Comparative Analysis Model for Lambda Iteration, Genetic Algorithm, and Particle Swarm Optimization-Based Economic Load Dispatch for Saudi Arabia Network System

1. Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford digital library metadata architecture. Int. J. Digit. Libr. 1, 108–121 (1997)

   Article  Google Scholar 

2. Pratap Nair, M., Nithiyananthan, K.: An effective cable sizing procedure model for industries and commercial buildings. Int. J. Electr. Comput. Eng. 6(1), 34–39 (2016)

   Google Scholar 

3. Warwick, W.M.: A Primer on Electric Utilities, Deregulation, and Restructuring of U.S. Electricity Markets. Pacific Northwest National Laboratory (2002)

   Google Scholar 

4. Vidhya, K.: An power efficient implementation of SDR using matlab and simulink with adaptive modulation and demodulation techniques. J. Green Eng. 10(9), 4584–4597 (2020)

   Google Scholar 

5. Blanco, M.I.: The economics of wind energy. Renew. Sustain. Energy Rev. 13(6–7), 1372–1382 (2009)

   Google Scholar 

6. Li, X.: Study of multi-objective optimization and multi-attribute decision making for economic and environmental power dispatch. Electr. Power Components Syst. 37(10) (2009)

   Google Scholar 

7. Sachan, A., Gupta, A.K.: A review of MPPT algorithms employed in wind energy conversion systems. J. Green Eng. 6(4), 385–402 (2017)

   Google Scholar 

8. Kalpana, N., Venu Gopala Rao, M.: Impact of crow search algorithm to minimize transmission system power losses. J. Green Eng. 10(12), 12851–12864 (2020)

   Google Scholar 

9. Faseela, C.K., Vennila, H.: Economic and emission dispatch using whale optimization algorithm. Int. J. Electr. Comput. Eng. (IJECE) 8, 1297–1304 (2018)

   Google Scholar 

10. Chen, F., Huang, G.H., Fan, Y.R., Liao, R.F.: A nonlinear fractional programming approach for environmental-economic power dispatch. Int. J. Electr. Power Energy Syst. 78, 463–469 (2016)

    Article  Google Scholar 

11. Touil, A., et al.: Economic and emission dispatch using cuckoo search algorithm. Int. J. Electr. Comput. Eng. 9, 3384–3390 (2019)

    Google Scholar 

12. Nithiyananthan, K., Mobarak, Y., Alharbi, F.: Application of cloud computing for economic load dispatch and unit commitment computations of the power system network. Adv. Intell. Syst. Comput. 1179–1189 (2020)

    Google Scholar 

13. Paulraj, D.: A gradient boosted decision tree-based sentiment classification of twitter data. Int. J. Wavelets Multiresolution Inf. Process. 18(4) (2020)

    Google Scholar 

14. Modiri-Delshad, M., Kaboli, S.H.A.: Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options. Energy 116, 637–649 (2016)

    Google Scholar 

15. Mobarak, Y.A., Kannan, N.: Vertically integrated utility power system structures for Egyptian scenario and electricity act. J. Adv Res. Dyn. Control Syst. 11(7), 766–772 (2019)

    Google Scholar 

16. Beigvand, S.D., Abdi, H., La Scala, M.: Combined heat and power economic dispatch problem using gravitational search algorithm. Electr. Power Syst. Res. 133, 160–172 (2016)

    Article  Google Scholar 

17. Krishnasamy, U., Nanjundappan, D.: Hybrid weighted probabilistic neural network and biogeography based optimization for dynamic economic dispatch of integrated multiple-fuel and wind power plants. Int. J. Electr. Power Energy Syst. 77, 385–394 (2016)

    Article  Google Scholar 

18. Zhang, Q., Zou, D., Duan, N., Shen, X.: An adaptive differential evolutionary algorithm incorporating multiple mutation strategies for the economic load dispatch problem. Appl. Soft. Comput. 78, 641–669 (2019)

