Document Type : Reseach Article
10.57647/j.mjee.2024.1802.28
Abstract
Distribution systems pose a significant challenge within the power grid, primarily due to their high current, low voltage, and comparatively high ohmic resistance compared to transmission and sub-transmission systems.This results in substantial power losses, necessitating the need for effective mitigation strategies.To address this issue, a wide range of methods and algorithms have been proposed and continuously developed. Over the past half-century, reconfiguring the distribution network has emerged as a cost-effective and straightforward approach to reducing distribution losses. Distribution system reconfiguration has been extensively studied, with each study aiming to achieve distinct objectives. Additionally, numerous studies have explored the dynamics of distribution system reconfiguration, evaluating and comparing various approaches. This study comprehensively assesses both static and dynamic methods of reconfiguring distribution systems and introduces a novel dynamic reconfiguration technique. Unlike traditional methods that rely on real-time or hourly load models, this approach utilizes a load model to address the dynamic reconfiguration problem. Simulations were conducted on the well-established IEEE 33-bus test system, employing MATLAB software in conjunction with a genetic algorithm to minimize losses and optimize voltage profiles. Based on the simulation results, this novel dynamic reconfiguration method demonstrated superior performance compared to previously employed methods. It effectively reduced power losses and enhanced the voltage profile, demonstrating its potential for improving the overall efficiency of distribution systems.
Keywords
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L. Li, J. Zhang, and J. P. S. Catalao. “ ˜ Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage.”. IEEE Trans. Sustain. Energy,
11(2):662–673, Apr. 2020.
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on deep learning algorithm for distribution network.”. Electric. Power Syst. Res., 195:107132, June.
2021.
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Symbolization of Time Series of Load.”. 2019 IEEE
3rd International Electrical and Energy Conference
(CIEEC), Beijing, China, :1001–1006, 2019. DOI:
https://doi.org/10.1109/CIEEC47146.2019.CIEEC2019377.
and N. D. Hatziargyriou. “Online reconfiguration of
active distribution networks for maximum integration of distributed generation.”. IEEE Trans. Autom.
Sci. Eng., 14(2):pp. 437–448, 2017.
[2] P.Kayal and C.K.Chanda. “A simple and fast approach for allocation and size evaluation of distributed generation.”. Int. J. Energy Environ. Eng., 4
(7), 2013.
[3] S. Lal and A. Laturi. “Techno-economic analysis of
a hybrid mini-grid system for Fiji islands.”. Int. J.
Energy. Environ. Eng., 3(10), 2012.
[4] S.M. Islam. “Increasing wind energy penetration
level using pumped hydro storage in island microgrid system.”. Int. J. Energy. Environ. Eng., 3(9),
2012.
[5] A. Merlin and H. Back. “Search for a minimal-loss
operating spanning tree configuration in an urban
power distribution system.”. Proc. 5th Power Syst.
Comput. Conf., London, U.K., pages pp. 1–18, 1975.
[6] G. Chang, J. Zrida, and J. D. Birdwell. “Knowledgebased distribution system analysis and reconfiguration.”. IEEE Trans. Power Syst., 5(3):pp. 744–749,
1990.
[7] Y.-Y. Fu and H.-D. Chiang. “Toward optimal multiperiod network recon-configuration for increasing the hosting capacity of distribution networks.”.
IEEE Trans. Power Del., 33(5):pp. 2294–2304, 2018.
[8] R. A. Jabr, I. Dac, and I. Huseinagi. “Real time
optimal reconfiguration of multiphase active distribution networks.”. IEEE Trans. Smart Grid, 9(6):pp.
6829–6839, 2018.
[9] Y. Takenobu, N. Yasuda, S. Kawano, S. I. Minato,
and Y. Hayashi. “Evaluation of annual energy loss
reduction based on reconfiguration scheduling.”.
IEEE Trans. Smart Grid, 9(3):pp. 1986–1996, 2018.
[10] A. G. Fonseca, O. L. Tortelli, and E. M. Lourenc¸o.
“Extended fast decoupled power −ow for reconfiguration networks in distribution systems.”. ET Gener.,
Transmiss. Distrib., 12(22):6033–6040, 2018.
