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Neural Computing and Applications

, Volume 31, Supplement 1, pp 509–522 | Cite as

Differential evolution-based efficient multi-objective optimal power flow

  • S. Surender ReddyEmail author
  • P. R. Bijwe
Original Article

Abstract

This paper proposes a novel-efficient evolutionary-based multi-objective optimization (MOO) approaches for solving the optimal power flow (OPF) problem using the concept of incremental load flow model based on sensitivities and some heuristics. This paper is useful in robust decision-making for the system operator. The main disadvantage of meta-heuristic-based MOO approach is computationally burdensome. The motivation of this paper is to overcome this drawback. By using the proposed efficient MOO approach, the number of load flows to be performed is reduced substantially, resulting to the solution speed up. Here, three objective functions, i.e., generator fuel cost minimization, loss minimization, and L index minimization are considered. The proposed approach can effectively handle the complex non-linearities, discontinuities, discrete variables, and multiple objectives. The potential and suitability of the proposed efficient MOO approach is tested on the IEEE 30 bus system. The results obtained with the proposed efficient MOO approach are also compared with the meta-heuristic-based non-dominated sorting genetic algorithm-2 (NSGA-II) technique. In this paper, the proposed efficient MOO approach is implemented using the differential evolutionary (DE) algorithm. However, it is a generic one and can be implemented with any type of evolutionary algorithm.

