Top

Energy Systems

Published in:

20-08-2020 | Review Article

Optimal current flow technique for distribution systems with renewable energy generation

Authors: Adolfo Blengini Neto, Fabiana Silva Podeleski, Lia Mota, Marcius Fabius Henriques de Carvalho

Published in: Energy Systems | Issue 3/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This article presents an optimal current flow (OCF) technique for analyzing distribution systems with renewable energy generation. The overall aim of this study is to reduce the complexity of the traditional nonlinear models by representing the problem as a quadratic optimization network flow model with linear constraints. The objective of the OCF method is minimization of renewable generation costs with or without consideration of transmission losses. Taking advantage of the topological characteristic of the distribution system, the network constraints are represented by Kirchhoff current and voltage laws. In addition, the model considers the capacity limits of the distribution components (renewable generators, transformers and lines current flow). The robustness and fastness of the OCF method is tested on 34-bus, 69-bus, and 400-bus systems under balanced and unbalanced, conditions and in radial or meshed distribution systems under different load scenarios. The results demonstrate the accuracy, and efficiency of the proposed approach in determining the optimal operation of a distribution system with renewable energy generation.

previous article RETRACTED ARTICLE: Green building design method based on system ecology

next article Prospect of green power generation as a solution to energy crisis in Bangladesh

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Latreche, Y., Bouchekara, H.R.E.R., Kerrour, F., Naidu, K., Mokhlis, H., Javaid, M.S.: Comprehensive review on the optimal integration of distributed generation in distribution systems. J. Renew. Sustain. Energy 10, 055303–33 (2018). https://doi.org/10.1063/1.5020190CrossRef

Sultana, U., Khairuddin, A.B., Aman, M.M., Mokhtar, A.S., Zareen, N.: A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system. Renew. Sustain. Energy Rev. 63, 363–378 (2016). https://doi.org/10.1016/j.rser.2016.05.056CrossRef

Sing, A.D., Sood, B.Y.R., Barnwal, C.A.K.: Case studies on optimal location and sizing of renewable energy generators in distribution system. J. Renew. Sustain. Energy 8, 65301–16 (2016). https://doi.org/10.1063/1.4972887CrossRef

Ghatak, U., Mukhrrjee, V.: An improved load flow technique based on load current injection for modern distribution system. Int. J. Electr. Power Energy Syst. 84, 168–181 (2017). https://doi.org/10.1016/j.ijepes.2016.05.008CrossRef

Garces, A.: A quadratic approximation for the optimal power flow in power distribution systems. Electr. Power Syst. Res. 130, 222–229 (2016). https://doi.org/10.1016/j.epsr.2015.09.006CrossRef

Jha, B.K., Kumar, A., Dheer, D.K., Singh, D., Misra, R.K.: A modified current injection load flow method under different load model of EV for distribution system. Int. Trans. Electr. Energy Syst. (2020). https://doi.org/10.1002/2050-7038.12284CrossRef

Tinney, W.G., Hart, C.E.: Power flow solutions by Newton’s method. IEEE Trans. Power Appar. Syst. 80, 1449–60 (1967). https://doi.org/10.1109/TPAS.1967.291823CrossRef

Stott, B., Alsaç, O.: Fast decoupled load flow. IEEE Trans. Power Appar. Syst. 93, 859–69 (1974). https://doi.org/10.1109/TPAS.1974.293985CrossRef

Yang, N.C., Chen, H.C.: Decomposed Newton algorithm-based three-phase power-flow for unbalanced radial distribution networks with distributed energy resources and electric vehicle demands. Int. J. Electr. Power Energy Syst. 96, 473–483 (2018). https://doi.org/10.1016/j.ijepes.2017.09.042CrossRef

10.

Seate, 2000. https://doi.org/10.1109/PESS.2000.867654

11.

Chen, T.H., Yang, N.C.: Loop frame of reference based three-phase power flow for unbalanced radial distribution systems. Electr. Power Syst. Res. 80, 799–800 (2010). https://doi.org/10.1016/j.epsr.2009.12.006CrossRef

12.

Garver, L.L., Wirgau, K.A.: Transmission network estimation using linear programming. IEEE Trans. Power Appar. Syst. 7, 1688–1697 (1970). https://doi.org/10.1109/TPAS.1970.292825CrossRef

13.

Garver, L.L., Wirgau, K.A.: Load supplying capability of generation-transmission networks. IEEE Trans. Power Appar. Syst. 3, 957–962 (1979). https://doi.org/10.1109/TPAS.1979.319253CrossRef

14.

Carvalho, M.F., Soares, S., Ohishi, T.: Optimal active power dispatch by network flow approach. IEEE Trans. Power Syst. 4, 1640–1647 (1988). https://doi.org/10.1109/59.192975CrossRef

15.

Antunez, C.S., Dominguez, O.D.M., Lavorato, M.L., Franco, J.F., Rider, M.J.: Volt-VAr control and energy storage device operation to improve the electric vehicle charging coordination in unbalanced distribution networks. IEEE Trans. Sustain. Energy (2017). https://doi.org/10.1109/TSTE.2017.2695195CrossRef

16.

