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RETRACTED ARTICLE: Multi objective dragonfly algorithm for congestion management in deregulated power systems

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This article was retracted on 04 July 2022

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Abstract

Congestion in transmission corridors are the major bother for deregulated power system’s operation. Generator rescheduling along with demand alteration is a traditional remedy for transmission line congestion. According to market clearing process, the system operator (SO) has to pay a certain amount of cost to the market participants for rescheduling the generation and demand. This kind of redispatch related congestion management (CM) procedure is mainly carried out to reduce the congestion cost, but they are failing to provide an attention in power systems security. The risky generator’s power shifts may diminish the voltage and transient stability of the power system. So power system security should be included in the congestion management procedure. In this proposed multi objective congestion management procedure, rescheduling of active power is carried out to improve/retain the power systems security along with a congestion cost reduction. Voltage security margin (λ) and corrected transient energy margin (CTEM) provides a measure for power system security level. Multi Objective Dragonfly Algorithm (MODA) is employed to trace the non dominated solutions for three conflicting objectives. Fuzzy decision making principle is applied to select the best Pareto solution depends on the objective’s significances. The goodness of the MODA optimization approaches is experimented in congestion alleviation of New England 39 bus systems and solutions are compared with some reputed methods.

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References

  • Andersson G, Donalek P, Farmer R, Hatziargyriou N, Kamwa I, Kundur P, Martins N, Paserba J, Pourbeik P, Sanchez-Gasca J, Schulz R, Stankovic A, Taylor C, Vittal V (2005) Causes of the 2003 major grid blackouts in north america and europe, and recommendedmeans to improve system dynamic performance. IEEE Trans Power Syst 20:1922–1928

    Article  Google Scholar 

  • Balaraman S, Kamaraj N (2011) Transmission congestion management using particle swarm optimization. J Electr Syst 7(1):54–70

    Google Scholar 

  • Bhattacharjee T, Chakraborty AK (2013) NSGAII-based congestion management in a pool-based electricity market incorporating voltage and transient stability. Electric Power Comp Syst 41(10):990–1001

    Article  Google Scholar 

  • Carlos CAC (2009) Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Front Comput Sci China 3:18–30

    Article  Google Scholar 

  • Christie R, Wollenberg B, Wangensteen I (2000) Transmission management in the deregulated environment. Proc IEEE 88:170–195

    Article  Google Scholar 

  • Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comput 8(3):256–279

    Article  Google Scholar 

  • Conejo AJ, Milano F, García-Bertrand R (2006) Congestion management ensuring voltage stability. IEEE Trans Power Syst 21(1):357–364

    Article  Google Scholar 

  • David AK, David X (2002) Dynamic security enhancement in power-market systems. IEEE Trans Power Syst 17(2):431–438

    Article  Google Scholar 

  • Dutta S, Singh SP (2008) Optimal rescheduling of generators for congestion management based on particle swarm optimization. IEEE Trans Power Syst 23(4):1560–1569

    Article  Google Scholar 

  • Esmaili M, Shayanfar HA, Amjady N (2009a) Congestion management considering voltage security of power systems. Energy Convers Manage 50:2562–2569

    Article  Google Scholar 

  • Esmaili M, Shayanfar HA, Amjady N (2009b) Multi-objective congestion management incorporating voltage and transient stabilities. Energy 34:1401–1412

    Article  Google Scholar 

  • Esmaili M, Shayanfar HA, Amjady N (2010a) Congestion management, enhancing transient stability of power systems. Appl Energy 87(3):971–981

    Article  Google Scholar 

  • Esmaili M, Amjady N, Shayanfar HA (2010b) Stocastic multi-Multi-objective congestion management in power markets improving voltage and transient stabilities. Eur Trans Electr Power 21:9–115

    Google Scholar 

  • Esmaili M, Shayanfar HA, Amjady N (2011) Multi-objective congestion management by modified augumented e-contraint method. Appl Energy 88:755–766

