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Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach

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Abstract

Nowadays, the increasing usage of renewable energy sources (RES) in modern power systems introduces new challenges in power system planning and operation. Specifically, a high penetration of RESs introduces additional complexity into the optimal power flow (OPF) problem, which has a highly nonlinear complex structure. Under this environment, this paper discusses a modified hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) integrated with chaotic maps (CPSOGSA) to apply the composite benchmark test functions and to solve the OPF problem with stochastic wind power and flexible alternating current transmission system (FACTS) devices. Numerical studies are used to illustrate effectiveness of the proposed CPSOGSA approach against other approaches such as moth swarm algorithm, grey wolf optimizer, and whale optimization algorithm. Additionally, to demonstrate the superiority and robustness of CPSOGSA algorithm, Wilcoxon signed-rank test is applied for all case studies. Case studies indicate the potential of CPSOGSA method in effectively solving OPF problem with stochastic wind power and FACTS devices.

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References

  1. Rahmani S, Amjady N (2017) A new optimal power flow approach for wind energy integrated power systems. Energy 134:349–359

    Article  Google Scholar 

  2. Teeparthi K, Kumar DMV (2017) Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators. Eng Sci Technol Int J 20(2):411–426

    Google Scholar 

  3. Luo J, Shi L, Ni Y (2018) A solution of optimal power flow incorporating wind generation and power grid uncertainties. IEEE Access 6:19681–19690

    Article  Google Scholar 

  4. Chen JJ, Wu QH, Zhang LL, Wu PZ (2017) Multi-objective mean–variance–skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties. Eur J Oper Res 263(2):719–732

    Article  MathSciNet  MATH  Google Scholar 

  5. Shafiq S, Javaid N, Asif S, Ali F, Hussain C, Khurshid N (2018) An optimal power flow approach for stochastic wind and solar energy integrated power systems. In: Proceedings of the 12th international conference on complex, intelligent, and software intensive systems (CISIS-2018), pp 292–304

  6. Kouadri R, Slimani L, Bouktir T, Musirin I (2018) Optimal power flow solution for wind integrated power in presence of VSC-HVDC using ant lion optimization. Indones J Electr Eng Comput Sci 12(2):625–633

    Article  Google Scholar 

  7. Ghasemi H, Park HS, Rabczuk T (2017) A level-set based IGA formulation for topology of flexoelectric materials. Comput Methods Appl Mech Eng 313:239–258

    Article  MathSciNet  MATH  Google Scholar 

  8. Yan X, Quintana VH (1999) Improving an interior-point-based OPF dynamic adjustments of step sizes and tolerances. IEEE Trans Power Syst 14(2):709–717

    Article  Google Scholar 

  9. Zhang S, Irving MR (1994) Enhanced Newton-Raphson algorithm for normal, controlled and optimal power flow solutions using column exchange techniques. IEE Proc Gener Transm Distrib 141(6):647–657

    Article  Google Scholar 

  10. Monticelli A, Pereira MVF, Granville S (1987) Security-constrained optimal power flow with post-contingency corrective rescheduling. IEEE Trans Power Syst 2(1):175–180

    Article  Google Scholar 

  11. Alsac O, Bright J, Prais M, Stott B (1990) Further developments in LP-based optimal power flow. IEEE Trans Power Syst 5(3):697–711

    Article  Google Scholar 

  12. Capitanescu F, Glavic M, Ernst D, Wehenkel L (2007) Interior-point based algorithms for the solution of optimal power flow problems. Electr Power Syst Res 77(5–6):508–517

    Article  Google Scholar 

  13. Capitanescu F, Glavic M, Ernst D, Wehenkel L (2006) Applications of security-constrained optimal power flows. In: Proceedings of modern electric power systems symposium (MEPS06), Wroclaw, Poland

  14. Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GAJ (2018) Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Eng Appl Artif Intell 68:81–100

    Article  Google Scholar 

  15. Attia AF, El Sehiemy RA, Hasanien HM (2018) Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int J Electr Power Energy Syst 99:331–343