Esmaeili, S., Anvari-Moghaddam, A., Jadid, S.: Optimal operational scheduling of reconfigurable multi-microgrids considering energy storage systems. Energies (2019). https://doi.org/10.3390/en12091766CrossRef

17.

Vahedipour-Dahraie, M., Rashidizadeh-Kermani, H., Anvari-Moghaddam, A.: Stochastic risk-constrained scheduling of renewable-powered autonomous microgrids with demand response actions: reliability and economic implications. IEEE Trans. Ind. Appl. 56, 1882–1895 (2020). https://doi.org/10.1109/TIA.2019.2959549CrossRef

18.

Li, H., Jin, Y., Zhang, A., Shen, X., Li, C., Kong, B.: An improved hybrid load flow calculation algorithm for weakly meshed power distribution system. Int. J. Electr. Power Energy Syst. 96, 437–445 (2016). https://doi.org/10.1016/j.ijepes.2015.08.004CrossRef

19.

Fujisawa, C.H., Carvalho, M.F., Azevedo, A.T., Soares, S., Santos, E.P., Ohishi, T.: Optimal power flow models using network flow method. In: Transmission and Distribution Conference and Exposition: Latin America IEEE PES (2012). https://doi.org/10.1109/TDC-LA.2012.6319108

20.

Yuntao, J., Wenchuan, J., Boming, Z., Hongbin, S.: Loop-analysis-based continuation power flow algorithm for distribution networks. IET Gener. Transm. Distrib. 8, 1284–1292 (2014). https://doi.org/10.1049/iet-gtd.2013.0529CrossRef

21.

Araujo, L.R., Penido, D.R.R., do Amaral Filho, N.Alves, Beneteli, T.A.P.: Sensitivity analysis of convergence characteristics in power flow methods for distribution systems. Electr. Power Energy Syst. 97, 211–219 (2018). https://doi.org/10.1016/j.ijepes.2017.11.012CrossRef

22.

Steponavice, I., Frank, S., Rebennack, S.: Optimal power flow: a bibliographic survey I. Energy Syst. 3, 221–258 (2012). https://doi.org/10.1007/s12667-012-0056-yCrossRef

23.

Berkelaar, M., Eikland, K., Notebaert, P.: lp solve, version 5.5.2.0. 2010. eprint: lpsolve.sourceforge.net

24.

Kersting, W.H.: Radial distribution test feeders. In: Power Engineering Society Winter Meeting (2001). https://doi.org/10.1109/PESW.2001.916993

25.

Aravindhababu, P., Ganapathy, S., Nayar, K.R.: A novel technique for the analysis of radial distribution systems. Int. J. Electr. Power Energy Syst. 23(3), 167–171 (2001). https://doi.org/10.1016/S0142-0615(00)00048-XCrossRef

26.

Blengini, A., Carvalho, M.F.H., Mota, L.T.M., Pezzuto, C.C.: Object-oriented load flow modeling and implementation for distribution power systems. IEEE Latin Am. Trans. 15, 439–444 (2017). https://doi.org/10.1109/TLA.2017.7867593CrossRef

27.

Savier, J.S., Das, D.: Impact of network reconfiguration on loss allocation of radial distribution systems. IEEE Trans. Power Deliv. 22(4), 2473–2480 (2007). https://doi.org/10.1109/TPWRD.2007.905370CrossRef

28.

Rupa, J.A.M., Ganesh, S.: Power flow analysis for radial distribution system using backward/forward sweep method. Int. J. Electr. Comput. Energ. Electr. Commun. Eng. 8(10), 1621–1625 (2014)

29.

Ghatak, U., Mukherjee, V.: A fast and efficient load flow technique for unbalanced distribution system. Int. J. Electr. Power Energy Syst. 84, 99–110 (2017). https://doi.org/10.1016/j.ijepes.2016.05.002CrossRef

30.

Baran, J.R.A.R., Fernandes, S.P.T.: A three-phase optimal power flow applied to the planning of unbalanced distribution networks. Int. J. Electr. Power Energy Syst. 74, 301–309 (2016). https://doi.org/10.1016/j.ijepes.2015.07.004CrossRef

31.

Pereira, C.A.N.: “Alocação ótima de reguladores de tensão em redes de distribuição”. M.S. thesis. Universidade Estadual de Campinas, (2009)

Title: Optimal current flow technique for distribution systems with renewable energy generation
Authors: Adolfo Blengini Neto
Fabiana Silva Podeleski
Lia Mota
Marcius Fabius Henriques de Carvalho
Publication date: 20-08-2020
Publisher: Springer Berlin Heidelberg
Published in: Energy Systems / Issue 3/2022
Print ISSN: 1868-3967
Electronic ISSN: 1868-3975
DOI: https://doi.org/10.1007/s12667-020-00402-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 3/2022

Adaptive cloud resource management through workload prediction

Scalable intelligent data-driven decision making for cognitive cities

A task-based model for minimizing energy consumption in WSNs

Green building design method based on system ecology

Intent and permission modeling for privacy leakage detection in android

Power load forecasting based on long short term memory-singular spectrum analysis