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Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm

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  • Panda, Ambarish
  • Tripathy, M.

Abstract

In this work the variability of WP (wind power) has been suitably modelled and incorporated with the thermal generating units. The goal is to operate the wind-thermal generation system in a cost effective manner while maintaining a voltage secure operation with reduction in system loss. These objectives have been formulated in an OPF (optimal power flow) framework. As the wind generation cost model is subjected to intermittent WP, the voltage security aspect is considered during both UE (under estimation) and OE (over estimation) of available WP. This is achieved by suitably incorporating shunt facts devices (STATCOM) to provide reactive power (Q) support during UE scenario and maintaining a spinning reserve of thermal generators during OE scenario. To further utilize the Q-support, the DFIG (doubly fed induction generators) are used in the wind turbine. The combinations of optimum operational paradigms are obtained by optimizing the objective function with ACO (ant colony optimization) and MBFA (modified bacteria foraging algorithm). Finally, after performing several tests the superiority of MBFA optimized scenario over ACO is revealed so that the IEEE30-bus system operates in a voltage secured manner when subjected to N-1 contingencies.

Suggested Citation

  • Panda, Ambarish & Tripathy, M., 2015. "Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm," Energy, Elsevier, vol. 93(P1), pages 816-827.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p1:p:816-827
    DOI: 10.1016/j.energy.2015.09.083
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    References listed on IDEAS

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    10. Yuan Liu & Qinliang Tan & Jian Han & Mingxin Guo, 2021. "Energy-Water-Carbon Nexus Optimization for the Path of Achieving Carbon Emission Peak in China Considering Multiple Uncertainties: A Case Study in Inner Mongolia," Energies, MDPI, vol. 14(4), pages 1-21, February.
    11. Rambabu Muppidi & Ramakrishna S. S. Nuvvula & S. M. Muyeen & SK. A. Shezan & Md. Fatin Ishraque, 2022. "Optimization of a Fuel Cost and Enrichment of Line Loadability for a Transmission System by Using Rapid Voltage Stability Index and Grey Wolf Algorithm Technique," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    12. Mohamed Farhat & Salah Kamel & Ahmed M. Atallah & Mohamed H. Hassan & Ahmed M. Agwa, 2022. "ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem," Sustainability, MDPI, vol. 14(4), pages 1-33, February.
    13. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    14. Khaled Nusair & Lina Alhmoud, 2020. "Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy," Energies, MDPI, vol. 13(22), pages 1-35, November.
    15. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
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