. Ji, P. Li, C. Wang, G. Song, J. Zhao, H. Su and J. Wu, A strengthened SOCP-based approach for evaluating the distributed generation hosting capacity with soft open points. Energy Proc. 142 (2017) 1947–1952. [Google Scholar]
  • H. Ji, C. Wang, P. Li, J. Zhao, G. Song, F. Ding and J. Wu, An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks. Applied Energy 208 (2017) 986–995. [Google Scholar]
  • H. Ji, C. Wang, P. Li, J. Zhao, G. Song and J. Wu, Quantified flexibility evaluation of soft open points to improve distributed generator penetration in active distribution networks based on difference-of-convex programming. Appl. Energy 218 (2018) 338–348. [Google Scholar]
  • A.R. Jordehi, Optimisation of electric distribution systems: a review. Renew. Sustainable Energy Rev. 51 (2015) 1088–1100. [Google Scholar]
  • N. Kanwar, N. Gupta, K.R. Niazi, A. Swarnkar, R.C. Bansal, Application of TLBO for distribution network planning via coordination of distributed generation and network reconfiguration. IFAC-PaperOnLine 48 (2015) 025–030. [Google Scholar]
  • N. Kanwar, N. Gupta, K.R. Niazi and A. Swarnkar, An integrated approach for distributed resource allocation and network reconfiguration considering load diversity among customers. Sustainable Energy Netw. Netw. 7 (2016) 37–46. [Google Scholar]
  • M.R. Kaveh, R.-A. Hooshmand and S.M. Madani, Simultaneous optimization of re-phasing, reconfiguration and DG placement in distribution networks using BF-SD algorithm. Appl. Soft Comput. 62 (2018) 1044–1055. [Google Scholar]
  • E. Kazemi-Robati and M.S. Sepasian, Passive harmonic filter planning considering daily load variations and distribution system reconfiguration. Electr. Power Syst. Res. 166 (2019) 125–135. [Google Scholar]
  • E. Kianmehr, S. Nikkhah and A. Rabiee, Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives. Renew. Energy 132 (2019) 471–485. [Google Scholar]
  • G.I. Koong, H. Mokhlis, J.J. Jamian, H.A. Illias, W.M. Dahalan and M.M. Aman, Simultaneous network reconfiguration with distributed generation sizing and tap changer adjustment for power loss reduction using imperialist competitive algorithm. Arab. J. Sci. Eng. 43 (2018) 2779–2792. [Google Scholar]
  • N.V. Kovački, P.M. Vidović and A.T. Sarić, Scalable algorithm for the dynamic reconfiguration of the distribution network using the Lagrange relaxation approach. Electr. Power Energy Syst. 94 (2018) 188–202. [Google Scholar]
  • P. Kumar, I. Ali, M.S. Thomas and S. Singh, Imposing voltage security and network radiality for reconfiguration of distribution systems using efficient heuristic and meta-heuristic approach. IET Gener. Transm. Distrib. 11 (2017) 2457–2467. [Google Scholar]
  • Y. LakshmiReddy, T. Sathiyanarayanan and M. Sydulu, Application of firefly algorithm for radial distribution network reconfiguration using different loads. In: Third International Conference on Advances in Control and Optimization of Dynamical Systems. March 13–15. Kanpur, India (2014). [Google Scholar]
  • L. Li and C. Xuefeng, Distribution network reconfiguration based on niche binary particle swarm optimization algorithm. Energy Proc. 17 (2012) 178–182. [Google Scholar]
  • H. Li, W. Mao, A. Zhang and C. Li, An improved distribution network reconfiguration method based on minimum spanning tree algorithm and heuristic rules. Electr. Power Energy Syst. 82 (2016) 466–473. [Google Scholar]
  • R. Li, W. Wang, Z. Chen, J. Jiang and W. Zhang, A review of optimal planning active distribution system: models, methods, and future researches. Energies 10 (2017) 1715. [Google Scholar]
  • C. Long, J. Wu, L. Thomas and N. Jenkins, Optimal operation of soft open points in medium voltage electrical distribution networks with distributed generation. Appl. Energy 184 (2016) 427–437. [Google Scholar]
  • A. Lotfipour and H. Afrakhte, A discrete teaching-learning-based optimization algorithm to solve distribution system reconfiguration in presence of distributed generation. Electr. Power Energy Syst. 82 (2016) 264–273. [Google Scholar]
  • C. Ma, C. Li, X. Zhang, G. Li and Y. Han, Reconfiguration of distribution networks with distributed generation using a dual hybrid particle swarm optimization algorithm. Hindawi Math. Probl. Eng. 2017 (2017) 1517435. [Google Scholar]
