Skip to main content

Advertisement

Log in

Algorithms for Operation Planning of Electric Distribution Networks

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

In this paper, two algorithms for solving the problem of operation planning of electric power distribution systems are presented. This problem consists in defining the values of a set of continuous and discrete control variables to obtain better system performance. Given the large number of possible combinations of the settings of the control variables, the problem presents combinatorial explosion. To work around it, two procedures are proposed. In the first one, the genetic algorithm developed by Chu and Beasley (GACB) is used with a special mechanism of creation of the initial population. In the second procedure, a simplified algorithm which is capable of obtaining good quality solutions at a much lower computational time complexity than GACB is proposed. The results for the 34- and 70-bus IEEE test systems show that the proposed algorithms are promising.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Alcântara, M. V. P. (2005). Alocação de capacitores em sistemas de distribuição de energia elétrica. Master’s thesis, Faculdade de Engenharia Elétrica e de Computação, UNICAMP.

  • Amasifen, J. C. C., Romero, R., & Mantovani, J. R. S. (2005). Algoritmos evolutivos dedicados à reconfiguração de redes radiais de distribuição sob demandas fixas e variáveis: estudo dos operadores genéticos e parâmetros de controle. Sba: Controle & Automação. Sociedade Brasileira de Automática, 16, 303–317.

  • AMPL. (2011). AMPL—A modeling language for mathematical programming. www.ampl.com. Accessed 25 March 2013.

  • ANEEL. (2010). Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional (PRODIST). Módulos 1 a 8. www.aneel.gov.br. Accessed 25 March 2013.

  • Auchariyamet, S., & Sirisumrannukul, S. (2010). Optimal daily coordination of volt/var control devices in distribution systems with distributed generators. 2010 45th International universities power engineering conference (UPEC) (pp. 1–6).

  • Bento, E. P., & Kagan, N. (n.d.). Algoritmos genéticos e variantes na solução de problemas de configuração de redes de distribuição. Sba: Controle & Automação. Sociedade Brasileira de Automática.

  • Chu, P. C., & Beasley, J. E. (1997). A genetic algorithm for the generalized assignment problem. Computers and Operations Research, 24, 17–23.

    Article  MathSciNet  MATH  Google Scholar 

  • de Almeida, M., Costa, F., de Souza, S. X., & Santana, F. (2011). Optimal placement of faulted circuit indicators in power distribution systems. Electric Power Systems Research, 81(2), 699–706.

    Article  Google Scholar 

  • de J Silva, I., Rider, M., Romero, R., Garcia, A., & Murari, C. (2005). Transmission network expansion planning with security constraints. IEE Proceedings in Generation, Transmission and Distribution, 152(6), 828–836.

    Article  Google Scholar 

  • Gallego, R., Monticelli, A., & Romero, R. (1998). Transmission system expansion planning by an extended genetic algorithm. IEE Proceedings—Generation, Transmission and Distribution, 145(3), 329–335.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley.

    MATH  Google Scholar 

  • Guimarães, M. A. N. (2009). Plataforma integrada para o planejamento de sistemas de distribuição de energia elétrica utilizando metaheurísticas. PhD thesis, Faculdade de Engenharia Elétrica e de Computação, UNICAMP.

  • Haffner, S., Monticelli, A., Garcia, A., Mantovani, J., & Romero, R. (2000). Branch and bound algorithm for transmission system expansion planning using a transportation model. IEE Proceedings—Generation, Transmission and Distribution, 147(3), 149–156.

    Article  Google Scholar 

  • IEEE-PES. (2010). IEEE 34 bus distribution test feeders. Accessed 30 May 2012, from www.ewh.ieee.org/soc/pes/dsacom/testfeeders/index.html.

  • Jauch, E. (2011). Possible effects of smart grid functions on LTC transformers. IEEE Transactions on Industry Applications, 47(2), 1013–1021.

