Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
-
Thandava Krishna Sai Pandraju
and
Varaprasad Janamala
Abstract
Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it’s improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values.
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Sarantakos, I, Greenwood, DM, Yi, J, Blake, SR, Taylor, PC. A method to include component condition and substation reliability into distribution system reconfiguration. Int J Electr Power Energy Syst 2019;109:122–38. https://doi.org/10.1016/j.ijepes.2019.01.040.Search in Google Scholar
2. Sedighizadeh, M, Bakhtiary, R. 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 2016;7:113–29. https://doi.org/10.1016/j.asej.2015.11.018.Search in Google Scholar
3. Nguyen, TT, Nguyen, TT, Nguyen, NA, Duong, TL. A novel method based on coyote algorithm for simultaneous network reconfiguration and distribution generation placement. Ain Shams Eng J 2020. https://doi.org/10.1016/j.asej.2020.06.005.Search in Google Scholar
4. Salau, AO, Gebru, YW, Bitew, D. Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems. Heliyon 2020;6:e04233. https://doi.org/10.1016/j.heliyon.2020.e04233.Search in Google Scholar PubMed PubMed Central
5. Sarkar, D, Konwar, P, De, A, Goswami, S. A graph theory application for fast and efficient search of optimal radialized distribution network topology. J King Saud Univ-Eng Sci 2020;32:255–64. https://doi.org/10.1016/j.jksues.2019.02.003.Search in Google Scholar
6. Quadri, IA, Bhowmick, S. A hybrid technique for simultaneous network reconfiguration and optimal placement of distributed generation resources. Soft Comput 2019;24:11315–36. https://doi.org/10.1007/s00500-019-04597-w.Search in Google Scholar
7. LakshmiReddy, Y, Sathiyanarayanan, T, Sydulu, M. Application of firefly algorithm for radial distribution network reconfiguration using different loads. IFAC Proc 2014;47:700–5. https://doi.org/10.3182/20140313-3-in-3024.00052.Search in Google Scholar
8. Taher, SA, Hossein Karimi, M. Optimal reconfiguration and DG allocation in balanced and unbalanced distribution systems. Ain Shams Eng J 2014;5:735–49. https://doi.org/10.1016/j.asej.2014.03.009.Search in Google Scholar
9. Ghasemi, S. Balanced and unbalanced distribution networks reconfiguration considering reliability indices. Ain Shams Eng J 2018;9:1567–79. https://doi.org/10.1016/j.asej.2016.11.010.Search in Google Scholar
10. Tran, TT, Truong, KH, Vo, DN. Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations. Ain Shams Eng J 2020;11:389–407. https://doi.org/10.1016/j.asej.2019.08.015.Search in Google Scholar
11. Mahdad, B. Optimal reconfiguration and reactive power planning based fractal search algorithm: a case study of the Algerian distribution electrical system. Eng Sci Technol 2019;22:78–101. https://doi.org/10.1016/j.jestch.2018.08.013.Search in Google Scholar
12. Rajaram, R, Sathish Kumar, K, Rajasekar, N. 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 2015;1:116–22. https://doi.org/10.1016/j.egyr.2015.03.002.Search in Google Scholar
13. Hao, Q, Gao, Z, Bai, X, Cao, M. Two-level reconfiguration algorithm of branch exchange and variable neighbourhood search for active distribution network. Syst Sci Control Eng 2018;6:109–17. https://doi.org/10.1080/21642583.2018.1536898.Search in Google Scholar
14. Kavousi-Fard, A, Niknam, T, Akbari-Zadeh, MR, Dehghan, B. Stochastic framework for reliability enhancement using optimal feeder reconfiguration. J Syst Eng Electron 2014;25:901–10. https://doi.org/10.1109/jsee.2014.00104.Search in Google Scholar
15. Wu, H, Dong, P. PPSO method for distribution network reconfiguration considering the stochastic uncertainty of wind turbine photovoltaic and load. J Eng 2017;2017:2032–6. https://doi.org/10.1049/joe.2017.0686.Search in Google Scholar
16. Muhammad, MA, Mokhlis, H, Naidu, K, Amin, A, Franco, JF, Othman, M. Distribution network planning enhancement via network reconfiguration and DG integration using dataset approach and water cycle algorithm. J Mod Power Syst Clean Energy 2020;8:86–93. https://doi.org/10.35833/mpce.2018.000503.Search in Google Scholar