  • K.N. Maya and E.A. Jasmin, A three phase power flow algorithm for distribution network incorporating the impact of distributed generation models. Proc. Technol. 21 (2015) 326–331. [Google Scholar]
  • M. Mohammadi, M. Abasi and A. Mohammadi Rozbahani, Fuzzy-GA based algorithm for optimal placement and sizing of distribution static compensator (DSTATCOM) for loss reduction of distribution network considering reconfiguration. J. Cent. South Univ. 24 (2017) 245–258. [Google Scholar]
  • H. Mori and H. Yokoyama, A hybrid intelligent method for estimating distribution network reconfigurations. IFAC-PapersOnLine 49–27 (2016) 152–157. [Google Scholar]
  • N.F. Napis, A.F. Abd Kadir, T. Khatib, E.E. Hassan and M.F. Sulaima, An improved method for reconfiguring and optimizing electrical active distribution network using evolutionary particle swarm optimization. Appl. Sci. 8 (2018) 804. [Google Scholar]
  • D. Nataraj, R. Loganathan, M. Veerasamy and V. Jawalkar, Optimizing radial distribution system for minimizing loss reduction and voltage deviation indices using modified grey wolf’s algorithm. Int. J. Intell. Eng. Syst. 11 (2018) 177–189. [Google Scholar]
  • T.T. Nguyen, A.V. Truong and T.A. Phung, A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. Electr. Power Energy Syst. 78 (2016) 801–815. [Google Scholar]
  • T.T. Nguyen, T.T. Nguyen, A.V. Truong, Q.T. Nguyen and T.A. Phung, Multi-objective electric distribution network reconfiguration solution using runner-root algorithm. Appl. Soft Comput. 52 (2017) 93–108. [Google Scholar]
  • A. Onlam, D. Yodphet, R. Chatthaworn, C. Surawanitkun, A. Siritaratiwat and P. Khunkitti. Power loss minimization and voltage stability improvement in electrical distribution system via network reconfiguration and distributed generation placement using novel adaptive shuffled frogs leaping algorithm. Energies 12 (2019) 553. [Google Scholar]
  • R. Pegado, Z. Ñaupari, Y. Molina and C. Castillo, Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO. Electr. Power Syst. Res. 169 (2019) 206–213. [Google Scholar]
  • L.L. Pfitscher, D.P. Bernardon, L.N. Canha, V.F. Montagner, V.J. Garcia and A.R. Abaide, Intelligent system for automatic reconfiguration of distribution network in real time. Electr. Power Syst. Res. 97 (2013) 84–92. [Google Scholar]
  • Q. Qi and J. Wu, Increasing distributed generation penetration using network reconfiguration and soft open points. Energy Proc. 105 (2017) 2169–2174. [Google Scholar]
  • Q. Qi, J. Wu, L. Zhang and M. Cheng, Multi-objective optimization of electrical distribution. In: Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid; 19–21 April. Maldives (2016) [Google Scholar]
  • A. Ram Gupta and A. Kumar, Energy savings using D-STATCOM placement in radial distribution system. Proc. Comput. Sci. 70 (2015) 558–564. [Google Scholar]
  • R. Rajaram, K. Sathish Kumar and N. Rajasekar, Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG). Energy Rep. 1 (2015) 116–122. [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. Electr. Power Energy Syst. 73 (2015) 932–942. [Google Scholar]
  • P.D.P. Reddy, V.C.V. Reddy, T.G. Manohar, Application of flower pollination algorithm for optimal placement and sizing of distributed generation in distribution systems. J. Electr. Syst. Inf. Technol. 3 (2016) 14–22. [Google Scholar]
  • A.V.S. Reddy, M.D. Reddy, M.S.K. Reddy, Network reconfiguration of distribution system for loss reduction using Gwo algorithm. Int. J. Electr. Computer Eng. 7 (2017) 34–39. [Google Scholar]
  • P.D.P. Reddy, V.C.V. Reddy and T.G. Manohar, Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms. J. Electr. Syst. Inf. Technol. 5 (2018) 175–191. [Google Scholar]
  • S. Rezaeian Marjani, V. Talavat and S. Galvani, Optimal allocation of D-STATCOM and reconfiguration in radial distribution network using MOPSO algorithm in TOPSIS framework. Int. Trans. Electr. Energy Syst. 29 (2019) e2723. [Google Scholar]
  • G.J.S. Rosseti, E.J. de Oliveira, L.W. de Oliveira, I.C. Silva and W. Peres, Optimal allocation of distributed generation with reconfiguration in electric distribution systems. Electr. Power Syst. Res. 103 (2013) 178–183. [Google Scholar]
  • J.L. Rueda, R. Loor and I. Erlich, MVMO for optimal reconfiguration in smart distribution systems. IFAC-PaperOnLine 48 (2015) 276–281. [Google Scholar]