    Article  Google Scholar 

  • Kashem, M., & Ledwich, G. (2004). Distributed generation as voltage support for single wire earth return systems. IEEE Transactions on Power Delivery, 19(3), 1002–1011.

    Article  Google Scholar 

  • Levitin, G., Kalyuzhny, A., Shenkman, A., & Chertkov, M. (2000). Optimal capacitor allocation in distribution systems using a genetic algorithm and a fast energy loss computation technique. IEEE Transactions on Power Delivery, 15(2), 623–628.

    Article  Google Scholar 

  • Miasaki, C. T., & Romero, R. (2007). Um algoritmo genético especializado aplicado ao planejamento da expansão do sistema de transmissão com alocação de dispositivos de compensação série. Sba: Controle & Automação. Sociedade Brasileira de Automática, 18, 210–222.

  • Murtagh, B., & Saunders, M. (n.d.). Linear and nonlinear mathematical optimization solver. www.sbsi-sol-optimize.com. Accessed 25 March 2013.

  • Padilha, L. N. (2010). Análise comparativa de estratégias para regulação de tensão em sistemas de distribuição de energia elétrica na presença de geradores distribuídos. Master’s thesis, Escola de Engenharia de São Carlos da Universidade de São Paulo, EESC/USP.

  • Silva, I., Rider, M., Romero, R., & Murari, C. (2006). Transmission network expansion planning considering uncertainty in demand. IEEE Transactions on Power Systems, 21(4), 1565–1573.

    Article  Google Scholar 

  • Viawan, F., & Karlsson, D. (2007). Combined local and remote voltage and reactive power control in the presence of induction machine distributed generation. IEEE Transactions on Power Systems, 22(4), 2003–2012.

    Article  Google Scholar 

  • Wu, Y.-K., Lee, C.-Y., Liu, L.-C., & Tsai, S.-H. (2010). Study of reconfiguration for the distribution system with distributed generators. IEEE Transactions on Power Delivery, 25(3), 1678–1685.

    Article  Google Scholar 

Download references

Acknowledgments

CAPES, FAPESP and CNPq for financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Madson C. de Almeida.

Appendix

Appendix

The equations of active and reactive powers and calculated active power losses in the branches are given by:

$$\begin{aligned}&P_{k}^\mathrm{cal}= V_{k}\sum \limits _{m \in \Omega _{k}}V_{m}(G_{km}\cos (\theta _{k} -\theta _{m}) + B_{km}\sin (\theta _{k} -\theta _{m}))\nonumber \\ \end{aligned}$$
(16)
$$\begin{aligned}&Q_{k}^\mathrm{cal}= V_k\sum \limits _{m \in \Omega _{k}}V_m(G_{km}\sin (\theta _{k} -\theta _{m}) - B_{km}\cos (\theta _{k} -\theta _{m}))\nonumber \\ \end{aligned}$$
(17)
$$\begin{aligned}&P_{j}^\mathrm{l}= \sum \limits _{r=1}^{nr}g_{km}(V_{k}^{2}t_{km}^{2} + V_{m}^2 - 2V_{k}V_{m}t_{km}cos(\theta _{k} -\theta _{m})).\nonumber \\ \end{aligned}$$
(18)

\(\Omega _{k}\) is the set of all neighboring buses to bus \(k\); \(V_{k},\) \(V_m,\) \(\theta _{k}\) and \(\theta _{m}\) are the magnitudes and angles of voltage in the buses at the branch terminals \(k m,\) and \(G_{km}\) and \(B_{km}\) are the real and imaginary parts of each element of the nodal admittance matrix. Furthermore, \(r\) is the branch between the buses \(k m,\) and \(nr\) is the total number of branches, while \(g_{km}\) is the conductance of the branch \(k m.\)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Araujo, R.A., Meira, P.C.M. & de Almeida, M.C. Algorithms for Operation Planning of Electric Distribution Networks. J Control Autom Electr Syst 24, 377–387 (2013). https://doi.org/10.1007/s40313-013-0018-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-013-0018-1

Keywords

Navigation