17. Kanwar, N, Gupta, N, Niazi, KR, Swarnkar, A. Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns. J Mod Power Syst Clean Energy 2015;3:475–84. https://doi.org/10.1007/s40565-015-0169-0.Search in Google Scholar
18. Wang, J, Wang, W, Yuan, Z, Wang, H, Wu, J. A chaos disturbed beetle antennae search algorithm for a multiobjective distribution network reconfiguration considering the variation of load and DG. IEEE Access 2020;8:97392–407. https://doi.org/10.1109/access.2020.2997378.Search in Google Scholar
19. Badran, O, Mokhlis, H, Mekhilef, S, Dahalan, W. Multi-objective network reconfiguration with optimal DG output using meta-heuristic search algorithms. Arabian J Sci Eng 2017;43:2673–86. https://doi.org/10.1007/s13369-017-2714-9.Search in Google Scholar
20. Dogan, A, Alci, M. Simultaneous optimization of network reconfiguration and DG installation using heuristic algorithms. Elektron Ir Elektrotech 2019;25. https://doi.org/10.5755/j01.eie.25.1.22729.Search in Google Scholar
21. Quoc, SN, Vo Ngoc, D. Symbiotic organism search algorithm for power loss minimization in radial distribution systems by network reconfiguration and distributed generation placement. Math Probl Eng 2020;2020:1–22. https://doi.org/10.1155/2020/1615792.Search in Google Scholar
22. Bagheri, A, Bagheri, M, Lorestani, A. Optimal reconfiguration and DG integration in distribution networks considering switching actions costs using tabu search algorithm. J Ambient Intell Humanized Comput 2020. https://doi.org/10.1007/s12652-020-02511-z.Search in Google Scholar
23. El-salam, A, Fouad, M, Beshr, E, Eteiba, MB. A new hybrid technique for minimizing power losses in a distribution system by optimal sizing and siting of distributed generators with network reconfiguration. Energies 2018;11:3351. https://doi.org/10.3390/en11123351.Search in Google Scholar
24. Onlam, A, Chatthaworn, D, Yodphet, R, Surawanitkun, C, Siritaratiwat, A, Khunkitti, P. 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 2019;12:553. https://doi.org/10.3390/en12030553.Search in Google Scholar
25. Diaaeldin, I, Abdel Aleem, S, El-Rafei, A, Abdelaziz, A, Zobaa, AF. Optimal network reconfiguration in active distribution networks with soft open points and distributed generation. Energies 2019;12:4172. https://doi.org/10.3390/en12214172.Search in Google Scholar
26. Koong, GI, Mokhlis, H, Jamian, JJ, Illias, HA, Dahalan, WM, Aman, MM. Simultaneous network reconfiguration with distributed generation sizing and tap changer adjustment for power loss reduction using imperialist competitive algorithm. Arab J Sci Eng 2017;43:2779–92. https://doi.org/10.1007/s13369-017-2778-6.Search in Google Scholar
27. Mohammadi, M, Mohammadi Rozbahani, A, Bahmanyar, S. Power loss reduction of distribution systems using BFO based optimal reconfiguration along with DG and shunt capacitor placement simultaneously in fuzzy framework. J Cent South Univ 2017;24:90–103. https://doi.org/10.1007/s11771-017-3412-1.Search in Google Scholar
28. Raut, U, Mishra, S. Enhanced SineCosine algorithm for optimal planning of distribution network by incorporating network reconfiguration and distributed generation. Arab J Sci Eng 2020. https://doi.org/10.1007/s13369-020-04808-9.Search in Google Scholar
29. Lotfi, H, Shojaei, AA, Kouhdaragh, V, Amiri, IS. The impact of feeder reconfiguration on automated distribution network with respect to resilience concept. SN Appl Sci 2020;2. https://doi.org/10.1007/s42452-020-03429-z.Search in Google Scholar
30. Jordehi, AR. Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems. Soft Comput 2020;24:18573–90. https://doi.org/10.1007/s00500-020-05093-2.Search in Google Scholar
31. Abu-Elanien, Ahmed, EB, Salama, MMA, Shaban, KB. Modern network reconfiguration techniques for service restoration in distribution systems: a step to a smarter grid. Alexandria Eng J 2018;57:3959–67. https://doi.org/10.1016/j.aej.2018.03.011.Search in Google Scholar
32. Wolpert, DH, Macready, WG. No free lunch theorems for optimization. IEEE Trans Evol Comput 1997;1:67–82. https://doi.org/10.1109/4235.585893.Search in Google Scholar
33. Arora, S, Singh, S. Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 2018;23:715–34. https://doi.org/10.1007/s00500-018-3102-4.Search in Google Scholar