[11] M. A. T. G. Jahani, P. Nazarian, A. Safari, and M. R.
Haghifam. “Multi-objective optimization model for
optimal reconfiguration of distribution networks
with demand response services.”. Sustain. Cities
Soc., 47:pp. 1–11, 2019.
[12] E. Kianmehr, S. Nikkhah, and A. Rabiee. “Multiobjective stochastic model for joint optimal allocation of DG units and network reconfiguration from
DG owner’s and DisCo’s perspectives.”. Renew. Energy, 132:pp. 471–485, 2019.
[13] C. Wang, S. Lei, P. Ju, C. Chen, C. Peng, and Y. Hou.
“MDP-based distribution network reconfiguration
with renewable distributed generation: Approximate dynamic programming approach.”. IEEE
Trans. Smart Grid, 11(4):pp. 3620–3631, 2020.
[14] M. Mahdavi and R. Romero. “Reconfiguration of
radial distribution systems: An efficient mathematical model.”. IEEE Latin Amer. Trans., 19(7):pp.
1172–1181, 2021.
[15] M. Mahdavi, H. H. Alhelou, N. D. Hatziargyriou, and
A. Al-Hinai. “An efficient mathematical model for
distribution system reconfiguration using AMPL.”.
IEEE Access, 9:pp. 79961–79993, 2021.
[16] N. V. Kova£ki, P. M. Vidovi, and A. T. Sari¢.
“Scalable algorithm for the dynamic reconfiguration of the distribution network using the Lagrange
relaxation approach”. Int. J. Elect. Power Energy
Syst., 94:pp. 188–202, 2018.
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system reconfiguration and DG sizing algorithm
without load −ow solution.”. IET Gener., Trans- miss.
Distrib, 12(6):pp. 1303–1313, 2018. [18] X. Ji, Q. Liu, Y. Yu, S. Fan, and N. Wu. “Distribution
network reconfiguration based on vector shift operation.”. IET Gener., Transmiss. Distrib., 12(13):pp.
3339–3345, 2018.
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allocation of distributed energy resources.”. IET
Gener., Transmiss. Distrib., 12(21):pp. 5700–5712,
2018.
[20] J. Wang, W. Wang, Z. Yuan, H. Wang, and J. Wu. “A
chaos disturbed beetle antennae search algorithm
for a multi-objective distribution network reconfiguration considering the variation of load and DG.”.
IEEE Access, 8:pp. 97392–97407, 2020.
[21] H. Wu, P. Dong, and M. Liu. “Distribution network
reconfiguration for loss reduction and voltage stability with random fuzzy uncertainties of renewable energy generation and load.”. IEEE Trans. Ind.
Informat., 16(9):pp. 5655–5666, 2020.
[22] A. Abbasi. “Investigation of simultaneous effect of
demand response and load uncertainty on distribution feeder reconfiguration.”. IET Gener., Transmiss.
Distrib., 14(8):pp. 1438–1449, 2020.
[23] Y. Song, Y. Zheng, T. Liu, S. Lei, and D. J. Hill. “A
new formulation of distribution network reconfiguration for reducing the voltage volatility induced by
distributed generation.”. IEEE Trans. Power Syst.,
35(1):pp. 496–507, 2020.
[24] I. B. Cattani, E. Chaparro, and B. Baran. ´
“Distribution system operation and expansion planning using network reconfiguration.”.
IEEE Latin Amer. Trans., 18(5):pp. 845–852, 2020.
[25] A. Tyagi, A. Verma, and P. R. Bijwe. “Reconguration for loadability limit enhancement of distribution systems.”. IET Gener., Transmiss. Dis- trib.,
12(1):pp. 88–93, 2018.
[26] V. B. Pamshetti, S. Singh, and S. P. Singh. “Combined
impact of network reconfiguration and volt-VAR
control devices on energy savings in the presence
of distributed generation.”. IEEE Syst. J., 14(1):pp.
995–1006, 2020.
[27] G. Zu, J. Xiao, and K. Sun. “Distribution network
reconfiguration comprehensively considering N−1
security and network loss.”. IET Gener., Transmiss.
Distrib., 12(8):pp. 1721–1728, 2018.
[28] Y. Yang, S. Zhang, W. Pei, J. Sun, and Y. Lu.
