Cost Driven Optimization of Microgrid Under Environmental Uncertainties Using Different Improved PSO Models

  • Meenakshi DeEmail author
  • G. Das
  • K. K. Mandal
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 302)


This paper presents a micro grid generation scheduling model using Non-linear Decreasing Inertia Weight Particle Swarm Optimization (NDIW-PSO) and Time Varying Acceleration Co-efficient Particle Swarm Optimization (TVAC-PSO) techniques. Here energy management in micro grid is done in presence of renewable energy sources such as wind and solar power. In this research work, implementation of Demand Response (DR) schedules are carried out as incentive based payment i.e., on offered price packages. In the typical microgrid, different power components including Wind Turbine (WT), Photovoltaic (PV) cell, Micro-Turbine (MT), Fuel Cell (FC), battery hybrid power source and responsive loads are used. Analytical approaches and case studies are conducted for obtaining minimum operating costs and comparative studies are carried out without demand response participation and with demand response participation respectively. The results obtained represent the superiority of the proposed approach for effective generation scheduling in micro grids.


Non-linear decreasing inertia weight particle swarm optimization Generation scheduling Demand response Micro grids 



The authors express gratitude towards the Department of Power Engineering, Jadavpur University for providing facilities for carrying this research work.


  1. 1.
    Jiayi, H., Chuanwen, J., Rong, X.: A review on distributed energy resources and micro grid. Int. J. Renew. Sustain. Energy Rev. 12(9), 2472–2483 (2008)CrossRefGoogle Scholar
  2. 2.
    Chowdhury, S., Crossley, P.: Microgrids and active distribution networks. The Institution of Engineering and Technology (2009)Google Scholar
  3. 3.
    Rouholamini, M., Mohammadian, M.: Energy management of a grid-tied residential-scale hybrid renewable generation system incorporating fuel cell and electrolyzer. J. Energy Build. 102, 406–16 (2015)CrossRefGoogle Scholar
  4. 4.
    Pandit, M., Srivastava, L., Sharma, M.: Environmental economic dispatch in multi area power system employing improved differential evolution with fuzzy selection. Appl. Soft Comput. 28, 498–510 (2015)CrossRefGoogle Scholar
  5. 5.
    Aghajani, R.G., Shayanfar, A.H., Shayeghi, H.: Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag. 106, 308–321 (2015)CrossRefGoogle Scholar
  6. 6.
    Jabbari-Sabet, R., Moghaddas-Tafreshi, S.M., Mirhoseini, S.S.: Microgrid operation and management using probabilistic reconfiguration and unit commitment. Electr. Power Energy Syst. 75, 328–336 (2016)CrossRefGoogle Scholar
  7. 7.
    Abido, M.A.: Optimal power flow using particle swarm optimization. Electr. Power Energy Syst. 24, 563–571 (2002)CrossRefGoogle Scholar
  8. 8.
    Chen, C., Duan, S., Cai, T., Liu, B., Hu, G.: Smart energy management system for optimal micro grid economic operation. IET Renew. Power Gener. 5(3), 258–267 (2011)CrossRefGoogle Scholar
  9. 9.
    Chongpeng, H., Yuling, Z., Dingguo, J., Baoguo, X.: On some non-linear decreasing inertia weight strategies in particle swarm optimization. In: Proceedings of the 26th Chinese Control Conference, Hunan, China, Zhangjiajie, pp. 570–753 (2007)Google Scholar
  10. 10.
    Imran, M., Hashima, R., Khalidb, Noor Elaiza Abd: An overview of particle swarm optimization variants. Procedia Eng. 53, 491–496 (2013)CrossRefGoogle Scholar
  11. 11.
    Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Power EngineeringJadavpur UniversityKolkataIndia

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