    Article  Google Scholar 

  • Fang DZ, Chung TS, Zhang Y, Song W (2000) Transient Stability limit conditions analysis using a corrected transient energy function approach. IEEE Trans Power Syst 15(2):804–810

    Article  Google Scholar 

  • Hazra J, Sinha AK (2007) Congestion management using multi objective particle swarm optimization. IEEE Trans Power Syst 22(4):1726–1734

    Article  Google Scholar 

  • Kumar A, Srivastava SC, Singh SN (2004a) A zonal congestion management approach using real and reactive power rescheduling. IEEETrans Power Syst 18:554–562

    Article  Google Scholar 

  • Kumar A, Srivastava SC, Singh SN (2004b) A zonal congestion management approach using AC transmission congestion distribution factors, Electric Power Syst. Res 72:85–93

    Google Scholar 

  • Lai LL (2001) Power system restructuring and deregulation. Wiley, New York

    Book  Google Scholar 

  • Larsson S, Ek E (2004) The black-out in southern Sweden and eastern Denmark. In: IEEE Power Engineering Society General Meeting. IEEE, pp. 1668-1672.

  • Makhadmeh SN, Khader AT, Al-Betar MA, Naim S (2019) Multi-objective power scheduling problem in smart homes using grey wolf optimizer. J Ambient Intell Humaniz Comput 10:3643–3667

    Article  Google Scholar 

  • Manikandan BV, Charles Raja S, Venkatesh P, Mandala M (2011) Comparative study of two congestion management methods for the restructured power systems. J Electr Eng Technol 6(3):302–310

    Article  Google Scholar 

  • Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2015) Novel performance metrics for robust multi-objective optimization algorithms. Swarm Evolut Comput 21:1–23

    Article  Google Scholar 

  • Niknam T, Meymand HZ, Mojarrad HD (2011) An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants. Energy 36:119–132

    Article  Google Scholar 

  • Niknam T, Narimani MR, Aghaei J, Azizipanah-Abarghooee R (2012) Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gener Trans Distrib 6(6):515–527

    Article  Google Scholar 

  • Pandya KS, Joshi SK (2013) Sensitivity and particle swarm optimization-based congestion management’. Electric Power Comp Syst 41(4):465–484

    Article  Google Scholar 

  • Pourbeik P, Kundur PS, Taylor CW (2006) The anatomy of a power grid blackout-root causes and dynamics of recent major blackouts. IEEE Power Energ Mag 4:22–29

    Article  Google Scholar 

  • Shahabi NS, Manthouri M, Farivar F (2020) A multi-objective ant colony optimization algorithm for community detection in complex networks. J Ambient Intell Humaniz Comput 11:5–21

    Article  Google Scholar 

  • Shahidehpour M, Yamin H, Li Z (2002) Market Operations in Electric Power Systems. John Wiley & Sons, Chichester

    Book  Google Scholar 

  • Verma S, Mukherjee V (2016a) Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser. IET Gener Trans Distrib 10(10):2548–2561

    Article  Google Scholar 

  • Verma S, Mukherjee V (2016b) Firefly algorithm for congestion management in deregulated environment. Eng Sci Technol Int J 19(3):1254–1265

    Google Scholar 

  • Yesuratnam G, Thukaram D (2007) Congestion management in open access based on relative electrical distances using voltage stability criteria. Electric Power Syst Res 77:1608–1618

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of EEE, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India

    C. Saravanan & P. Anbalagan

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  1. C. Saravanan
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  2. P. Anbalagan
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Correspondence to C. Saravanan.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04282-1

Appendix

Appendix

See Tables 7 and 8.

Table 7 Market clearing data for supply unit
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Table 8 Market data for demand unit
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Saravanan, C., Anbalagan, P. RETRACTED ARTICLE: Multi objective dragonfly algorithm for congestion management in deregulated power systems. J Ambient Intell Human Comput 12, 7519–7528 (2021). https://doi.org/10.1007/s12652-020-02440-x

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  • DOI: https://doi.org/10.1007/s12652-020-02440-x

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