Optimal Power Generation in Microgrid System Using Particle Swarm Optimization

  • M. N. AbdullahEmail author
  • N. F. A. Mohd Azlan
  • W. M. Dahalan
  • N. F. Naswan
  • R. Hamdan
  • M. N. Ismail
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


This paper presents an application of Particle Swarm Optimization (PSO) algorithm for minimizing the total generation cost in microgrid system within 24 h. The microgrid system consists of conventional and renewable energy power plants are considered in this project. The main objective is to minimize the generation cost while satisfied the load demand and system constraints. This case study consists of three fuel cells, two diesel generators and two wind turbines. The proposed PSO algorithm has been simulated in Matlab software to determine optimal generation cost. The results are compared with other existing algorithms to validate performances of PSO in term of minimizing generation cost in microgrid. It found that the PSO algorithm gives the lower optimal cost compared to other methods.


Generation scheduling Microgrid Particle swarm optimization Renewable energy 



The authors gratefully appreciate the Universiti Tun Hussein Onn Malaysia (UTHM) under Incentive Grant Scheme for Publication (U684) and Department of Marine Electrical and Electronic Technology, Universiti Kuala Lumpur, Malaysian Institute of Marine Engineering Technology for supporting this research work.


  1. 1.
    Toma L, Tristiu I, Bulac C, Neagoe-Stefana AG (2016) Optimal generation scheduling strategy in a microgrid. In: 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2016Google Scholar
  2. 2.
    Modiri-Delsha M, Rahim NA, Taheri SS, Seyed-Shenava SJ (2014) Optimal generation scheduling in microgrids by Cuckoo search algorithm. In: 3rd IET Int Conf Clean Energy Technol 10(5)Google Scholar
  3. 3.
    Wei D, Zheng D (2014) Optimal energy management strategy for an isolated industrial microgrid using a modified particle swarm optimization, pp 494–498Google Scholar
  4. 4.
    Karthikeyan A, Manikandan K, Somasundaram P (2016) Economic dispatch of microgrid with smart energy storage systems using particle swarm optimization. In: 2016 international conference on computation of power, energy information and communicationGoogle Scholar
  5. 5.
    Kumar KP, Saravanan B, Swarup KS (2016) Optimization of renewable energy sources in a microgrid using artificial fish swarm algorithm. Energy ProcediaGoogle Scholar
  6. 6.
    Kamboj A, Chanana S (2017) Optimization of cost and emission in a renewable energy micro-grid. In: 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)Google Scholar
  7. 7.
    Wu H, Liu X, Ding M (2014) Dynamic economic dispatch of a microgrid: mathematical models and solution algorithm. Int J Electr Power Energy SystGoogle Scholar
  8. 8.
    Modiri-delshad M, Koohi-kamali S, Taslimi E, Aghay SH (2013) Economic dispatch in a microgrid through an iterated-based algorithmGoogle Scholar
  9. 9.
    Li P, Zhou Z, Lin X, Yang X, Niu X (2014) Dynamic optimal operation scheduling of microgrid using binary gravitational search algorithmGoogle Scholar
  10. 10.
    Lee K, Park J (2006) Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. In: 2006 IEEE PES Power Systems Conference and ExpositionGoogle Scholar
  11. 11.
    Gaing Z (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst, 1187–1195CrossRefGoogle Scholar
  12. 12.
    Khan NA, Awan AB, Mahmood A, Razzaq S, Zafar A, Sidhu GAS (2015) Combined emission economic dispatch of power system including solar photo voltaic generation. Energy Convers ManagGoogle Scholar
  13. 13.
    Abdullah MN, Tawai R, Yousof MF (2017) Comparison of constraints handling methods for economic load dispatch problem using particle swarm optimization algorithm. Int J Adv Sci Eng Inf Technol 7:1322–1327CrossRefGoogle Scholar
  14. 14.
    Madi M, Markovi D, Radovanovi M (2013) Comparison of meta-heuristic algorithms for solving machining optimization problems. Facta Universitatis 11:29–44Google Scholar
  15. 15.
    Basu M, Chowdhury A (2013) Cuckoo search algorithm for economic dispatch. Energy 60:99–108CrossRefGoogle Scholar
  16. 16.
    Abdullah MN, Abdullah NL, Jamian JJ (2017) Optimal power generation in microgrid system based on firefly algorithm. In: 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI), pp 1–6Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • M. N. Abdullah
    • 1
    Email author
  • N. F. A. Mohd Azlan
    • 1
  • W. M. Dahalan
    • 2
  • N. F. Naswan
    • 1
  • R. Hamdan
    • 1
  • M. N. Ismail
    • 1
  1. 1.Green and Sustainable Energy (GSEnergy) Focus Group, Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  2. 2.Department of Marine Electrical and Electronic TechnologyUniversiti Kuala Lumpur, Malaysian Institute of Marine Engineering TechnologyLumutMalaysia

Personalised recommendations