    Article  Google Scholar 

19. Rao, R., Jaya, V.: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7(1), 19–34 (2016)

    Google Scholar 

20. Awad, M., Tawila, M., Alnakeeb, H.: The development of trans-border energy supply to the South Mediterranean Coast particularly electricity and natural gas. In: 8th World Congress, Sydney, Australia, 5–9 Sept 2004

    Google Scholar 

21. Nikmehr, N., Ravadanegh, S.: Optimal power dispatch of multimicrogrids at future smart distribution grids. IEEE Trans. Smart Grid 6(4), 1648–1657 (2015)

    Article  Google Scholar 

22. Neelakandan, S.: An automated exploring and learning model for data prediction using balanced CA-SVM. J. Ambient Intell. Human. Comput. 66 (2020)

    Google Scholar 

23. Al-Shetwi, A.Q., Alomoush, M.I.: A new approach to the solution of economic dispatch using genetic algorithm. J. Eng. Technol. 7(1), 40–48 (2016)

    Google Scholar 

24. Kavitha Priya, C.J.: An Analysis of types of protocol implemented in internet of things based on packet loss ratio. In: Proceedings of International Conference of Information and Communication Technology for Competitive Strategies, pp. 1–6 (2016)

    Google Scholar 

25. Bora, T., Mariani, V., Coelho, L.: Multiobjective optimization of the environmental economic dispatch with reinforcement learning based on non-dominated sorting genetic algorithm. Appl. Therm. Eng. 146, 688–700 (2019)

    Article  Google Scholar 

26. Subbulakshmi, P., Ramalakshmi, V.: Honest auction based spectrum assignment and exploiting spectrum sensing data falsification attack using stochastic game theory in wireless cognitive radio network. Wirel. Pers. Commun. Int. J. 102(2), 799–816 (2018)

    Article  Google Scholar 

27. Cheng, Y.-S., et al.: A particle swarm optimization-based power dispatch algorithm with roulette wheel redistribution mechanism for equality constraint. Renew. Energy 88, 58–72 (2016)

    Google Scholar 

28. Thangavel, R.: Resource selection in grid environment based on trust evaluation using feedback and performance. Am. J. Appl. Sci. 10(8), 924–930 (2013)

    Google Scholar 

29. La, C., Balachander, K.: Design of power system stabilizer for multi-machine systems using modified swarm optimization algorithm with wind energy generation. J. Green Eng. 11(1), 156–178 (2021)

    Google Scholar 

30. Zhang, H.: An improved particle swarm optimization algorithm for dynamic economic dispatch problems. Int. J. Innov. Res. Eng. Manage. (IJIREM) 3(4), 264–266 (2016)

    Google Scholar 

31. Gholamghasemi, M., Akbari, E., Asadpoor, M.B., Ghasemi, M.: A new solution to the non-convex economic load dispatch problems using phasor particle swarm optimization. Appl. Soft. Comput. 79, 111–124 (2019)

    Article  Google Scholar 

32. Lotfi, H., Dadpour, A., Samadi, M.: Solving economic dispatch in competitive power market using improved particle swarm optimization algorithm. Int. J. Smart Electr. Eng. 6(1), 35–41 (2017)

    Google Scholar 

33. Jadoun, V.K., Gupta, N., Niazi, K.R., Swarnkar, A.: Modulated particle swarm optimization for economic emission dispatch. Int. J. Electr. Power Energy Syst. 73, 80–88 (2015)

    Article  Google Scholar 

34. Low, S.H.: Optimal Power Flow for Future Smart Grid. Clean Energy Institute, University of Washington (2015)

    Google Scholar 

35. Ebeed, M., Kamel, S., Jurado, F.: Optimal power flow using recent optimization techniques. In: Classical and Recent Aspects of Power System Optimization, pp. 157–183 (2018)

    Google Scholar 

36. Keerthivasan, K., Pavan Kumar Reddy, Y.: A multilevel H-bridge cascaded using a single dc source inverter for automobile applications. Int. J. Innov. Sci. Eng. Res. (IJISER) 5(10), 93–98 (2018)

    Google Scholar 

Download references