10.1007/s40095-021-00397-x

Multi-objective optimal power flow using a new heuristic optimization algorithm with the incorporation of renewable energy sources

  1. Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, IN
  2. School of Electrical Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamilnadu, IN
  3. Department of Electrical and Electronics Engineering, Agni College of Technology, Chennai, Chennai, Tamilnadu, IN

Published in Issue 2021-06-11

How to Cite

Karthik, N., Parvathy, A. K., Arul, R., & Padmanathan, K. (2021). Multi-objective optimal power flow using a new heuristic optimization algorithm with the incorporation of renewable energy sources. International Journal of Energy and Environmental Engineering, 12(4 (December 2021). https://doi.org/10.1007/s40095-021-00397-x

Abstract

Abstract The current research study proposes a multi-objective optimal power flow (OPF) solution using a modified Interior Search Algorithm in which Levy Flight feature with two different strategies is incorporated to accelerate the convergence speed and to enhance solution quality. In traditional OPF problems, the thermal generation units alone are accounted, whereas the security challenges faced by the network are mostly ignored. In other terms, the emission needs to be significantly reduced in terms of environmental sustainability aspects. So, the electrical grid must be infused with power generated from different renewable energy sources. Consequently, the current research article proposes an approach in order to accomplish OPF through a combination of stochastic wind and solar power coupled with traditional thermal power generators in the system. The authors leveraged modified IEEE 30-bus system, IEEE 118-bus system and real-time electrical network 62-bus Indian Utility System in order to validate the Levy Interior Search Algorithm proposed in the study by incorporating renewable energy sources. During implementation, the researchers considered different factors such as network security limitations, for instance transmission line capacity, bus voltage limits and restricted operation zones for thermal units. The simulation results obtained using the proposed LISA Strategy-II algorithm are compared with the results obtained using LISA Strategy-I, ISA and other optimization algorithms reported in the literature. The results achieved from the implementation infer that the proposed method has inherently good convergence characteristic and affords better exploration of the Pareto front.

Keywords

  • Levy interior search algorithm,
  • Optimal power flow,
  • Multi-objective optimization,
  • Emission,
  • Renewable energy sources,
  • Probability density function