    Article  Google Scholar 

  16. El-Fergany AA, Hasanien HM (2018) Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons. Appl Soft Comput 64:307–316

    Article  Google Scholar 

  17. Saha A, Das P, Chakraborty AK (2017) Water evaporation algorithm: a new metaheuristic algorithm towards the solution of optimal power flow. Eng Sci Technol Int J 20:1540–1552

    Google Scholar 

  18. Mohamed AAA, Mohamed YS, El-Gaafary AAM, Hemeida AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206

    Article  Google Scholar 

  19. Mukherjee A, Mukherjee V (2016) Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm. Int J Electr Power Energy Syst 78:700–714

    Article  Google Scholar 

  20. Prasad D, Mukherjee V (2016) A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Eng Sci Technol Int J 19:79–89

    Google Scholar 

  21. Basu M (2008) Optimal power flow with FACTS devices using differential evolution. Int J Electr Power Energy Syst 30:150–156

    Article  Google Scholar 

  22. Rao BV, Kumar GVN (2015) Optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller. Int J Electr Power Energy Syst 68:81–88

    Article  Google Scholar 

  23. Edward JB, Rajasekar N, Sathiyasekar K, Senthilnathan N, Sarjila R (2013) An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability. ISA Trans 52(5):622–628

    Article  Google Scholar 

  24. Ongsakul W, Bhasaputra P (2002) Optimal power flow with FACTS devices by hybrid TS/SA approach. Int J Electr Power Energy Syst 24(10):851–857

    Article  Google Scholar 

  25. Panda A, Tripathy M (2015) Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm. Energy 93:816–827

    Article  Google Scholar 

  26. Shi L, Wang C, Yao L, Ni Y, Bazargan M (2012) Optimal power flow solution incorporating wind power. IEEE Syst J 6(2):233–241

    Article  Google Scholar 

  27. Reddy SS (2018) Multi-objective optimal power flow for a thermal-wind-solar power system. J Green Eng 7(4):451–476

    Article  Google Scholar 

  28. Roy R, Jadhav HT (2015) Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. Int J Electr Power Energy Syst 64:562–578

    Article  Google Scholar 

  29. Pecora LM, Carroll TL (1990) Synchronization in chaotic systems. Phys Rev Lett 64(8):821–824

    Article  MathSciNet  MATH  Google Scholar 

  30. Yousri D, AbdelAty AM, Said LA, Elwakil AS, Maundy B, Radwan AG (2019) Chaotic flower pollination and grey wolf algorithms for parameter extraction of bio-impedance models. Appl Soft Comput J 75:750–774

    Article  Google Scholar 

  31. Ewees AA, Abd El Aziz M, Hassanien AE (2019) Chaotic multi-verse optimizer-based feature selection. Neural Comput Appl 31(4):991–1006

    Article  Google Scholar 

  32. Zawbaa HM, Emary E, Grosan C (2016) Feature selection via chaotic antlion optimization. PLoS ONE 11(3):e0150652. https://doi.org/10.1371/journal.pone.0150652

    Article  Google Scholar 

  33. Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    Article  MathSciNet  Google Scholar 

  34. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98

    Article  MathSciNet  MATH  Google Scholar 

  35. Mirjalili S, Gandomi AH (2017) Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 53:407–419

    Article  Google Scholar 

  36. Mingjun J, Huanwen T (2004) Application of chaos in simulated annealing. Chaos Solut Fractals 21(4):933–941

    Article  MATH  Google Scholar 

  37. Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699

    MATH  Google Scholar 

  38. Adarsh BR, Raghunathan T, Jayabarathi T, Yang XS (2016) Economic dispatch using chaotic bat algorithm. Energy 96:666–675

    Article  Google Scholar 

  39. Lu P, Zhou J, Zhang H, Zhang R, Wang C (2014) Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. Int J Electr Power Energy Syst 62:130–143

    Article  Google Scholar 

  40. Secui DC (2015) A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manag 89:43–62

    Article  Google Scholar 

  41. Shayeghi H, Ghasemi A (2014) A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch. Energy Convers Manag 79:344–354

