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A modified bacteria foraging based optimal power flow framework for Hydro-Thermal-Wind generation system in the presence of STATCOM

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  • Panda, Ambarish
  • Tripathy, M.
  • Barisal, A.K.
  • Prakash, T.

Abstract

Considering the importance of clean energy, the combined operation of hydro-thermal-wind (HTW) system is formulated in optimal power flow (OPF) framework. The objective is to find an optimal generation schedule for the HTW system where the system will work economically and in a voltage secure manner with reduced loss during normal as well as stressed system operation. As system voltage may be vulnerable especially during under estimation (UE) situation, provision of additional reactive power (Q) support is essential as a possible solution. This is achieved by installing shunt facts devices i.e. (STATCOM) at the weak nodes of the power network. A comparative assessment between wind-thermal (WT) and HTW system operation with STATCOM at different wind penetration levels is also depicted. The optimum operational paradigms are obtained by optimizing the objective with Genetic Algorithm (GA), Hybrid Algorithm (HA) and modified bacteria foraging algorithm (MBFA). After several tests, superiority of MBFA optimization over HA and GA is revealed so that the IEEE30-bus system operates in a voltage secure and cost-effective manner.

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  • Panda, Ambarish & Tripathy, M. & Barisal, A.K. & Prakash, T., 2017. "A modified bacteria foraging based optimal power flow framework for Hydro-Thermal-Wind generation system in the presence of STATCOM," Energy, Elsevier, vol. 124(C), pages 720-740.
    • Handle: RePEc:eee:energy:v:124:y:2017:i:c:p:720-740
    DOI: 10.1016/j.energy.2017.02.090
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    References listed on IDEAS

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    Cited by:

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    3. Elattar, Ehab E. & ElSayed, Salah K., 2019. "Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement," Energy, Elsevier, vol. 178(C), pages 598-609.
    4. Fang, Xin & Hodge, Bri-Mathias & Du, Ershun & Zhang, Ning & Li, Fangxing, 2018. "Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach," Applied Energy, Elsevier, vol. 230(C), pages 531-539.
    5. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    6. Ji, Bin & Zhang, Binqiao & Yu, Samson S. & Zhang, Dezhi & Yuan, Xiaohui, 2021. "An enhanced Borg algorithmic framework for solving the hydro-thermal-wind Co-scheduling problem," Energy, Elsevier, vol. 218(C).
    7. Panda, Ambarish & Mishra, Umakanta & Aviso, Kathleen B., 2020. "Optimizing hybrid power systems with compressed air energy storage," Energy, Elsevier, vol. 205(C).
    8. Wang, Xuebin & Chang, Jianxia & Meng, Xuejiao & Wang, Yimin, 2018. "Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems," Applied Energy, Elsevier, vol. 229(C), pages 945-962.
    9. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    10. 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.
    11. 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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    13. Fang, Xin & Hodge, Bri-Mathias & Jiang, Huaiguang & Zhang, Yingchen, 2019. "Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow," Applied Energy, Elsevier, vol. 239(C), pages 938-947.

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