  • M. Sedighizadeh, M. Esmaili and M. Esmaeili, Application of the hybrid Big Bang–Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems. Energy 76 (2014) 920–930. [CrossRef] [Google Scholar]
  • R. Sirjani and A.R. Jordehi, Optimal placement and sizing of distribution static compensator (D-STATCOM) in electric distribution networks: a review. Renew. Sustainable Energy Rev. 77 (2017) 688–694. [Google Scholar]
  • S.S.F. Souza, R. Romero and J.F. Franco, Artificial immune networks Copt-aiNet and Opt-aiNet applied to the reconfiguration problem of radial electrical distribution systems. Electr. Power Syst. Res. 119 (2015) 304–312. [Google Scholar]
  • S.S.F. Souza, R. Romero, J. Pereira and J.T. Saraiva, Artificial immune algorithm applied to distribution system reconfiguration with variable demand. Electr. Power Energy Syst. 82 (2016) 561–568. [Google Scholar]
  • M. Subramaniyan, S. Subramaniyan, M. Veeraswamy and V.R. Jawalkar, Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization. COMPEL – Int. J. Comput. Math. Electr. Electron. Eng. 38 (2019) 247–262. [Google Scholar]
  • B. Sultana, M.W. Mustafa, U. Sultana and A.R. Bhatti, Review on reliability improvement and power loss reduction in distribution system via network reconfiguration. Renew. Sustainable Energy Rev. 66 (2016) 297–310. [Google Scholar]
  • S.A. Taher and S.A. Afsari, Optimal location and sizing of DSTATCOM in distribution systems by immune algorithm. Electr. Power Energy Syst. 60 (2014) 34–44. [Google Scholar]
  • Y. Takenobu, N. Yasuda, S. Minatoc and Y. Hayashi, Scalable enumeration approach for maximizing hosting capacity of distributed generation. Electr. Power Energy Syst. 105 (2019) 867. [Google Scholar]
  • E.-G. Talbi, Metaheuristics: From Design to Implementation. A John Wiley & Sons, Inc., Publication (2009). [Google Scholar]
  • S. Teimourzadeh and K. Zare, Application of binary group search optimization to distribution network reconfiguration. Electr. Power Energy Syst. 62 (2014) 461–468. [Google Scholar]
  • Y. Thangaraj and R. Kuppan, Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm. J. Appl. Res. Technol. 15 (2017) 477–491. [Google Scholar]
  • H.B. Tolabi, M.H. Ali, S.B.M. Ayob and M. Rizwan, Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation. Energy 71 (2014) 507–515. [Google Scholar]
  • A.V. Truong, T.N. Ton, T.T. Nguyen and T.L. Duong, Two states for optimal position and capacity of distributed generators considering network reconfiguration for power loss minimization based on runner root algorithm. Energy 12 (2018) 106. [Google Scholar]
  • H.K. Verma and P. Singh, Optimal reconfiguration of distribution network using modified culture algorithm. J. Inst. Eng. India Ser. B. 99 (2018) 613–622. [Google Scholar]
  • P. Vijay Babu and S.P. Singh, Optimal placement of DG in distribution network for power loss minimization using NLP & PLS technique. Energy Proc. 90 (2016) 441–454. [Google Scholar]
  • C. Wang, G. Song, P. Li, H. Ji, J. Zhao and J. Wu, Optimal configuration of soft open point for active distribution network based on mixed-integer second-order cone programming. Energy Proc. 103 (2016) 70–75. [Google Scholar]
  • C. Wang, G. Song, P. Li, H. Ji, J. Zhao and J. Wu, Optimal siting and sizing of soft open points in active electrical distribution networks. Appl. Energy 189 (2017) 301–309. [Google Scholar]
  • T. Yuvaraj, K.R. Devabalaji and K. Ravi, Optimal placement and sizing of DSTATCOM using Harmony search algorithm. Energy Proc. 79 (2015) 759–765. [Google Scholar]
  • T. Yuvaraj, K. Ravi and K.R. Devabalaji, DSTATCOM allocation in distribution networks considering load variations using bat algorithm. Ain Shams Eng. J. 8 (2017) 391–403. [Google Scholar]
  • H.F. Zhai, M. Yang, B. Chen and N. Kang, Dynamic reconfiguration of three-phase unbalanced distribution networks. Electr. Power Energy Syst. 99 (2018) 1–10. [Google Scholar]
  • L. Zhang, C. Shen, Y. Chen, S. Huang and W. Tang, Coordinated allocation of distributed generation, capacitor banks and soft open points in active distribution networks considering dispatching results. Appl. Energy 231 (2018) 1122–1131. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.