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Wake management based life enhancement of battery energy storage system for hybrid wind farms

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  • Dhiman, Harsh S.
  • Deb, Dipankar

Abstract

One way of setting up hybrid wind farms is through augmentation of turbines with battery energy storage systems (BESS). Due to variation in wind speed in such wind farms, the cost of BESS increases and reduction in battery life takes place. This paper proposes a wake management technique to reduce the operational cost of a hybrid wind farm equipped with BESS. The battery charging and discharging powers are ascertained considering error in the wind speed prediction. This investigation considers three different conditions for a wind farm, namely, (i) without wake (ii) without wake management and (iii) with wake management. An ageing model is utilized for the lead-acid battery while accounting for temperature and depth of discharge changes to assess the battery operational cost and lifecycle count. Simulation analysis for a two-turbine layout with the upstream turbine yawed from 0∘to 5∘, presents a 44.37% savings in operational cost and 79.74% increase in battery life evaluated for a dataset obtained from Challicum hills in Australia. Additionally, the battery operational cost is minimized at a yaw angle of 15°. Uncertainty analysis is done to study the effect of prediction technique on the lifecycle count. The proposed methodology is extended to a 5-turbine layout where along with lifecycle count and operational cost, the horizontal shear on the turbine blades is also analyzed.

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  • Dhiman, Harsh S. & Deb, Dipankar, 2020. "Wake management based life enhancement of battery energy storage system for hybrid wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    • Handle: RePEc:eee:rensus:v:130:y:2020:i:c:s1364032120302033
    DOI: 10.1016/j.rser.2020.109912
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    3. Asmamaw Sewnet & Baseem Khan & Issaias Gidey & Om Prakash Mahela & Adel El-Shahat & Almoataz Y. Abdelaziz, 2022. "Mitigating Generation Schedule Deviation of Wind Farm Using Battery Energy Storage System," Energies, MDPI, vol. 15(5), pages 1-26, February.
    4. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    5. Fahd A. Alturki & Emad Mahrous Awwad, 2021. "Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems," Energies, MDPI, vol. 14(2), pages 1-20, January.
    6. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.

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