34. Satyanarayana, S, Ramana, T, Sivanagaraju, S, Rao, GK. An efficient load flow solution for radial distribution network including voltage dependent load models. Electr Power Component Syst 2007;35:539–51. https://doi.org/10.1080/15325000601078179.Search in Google Scholar
35. Janamala, V, Pandraju, TKS. Static voltage stability of reconfigurable radial distribution system considering voltage dependent load models. Math Modell Eng Probl 2020;7:450–8. https://doi.org/10.18280/mmep.070316.Search in Google Scholar
36. Fan, Y, Shao, J, Sun, G, Shao, X. A self-adaption butterfly optimization algorithm for numerical optimization problems. IEEE Access 2020;8:88026–41. https://doi.org/10.1109/access.2020.2993148.Search in Google Scholar
37. Zimmerman, RD, Murillo-Sánchez, CE, Thomas, RJ. MATPOWER: steady-state operations planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 2011;26:12–9. https://doi.org/10.1109/tpwrs.2010.2051168.Search in Google Scholar
38. Mwasilu, F, Justo, JJ, Kim, EK, Do, TD, Jung, JW. Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration. Renew Sustain Energy Rev 2014;34:501–16. https://doi.org/10.1016/j.rser.2014.03.031.Search in Google Scholar
39. Kennedy, J, Russell, E. Particle swarm optimization. Neural Netw. https://doi.org/10.1109/icnn.1995.488968.Search in Google Scholar
40. Rao, RV, Savsani, VJ, Vakharia, DP. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 2011;43:303–15. https://doi.org/10.1016/j.cad.2010.12.015.Search in Google Scholar
41. Yang, X-S, Deb, S. Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 2010;1:330–43. https://doi.org/10.1504/IJMMNO.2010.03543.Search in Google Scholar
42. Yang, XS. Flower pollination algorithm for global optimization. In: Durand-Lose, J, Jonoska, N, editors. Unconventional computation and natural computation. UCNC 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2012.10.1007/978-3-642-32894-7_27Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance
- Optimal placement of distributed generation based on DISCO’s additional benefit using self adaptive levy flight based black widow optimization
- A novel swarm intelligence assisted Euclidean distance based single detection index approach in transmission line relaying for fast decision making
- Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
- Multi-objective optimal planning of fast charging stations by considering various load models in distribution system
- AC/DC robust controller technique for reliable operation of photovoltaic-based microgrid using firefly algorithm and fuzzy logic
- Preventing third zone maloperation during power system stressed conditions
- Fuzzy logic based dynamic performance enhancement of five phase induction motor under arbitrary open phase fault for electric vehicle
- Switched capacitor based single DC source boost multilevel inverter (S2-MLI) featuring isolation based soft charging with minimum device count
- Coordinated control among PSS, WTG and BESS for improving Small-Signal Stability
Articles in the same Issue
- Frontmatter
- Research Articles
- Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance
- Optimal placement of distributed generation based on DISCO’s additional benefit using self adaptive levy flight based black widow optimization
- A novel swarm intelligence assisted Euclidean distance based single detection index approach in transmission line relaying for fast decision making
- Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
- Multi-objective optimal planning of fast charging stations by considering various load models in distribution system
- AC/DC robust controller technique for reliable operation of photovoltaic-based microgrid using firefly algorithm and fuzzy logic
- Preventing third zone maloperation during power system stressed conditions
- Fuzzy logic based dynamic performance enhancement of five phase induction motor under arbitrary open phase fault for electric vehicle
- Switched capacitor based single DC source boost multilevel inverter (S2-MLI) featuring isolation based soft charging with minimum device count
- Coordinated control among PSS, WTG and BESS for improving Small-Signal Stability
