A Novel Hybrid Multi-Objective Evolutionary Algorithm for Optimal Power ‎Flow in Wind, PV, and PEV Systems ‎

Document Type : Research paper

Authors

Department of Electrical Engineering, National Institute of Technology Warangal,Telangana, 506004, India

Abstract

In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total cost (TC), total emission (TE), active power loss (APL), and voltage magnitude deviation (VMD) as objectives and a novel constraint handling method, which adaptively adds the penalty function and eliminates the parameter dependence on penalty function evaluation is deployed to handle several constraints in the MOOPF problem. In addition, summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of MOEA. Further, a fuzzy min-max method is utilized to get the best-compromised values from Pareto-optimal solutions. The impact of intermittence of Wind, PV, and PEVs integration is considered for optimal cost analysis. The uncertainty associated with Wind, PV, and PEV systems are represented using probability distribution functions (PDFs) and its uncertainty cost is calculated using the Monte-Carlo simulations (MCSs). A commonly used statistical method called the ANOVA test is used for the comparative examination of several methods. To test the proposed algorithm, standard IEEE 30, 57, and 118-bus test systems were considered with different cases and the acquired results were compared with NSGA-II and MOPSO to validate the suggested algorithm's effectiveness

Keywords

References

  1. Biswas et al., “Multi-objective economic-environmental power dispatch with stochastic wind-solar-small hydro power”, Energy, vol.150, pp.1039-1057, 2018.
  2. Molzahm, “Identifying and characterizing non-convexities in feasible spaces of optimal power flow problems”, IEEE Trans. Circuits Syst. II., vol. 65(5), pp. 672-676, 2018.
  3. Wu et al., “A deterministic method to identify multiple local extreme for the AC optimal power flow problem”, IEEE Trans. Power Syst., vol. 33(1), pp. 654-668, 2018.
  4. Dommel, W. Tinney, “Optimal power flow solutions”, IEEE Trans. Power Appar. Syst., vol.87, pp.1866-1876, 1968.
  5. Lin et al., “A hybrid current-power optimal power flow technique”, IEEE Trans. Power Syst., vol. 23(1), pp.177-185, 2008.
  6. Yan et al., “A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method”, IEEE Trans. Power Syst., vol. 21(1), pp.61-67, 2006.
  7. Duman et al., “Symbiotic organisms search algorithm-based security-constrained AC-DC OPF regarding uncertainty of wind, PV and PEV systems”, Soft Comput., vol. 25, pp. 9389-9426, 2021.
  8. Sarda et al., “Dynamic optimal power flow with cross entropy covariance matrix adaption evolutionary strategy for systems with electric vehicles and renewable generators”, Int J Energy Res., pp. 1-13, 2021.
  9. Duman, J. Li, L. Wu, “AC optimal power flow with thermal-wind-solar-tidal systems using the symbiotic organisms search algorithm”, IET Renew. Power Gener., vol. 15, pp. 278-296, 2021.
  10. Ben, T. Chambers and J. Lee, “Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming”, Elect. Power Syst. Research., vol.177, p.105989, 2019.
  11. Thorat, I. Korachgaon and A. Mulla, “Optimization of fuel cost incorporating with wind, solar PV and Electric vehicle energy sources using improved artificial bee colony algorithm”, Int. J. Electr. Engg. Tech. (IJEET), vol. 12(6), pp. 19-18, 2021.
  12. Nusair and F. Alasali, “Optimal power flow management system for a power network with stochastic renewable energy resources using golden ratio optimization method”, Energies. vol.13, 2020.
  13. Sulaiman and Z. Mustaffa, “Solving optimal power flow problem with stochastic wind-solar-small hydro power using barnacles mating optimizer”, Control Eng. Practice., vol. 106, 2021.
  14. Pandya and H. Jariwala, “Single and Multi-objective Optimal Power Flow with Stochastic Wind and Solar power Plants Using Moth Flame Optimization Algorithm”, Smart Sci., 2021.
  15. Bouchekara, “Solution of the optimal power flow problem considering security constraint using an improved chaotic electromagnetic field optimization algorithm”, Neural Comput. Appl. vol. 32, pp. 2683-2703, 2020.
  16. Biswas et al., “Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques”, Eng. Appl. Artificial Intell., vol. 68, pp. 81-100, 2018.
  17. Alasali et al., “An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources”, Int. Trans. Electr. Energ. Syst., 2021.
  18. Bhattacharya and P. Roy, “Solution of multi-objective optimal power flow using gravitational search algorithm”. IET Gener. Transm. Distrib., vol. 6, pp. 751-763, 2012.