Keywords

Multi-objective optimization Optimal power flow Pareto optimal solutions Sensitivity Fuel cost Transmission loss Voltage stability 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Osman MS, Abo-Sinna MA, Mousa AA (2004) A solution to the optimal power flow using genetic algorithm. Appl Math Comput 155(2):391–405MathSciNetzbMATHGoogle Scholar
  2. 2.
    Sailaja Kumari M, Maheswarapu S (2010) Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution. Int J Electr Power Energy Syst 32(6):736–742CrossRefGoogle Scholar
  3. 3.
    Bakirtzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. IEEE Trans Power Syst 17(2):229–236CrossRefGoogle Scholar
  4. 4.
    Abido MA (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24(7):563–571CrossRefGoogle Scholar
  5. 5.
    Ongsakul W, Tantimaporn T (2006) Optimal power flow by improved evolutionary programming. Electric Power Components and Systems 34(1):79–95CrossRefGoogle Scholar
  6. 6.
    Abou El Ela AA, Abido MA, Spea SR (2010) Optimal power flow using differential evolution algorithm. Electr Power Syst Res 80(7):878–885CrossRefGoogle Scholar
  7. 7.
    Tang WJ, Li MS, Wu QH, Saunders JR (2008) Bacterial foraging algorithm for optimal power flow in dynamic environments. IEEE Trans Circuits and Systems I: Regular Papers 55(8):2433–2442MathSciNetCrossRefGoogle Scholar
  8. 8.
    Duman S, Güvenç U, Sönmez Y, Yörükeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59:86–95CrossRefGoogle Scholar
  9. 9.
    C. A. Roa-Sepulveda, B. J. Pavez-Lazo, A solution to the optimal power flow using simulated annealing, Proc. IEEE Power Tech, vol. 2, pp. 5Google Scholar
  10. 10.
    Bhattacharya A, Chattopadhyay PK (2010) Biogeography-based optimization for solution of optimal power flow problem. Proc. Electrical Engineering/Electronics Computer Telecommunications and Information Technology, Chaing Mai, pp 435–439Google Scholar
  11. 11.
    Abido MA (2002) Optimal power flow using Tabu search algorithm. Electric Power Components and Systems 30(5):469–483CrossRefGoogle Scholar
  12. 12.
    Surender Reddy S, Bijwe PR, Abhyankar AR (2014) Faster evolutionary algorithm based optimal power flow using incremental variables. International Journal of Electrical Power and Energy Systems 54:198–210CrossRefGoogle Scholar
  13. 13.
    Surender Reddy S, Bijwe PR (2016) Efficiency improvements in meta-heuristic algorithms to solve the optimal power flow problem. International Journal of Electrical Power and Energy Systems 82:288–302CrossRefGoogle Scholar
  14. 14.
    Lashkar Ara A, Kazemi A, Gahramani S, Behshad M (2012) Optimal reactive power flow using multi-objective mathematical programming. Scientia Iranica 19(6):1829–1836CrossRefGoogle Scholar
  15. 15.
    Liu X, Xu W (2010) Minimum emission dispatch constrained by stochastic wind power availability and cost. IEEE Trans Power Syst 25(3):1705–1713CrossRefGoogle Scholar
  16. 16.
    Abido MA (2004) Multiobjective optimal power flow using strength Pareto evolutionary algorithm. Universities Power Engineering Conference, Bristol, pp 457–461Google Scholar
  17. 17.
    M.A. Abido, Multiobjective particle swarm optimization for optimal power flow problem, 12th International Middle-East Power System Conference, Aswan, 2008, pp. 392–396Google Scholar
  18. 18.
    Hazra J, Sinha AK (2011) A multi-objective optimal power flow using particle swarm optimization. European Transactions on Electrical Power 1(1):1028–1045CrossRefGoogle Scholar
  19. 19.
    Niknam T, Narimani MR, Aghaei J, Azizipanah-Abarghooee R (2011) Improved particle swarm optimization for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gereration, Transmission and Distribution 6(6):515–527CrossRefGoogle Scholar
  20. 20.
    Abido MA, Al-Ali NA (2012) Multi-objective optimal power flow using differential evolution. Arab J Sci Eng 37(4):991–1005CrossRefzbMATHGoogle Scholar
  21. 21.
    Varadarajan M, Swarup KS (2008) Solving multi-objective optimal power flow using differential evolution. IET Gereration, Transmission and Distribution 2(5):720–730CrossRefGoogle Scholar
  22. 22.
    Basu M (2016) Multi-objective optimal reactive power dispatch using multi-objective differential evolution. Int J Electr Power Energy Syst 82:213–224CrossRefGoogle Scholar
  23. 23.
    Capitanescu F (2016) Critical review of recent advances and further developments needed in AC optimal power flow. Electr Power Syst Res 136:57–68CrossRefGoogle Scholar
  24. 24.
    Abaci K, Yamacli V (2016) Differential search algorithm for solving multi-objective optimal power flow problem. Int J Electr Power Energy Syst 79:1–10CrossRefGoogle Scholar
  25. 25.
    Daryani N, Hagh MT, Teimourzadeh S (2016) Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl Soft Comput 38:1012–1024CrossRefGoogle Scholar
  26. 26.
    Chaib AE, Bouchekara HREH, Mehasni R, Abido MA (2016) Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. Int J Electr Power Energy Syst 81:64–77CrossRefGoogle Scholar
  27. 27.
    Bouchekara HREH, Chaib AE, Abido MA, El-Sehiemy RA (2016) Optimal power flow using an improved colliding bodies optimization algorithm. Appl Soft Comput 42:119–131CrossRefGoogle Scholar
  28. 28.
    Ding T, Li C, Li F, Chen T, Liu R (2017) A bi-objective DC-optimal power flow model using linear relaxation-based second order cone programming and its Pareto frontier. Int J Electr Power Energy Syst 88:13–20CrossRefGoogle Scholar
  29. 29.
    M. Ding, H. Chen, N. Lin, S. Jing, F. Liu, X. Liang, W. Liu, Dynamic population artificial bee colony algorithm for multi-objective optimal power flow, Saudi Journal of Biological Sciences, 2017Google Scholar
  30. 30.
    Zhang J, Tang Q, Li P, Deng D, Chen Y (2016) A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Appl Soft Comput 47:494–514CrossRefGoogle Scholar
  31. 31.
    Zhou J, Wang C, Li Y, Wang P, Li C, Lu P, Mo L (2017) A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security. Appl Math Model 45:684–704MathSciNetCrossRefGoogle Scholar
  32. 32.
    Yuan X, Zhang B, Wang P, Liang J, Yuan Y, Huang Y, Lei X (2017) Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy 122:70–82CrossRefGoogle Scholar
  33. 33.
    Bai W, Eke I, Lee KY (2017) An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem. Control Eng Pract 61:163–172CrossRefGoogle Scholar
  34. 34.
    Surender Reddy S (2017) Optimizing energy and demand response programs using multi-objective optimization. Electr Eng 99(1):397–406CrossRefGoogle Scholar
  35. 35.
    Roy PK, Ghoshal SP, Thakur SS (2010) Combined economic and emission dispatch problems using biogeography-based optimization. Electr Eng 92(4):173–184CrossRefGoogle Scholar
  36. 36.
    J. Ning, B. Zhang, T. Liu, C. Zhang, An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem, Neural Computing and Applications, pp. 1–11, 2016Google Scholar
  37. 37.
    Jia L, Cheng D, Chiu MS (2012) Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes. Neural Comput & Applic 21(6):1107–1116CrossRefGoogle Scholar
  38. 38.
    Reddy SS, Rathnam CS (2016) Optimal power flow using glowworm swarm optimization. Int J Electr Power Energy Syst 80:128–139CrossRefGoogle Scholar
  39. 39.
    K. Deb, Multi-objective optimization using evolutionary algorithms, John Wiley and Sons, 2001Google Scholar
  40. 40.
    Sailaja Kumari M, Maheswarapu S (2010) Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution. Int J Electr Power Energy Syst 32(6):736–742CrossRefGoogle Scholar
  41. 41.
    Surender Reddy S, Abhyankar AR, Bijwe PR (2011) Reactive power price clearing using multi-objective optimization. Energy 36(5):3579–3589CrossRefGoogle Scholar
  42. 42.
    IEEE tutorial course on optimal power flow: solution techniques, requirements and challenges, 1996Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Railroad and Electrical EngineeringWoosong UniversityDaejeonRepublic of Korea
  2. 2.Department of Electrical EngineeringIndian Institute of Technology DelhiNew DelhiIndia

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