References

  1. Mojica-Nava et al. (2017) Game-theoretic dispatch control in microgrids considering network losses and renewable distributed energy resources integration 11(6) (pp. 1583-1590) https://doi.org/10.1049/iet-gtd.2016.1486
  2. Lu, X., Liu, N., Chen, Q., Zhang, J.: Multi-objective optimal scheduling of a DC micro-grid consisted of PV system and EV charging station. In: 2014 IEEE Innovative Smart Grid Technologies—Asia (ISGT ASIA), Kuala Lumpur, Malaysia, 20–23 May, (2014)
  3. Frank and Rebennack (2016) An introduction to optimal power flow: theory formulation, and examples 48(12) (pp. 1172-1197) https://doi.org/10.1080/0740817X.2016.1189626
  4. Abdi (2017) Soheil Derafshi Beigvand, Massimo La Scala, A review of optimal power flow studies applied to smart grids and microgrids 71(1) (pp. 742-766) https://doi.org/10.1016/j.rser.2016.12.102
  5. Samakpong et al. (2020) Optimal power flow incorporating renewable uncertainty related opportunity costs https://doi.org/10.1111/coin.12316
  6. Abbasi et al. (2020) Single and multi-objective optimal power flow using a new differential-based harmony search algorithm https://doi.org/10.1007/s12652-020-02089-6
  7. Surender Reddy and Bijwe (2016) Multi-objective optimal power flow using efficient evolutionary algorithm 18(2) https://doi.org/10.1515/ijeeps-2016-0233
  8. Arul et al. (2013) Solving optimal power flow problems using chaotic self-adaptive differential harmony search algorithm (pp. 782-805) https://doi.org/10.1080/15325008.2013.769033
  9. Biswas et al. (2020) Multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms (pp. 2999-3023) https://doi.org/10.1007/s00500-019-04077-1
  10. Bai et al. (2017) An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem (pp. 163-172) https://doi.org/10.1016/j.conengprac.2017.02.010
  11. Hmida et al. (2019) Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming https://doi.org/10.1016/j.epsr.2019.105989
  12. Hmida et al. (2018) Hybrid imperialist competitive and grey wolf algorithm to solve multi-objective optimal power flow with wind and solar units 11(11) https://doi.org/10.3390/en11112891
  13. Chen et al. (2020) Application of modified pigeon-inspired optimization algorithm and constraint-objective sorting rule on multi-objective optimal power flow problem https://doi.org/10.1016/j.asoc.2020.106321
  14. Panda et al. (2020) Hybrid power systems with emission minimization: multi-objective optimal operation https://doi.org/10.1016/j.jclepro.2020.121418
  15. Hu et al. (2017) Combined economic and emission dispatch considering conventional and wind power generating units 27(12) https://doi.org/10.1002/etep.2424
  16. Naidji and Boudour (2020) Stochastic multi-objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system 30(6) https://doi.org/10.1002/2050-7038.12374
  17. Sharifzadeh and Amjady (2016) Stochastic security-constrained optimal power flow incorporating preventive and corrective actions 26(11) https://doi.org/10.1002/etep.2207
  18. Taher, M.A., Kamel, S., Jurado, F., Ebeed, M.: An improved moth‐flame optimization algorithm for solving optimal power flow problem. Int. Trans. Electr. Energy Syst.
  19. 29
  20. (3), e2743 (2018)
  21. Li, S., Gong, W., Wang, L., Yan, X., Hu, C.: Optimal power flow by means of improved adaptive differential evolution. Energy
  22. 198
  23. (1), 117314 (2020)
  24. Kahourzade et al. (2015) A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm (pp. 1-12) https://doi.org/10.1007/s00202-014-0307-0
  25. Ye and Huang (2015) Multi-objective optimal power flow considering transient stability based on parallel NSGA-II 30(2) (pp. 857-866) https://doi.org/10.1109/TPWRS.2014.2339352
  26. Gandomi, A.H.: Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans.
  27. 53
  28. (4), 1168–1183 (2014)
  29. Karthik et al. (2019) Multi-objective economic emission dispatch using interior search algorithm https://doi.org/10.1002/etep.2683
  30. Karthik, N., Parvathy, A.K., Arul, R., Padmanathan, K.: Economic load dispatch in a microgrid using interior search algorithm. In: International Conference on Power and advanced computing, i-PACT 2019 (2019)
  31. Biswas et al. (2017) Optimal power flow solutions incorporating stochastic wind and solar power 148(1) (pp. 1194-1207) https://doi.org/10.1016/j.enconman.2017.06.071
  32. Abdullah, M., Javaid, N., Khan, I.U., Khan, Z.A., Chand, A., Ahmad, N.: Optimal power flow with uncertain renewable energy sources using flower pollination algorithm. In: Advances in Intelligent Systems and Computing, pp. 95–107 (2020)
  33. Abdullah, M., Javaid, N., Chand, A., Khan, Z.A., Waqas, M., Abbas, Z.: Multi-objective optimal power flow using improved multi-objective multi-verse algorithm. In: Advances in Intelligent Systems and Computing, pp. 1071–1093 (2019)
  34. Biswas et al. (2018) Multiobjective economic-environmental power dispatch with stochastic wind-solar small hydro power 150(1) (pp. 1039-1057) https://doi.org/10.1016/j.energy.2018.03.002
  35. Chang (2010) Investigation on frequency distribution of global radiation using dierent probability density functions 8(2) (pp. 99-107)
  36. Surender et al. (2014) Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period 9(4) (pp. 1440-1451) https://doi.org/10.1109/JSYST.2014.2325967
  37. Never (2011) Flood frequency analysis using the Gumbel distribution 3(7)
  38. Pieter (2008) River flow prediction through rainfall runoff modelling with a probability-distributed model (PDM) in Flanders, Belgium 95(7)
  39. Gnanadass et al. (2004) Assessment of available transfer capability for practical power systems with combined economic emission dispatch (pp. 267-276) https://doi.org/10.1016/j.epsr.2003.10.007
  40. Yang (2010) Wiley https://doi.org/10.1002/9780470640425
  41. Mandal and Kumar Roy (2014) Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization https://doi.org/10.1016/j.asoc.2014.04.010
  42. Duman et al. (2019) Optimal power flow of power systems with controllable wind-photovoltaic energy systems via differential evolutionary particle swarm optimization
  43. Yao et al. (2012) Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia 8(4) (pp. 880-888) https://doi.org/10.1109/TII.2012.2210431
  44. Man-Im et al. (2019) Multi-objective optimal power flow considering wind power cost functions using enhanced PSO with chaotic mutation and stochastic weights 101(1) (pp. 699-718) https://doi.org/10.1007/s00202-019-00815-8
  45. Yang and Deb (2013) Multi-objective cuckoo search for design optimization 40(6) (pp. 1616-1624) https://doi.org/10.1016/j.cor.2011.09.026
  46. IEEE 118-bus test system data
  47. http://labs.ece.uw.edu/pstca/pf118/pg_tca118bus.htm
  48. Zimmerman et al. (2011) MATPOWER: Steady-State operations, planning, and analysis tools for power systems research and education, power systems 26(1) (pp. 12-19) https://doi.org/10.1109/TPWRS.2010.2051168
  49. MATPOWER
  50. http://www.pserc.cornell.edu/matpower/
  51. Gnanadass et al. (2004) evolutionary programming based optimal power flow for units with non-smooth fuel cost functions 33(3) (pp. 349-361) https://doi.org/10.1080/15325000590474708
  52. Hakli and Uguz (2014) A novel particle swarm optimization algorithm with Levy flight 23(1) (pp. 333-345) https://doi.org/10.1016/j.asoc.2014.06.034
  53. Chechkin et al. (2008) Introduction to the theory of levy flights (pp. 129-162) Wiley https://doi.org/10.1002/9783527622979.ch5