  19. Jasemi and H. Abdi, “Probabilistic Multi-Objective Optimal Power Flow in an AC/DC hybrid microgird considering emission cost, J. Oper. Autom. Power Eng., vol. 10, pp. 13-27, 2022.
  20. Dehghani et al., “FOA: following’ optimization algorithm for solving power engineering optimization problems”, J. Oper. Autom. Power Eng., vol.8 (1), pp. 57-64, 2020.
  21. Li, W. Gong, L. Wang and Q. Gu. “Multi-objective optimal power flow with stochastic wind and solar power”. Appl. Soft Comput. vol.114, 2022.
  22. Reddy, “Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system”, Int. Journal of Green Energy, vol.16, pp.1547-1561, 2019.
  23. Effatnejad et al., “Solving multi-objective optimal power flow using modified GA and PSO based on hybrid algorithm”, J. Oper. Autom. Power Eng., vol. 5(1), pp.51-60, 2017.
  24. Morshed, J. Hmida and A. Fekih, “A probabilistic multi-objective approach for optimal power flow optimization in hybrid wind-PV-PEV systems”. Appl. Energy, vol.211, pp. 1136-1149, 2018.
  25. Galvani et al., “A multi-objective probabilistic approach for smart voltage control in wind-energy integrated networks considering correlated parameters”, Sustain. Cities Soc., vol.78, p.103651, 2022.
  26. Sandya, P. Jangir and I. Trivedi, “Multi-objective Moth Flame Optimizer: A Fundamental visions for wind power integrated optimal power flow with FACTS devices”, Smart Sci., 2021.
  27. Alvarez, “Stochastic optimization considering the uncertainties in the electricity demand, natural gas infrastructures, photovoltaic units, and wind generation”, Comput. Chem. Eng., vol.160, p.107712, 2022.
  28. Bakhtiari et al., “ Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based micro grid using Metropolis-coupled Markov chain Monte Carlo simulation”, Appl. Energy, vol. 290, p.116719, 2021.
  29. Li et al., “An evolutionary many-objective optimization algorithm based on dominance and decomposition”, IEEE Trans. Evol. Comput, vol. 19(5), pp. 694-716, 2015.
  30. Mishra et al., “Optimal power flow in the presence of wind power using modified cuckoo search”, IET Gener. Transm. Distrb, vol. 9(7), pp.615-626, 2015.
  31. Biswas et al., “Optimal power flow solutions incorporating stochastic wind and solar power”, Energy Convers. Manage, vol.148, pp.1194-1207, 2017.
  32. Sulaiman et al., “Optimal power flow with stochastic solar power using barnacles mating optimizer”, Int. Trans. Electr. Energy Syst., vol. 66, pp. 88-93, 2021.
  33. Zhao et al., “Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization”, IEEE Trans. Indus. Inform., vol. 8(4), pp. 889-899, 2012.
  34. Hanemann et al., “Effects of electric vehicle charging strategies on the German power system”, Appl. Energy. vol. 203, pp.608-622, 2017.
  35. Wu et al., “Coordinated energy dispatching in micro grid with wind power generation and plug-in electric vehicles”. IEEE Trans. Smart Grid. vol.4, pp.1453-63, 2013.
  36. Deb. “An efficient constraint handling method for genetic algorithms”, Comput. Methods Appl. Mech. Engrg., vol.186, pp.311-338, 2000.
  37. Li et al., “An evolutionary many-objective optimization algorithm based on dominance and decomposition”. IEEE Trans. Evol. Comput., vol. 19(5), pp.694-716, 2015.
  38. Zhang and H. Li, “MOEA/D: A multi objective evolutionary algorithm based on decomposition”, IEEE Trans. Evol. Comput., vol.11(6), pp.712-731, 2007.
  39. Qu and P. Suganthan, “Multi-objective differential evolution based on the summation of normalized objectives and improved selection method”, IEEE Symp., pp.1-8, 2011.
  40. Deb et al., “A fast and elitist multi objective genetic algorithm: NSGA-II”, IEEE Trans. Evol. Comput., vol.6(2), pp.182-197, 2002.
  41. Coello, G. Pulido, and M. Lechuga. “Handling multiple objectives with particle swarm optimization”, IEEE Trans. Evol. Comput., vol. 8(3), pp.256-279, 2004.
  42. Das and J. Dennis. “Normal–boundary intersection: A new method for generating Pareto optimal points in multi-criteria optimization problems”, SIAM J. Optimiz., vol. 8(3), pp.631-657, 1998.
  43. Zhang and Y. Li. “A many-objective evolutionary algorithm based on decomposition and local dominance”, 2018.
  44. Qu and P. Suganthan, “Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection”, Inf. Sci., vol.180 (17), pp.3170-3181, 2010.
  45. Zimmerman et al., “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education”, IEEE Trans. Power Syst., vol. 26, pp. 9-12, 2011.
Journal of Operation and Automation in Power Engineering
Volume 11, Issue 2
August 2023
Pages 130-143

Files

History

  • Receive Date: 12 March 2022
  • Revise Date: 31 March 2022
  • Accept Date: 02 May 2022
  • First Publish Date: 08 June 2022

Share

How to cite

Statistics