    Article  Google Scholar 

  42. Mukherjee A, Mukherjee V (2016) Chaotic krill herd algorithm for optimal reactive power dispatch considering FACTS devices. Appl Soft Comput 44:163–190

    Article  Google Scholar 

  43. Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: International conference on computer and information application (ICCIA 2010), pp 374–377

  44. Singh RP, Mukherjee V, Ghoshal SP (2015) Particle swarm optimization with an aging leader and challengers algorithm for optimal power flow problem with FACTS devices. Int J Electr Power Energy Syst 64:1185–1196

    Article  Google Scholar 

  45. Prasad D, Mukherjee V (2018) Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating FACTS devices. IETE J Res 64(1):149–160

    Article  Google Scholar 

  46. Elmitwally A, Eladl A (2016) Planning of multi-type FACTS devices in restructured power systems with wind generation. Int J Electr Power Energy Syst 77:33–42

    Article  Google Scholar 

  47. Banu RN, Devaraj D (2008) Genetic algorithm approach for optimal power flow with FACTS devices. In: 4th International IEEE conference intelligent systems

  48. Cai LJ, Erlich I, Stamtsis G (2004) Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms. In: IEEE PES power systems conference and exposition

  49. Biswas PP, Suganthan PN, Amaratunga GAJ (2017) Optimal power flow solutions incorporating stochastic wind and solar power. Energy Convers Manag 148:1194–1207

    Article  Google Scholar 

  50. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: International conference on neural network, pp 1942–1948

  51. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  52. Wang X, Wang M (2008) A hyperchaos generated from Lorenz system. Physica A 387(14):3751–3758

    Article  MathSciNet  Google Scholar 

  53. Wang X, Wang M (2007) Dynamic analysis of the fractional-order Liu system and its synchronization. Chaos 17:033106. https://doi.org/10.1063/1.2755420

    Article  MATH  Google Scholar 

  54. Ying-Qian Z, Xing-Yuan W (2014) A symmetric image encryption algorithm based on mixed linear-nonlinear coupled map lattice. Inf Sci 273:329–351

    Article  Google Scholar 

  55. Ying-Qian Z, Xing-Yuan W (2015) A new image encryption algorithm based on non-adjacent coupled map lattices. Appl Soft Comput 26:10–20

    Article  Google Scholar 

  56. 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 81:64–77

    Article  Google Scholar 

  57. IEEE 30-bus test system data. http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm. Accessed 5 July 2019

  58. IEEE 57-bus test system data. https://www2.ee.washington.edu/research/pstca/pf57/ieee57cdf.txt. Accessed 5 July 2019

  59. Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE swarm intelligence symposium (SIS 2005)

  60. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  61. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  62. Zimmerman RD, Murillo-Sanchez CE, Thomas RJ (2011) MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 26(1):12–19

    Article  Google Scholar 

  63. MATPOWER. https://matpower.org/. Accessed 5 July 2019

  64. Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18

    Article  Google Scholar 

  65. Gibbons JD, Chakraborti S (2010) Nonparametric statistical inference, 5th edn. Chapman & Hall, London

    Book  MATH  Google Scholar 

  66. Buch H, Trivedi IN, Jangir P (2017) Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation. Cogent Eng 4(1):1–22

    Article  Google Scholar 

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Acknowledgements

Dr. Serhat DUMAN would like to thank the support provided by Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB 2219 Postdoctoral Research Program under Application Number 1059B191700888.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Clarkson University, Potsdam, NY, 13699, USA

    Serhat Duman & Jie Li

  2. Electrical and Electronics Engineering, Technology Faculty, Duzce University, 81600, Duzce, Turkey

    Serhat Duman & Ugur Guvenc

  3. Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Lei Wu

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  1. Serhat Duman
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Duman, S., Li, J., Wu, L. et al. Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach. Neural Comput & Applic 32, 8463–8492 (2020). https://doi.org/10.1007/s00521-019-04338-y

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