“Network reconfiguration and operation optimisation of distribution system with flexible DC device.’.
J. Eng., 2019(16):pp. 2401–2404, 2019.
[29] S. Sadeghi, A. Jahangiri, and A. Ghaderi Shamim.
“Optimal reconfiguration of a smart distribution
network in the presence of shunt capacitors.”. Electrical Engineering, 106:pp. 603–6014, 2023. DOI:
https://doi.org/10.1007/s00202-023-01997-y.
[30] O. W. Adebiyi, M. O. Okelola, and S. A. Salimon.
“Multi-objective Optimal Allocation of Renewable
Energy Distributed Generations and Shunt Capacitors in Radial Distribution System using Corona
Virus Herd Optimization.”. Majlesi Journal of Electrical Engineering, 17(3):pp. 145–164, 2023. DOI:
https://doi.org/10.30486/mjee.2023.1978728.1068.
[31] W. K. Al-Azzawi et al. “Optimization of Energy Systems in a Distribution Micro grid:
An Application of the Particle Swarm Optimization.”. Majlesi Journal of Electrical
Engineering, 17(2):pp. 41–46, 2023. DOI:
https://doi.org/10.30486/mjee.2023.1987130.1147.
[32] H. Lotfi, A. Nikooei, A. Shojaei, R. Ghazi, and
M. B. Naghibi Sistani. “An Enhanced Evolutionary Algorithm for Providing Energy Management
Schedule in the Smart Distribution Network.”. (in
en), Majlesi Journal of Electrical Engineering, 14(2):
pp. 17–23, 2020.
[33] A. Sadighmanesh, M. Sabahi, and M. Zavvari.
“Pareto Local Search Function for Optimal Placement of DG and Capacitors Banks in Distribution Systems.”. Majlesi Journal of Electrical Engineering, 15(3):pp. 81–92, 2021. DOI:
https://doi.org/10.52547/mjee.15.3.81.
[34] A. V. Reddy, M. Damodar Reddy, and Y. V. Reddy.
“Feeder Reconfiguration of Distribution Systems
for Loss Reduction and Emissions Reduction Using MVO Algorithm.”. (in en), Majlesi Journal of
Electrical Engineering, 12(2):1–8, 2018.
[35] Q. Liu, L. Zhang, X. Ji, and H. Shi. “Dynamic
reconfiguration of distribution network considering the uncertainty of distributed generation
and loads.”. (in English), Frontiers in Energy
Research, Original Research, 11, 2023. DOI:
https://doi.org/10.3389/fenrg.2023.1279579.
[36] S. M. Razavi, H. R. Momeni, M. R. Haghifam, and S. Bolouki. “Multi-Objective Optimization of Distribution Networks via Daily
Reconfiguration.”. IEEE Transactions on
Power Delivery, 37(2):pp. 775–785, 2022. DOI:
https://doi.org/10.1109/TPWRD.2021.3070796.
[37] H. Gao, W. Ma, S. He, L. Wang, and J. Liu. “TimeSegmented Multi-Level Reconfiguration in Distribution Network: A Novel Cloud-Edge Collaboration Framework.”. IEEE Transactions on
Smart Grid, 13(4):pp. 3319–3322, 2022. DOI:
https://doi.org/10.1109/TSG.2022.3156433.
[38] A. Azizivahed, A. Are, S. Ghavidel, M. Shae-Khah,
L. Li, J. Zhang, and J. P. S. Catalao. “ ˜ Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage.”. IEEE Trans. Sustain. Energy,
11(2):662–673, Apr. 2020.
[39] X. Ji, Z. Yin, Y. Zhang, B. Xu, and et al. “Realtime autonomous dynamic reconfiguration based
on deep learning algorithm for distribution network.”. Electric. Power Syst. Res., 195:107132, June.
2021.
[40] C. Wang, Y. Xu, and J. Zhang. “Dynamic Reconfiguration of Distribution Network Based on
Symbolization of Time Series of Load.”. 2019 IEEE
3rd International Electrical and Energy Conference
(CIEEC), Beijing, China, :1001–1006, 2019. DOI:
https://doi.org/10.1109/CIEEC47146.2019.CIEEC2019377.
)