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Using Atmospheric Pressure Tendency to Optimise Battery Charging in Off-Grid Hybrid Wind-Diesel Systems for Telecoms

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  • Shane Phelan

    (School of Electronic Engineering, Dublin City University, Dublin 9, Ireland)

  • Paula Meehan

    (School of Electronic Engineering, Dublin City University, Dublin 9, Ireland)

  • Stephen Daniels

    (School of Electronic Engineering, Dublin City University, Dublin 9, Ireland)

Abstract

Off grid telecom base stations in developing nations are powered by diesel generators. They are typically oversized and run at a fraction of their rated load for most of their operating lifetime. Running generators at partial load is inefficient and, over time, physically damages the engine. A hybrid configuration uses a battery bank, which powers the telecoms’ load for a portion of the time. The generator only operates when the battery bank needs to be charged. Adding a wind turbine further reduces the generator run hours and saves fuel. The generator is oblivious to the current wind conditions, which leads to simultaneous generator-wind power production. As the batteries become charged by the generator, the wind turbine controller is forced to dump surplus power as heat through a resistive load. This paper details how the relationship between barometric pressure and wind speed can be used to add intelligence to the battery charger. A Simulink model of the system is developed to test the different battery charging configurations. This paper demonstrates that if the battery charger is aware of upcoming wind conditions, it will provide modest fuel savings and reduce generator run hours in small-scale hybrid energy systems.

Suggested Citation

  • Shane Phelan & Paula Meehan & Stephen Daniels, 2013. "Using Atmospheric Pressure Tendency to Optimise Battery Charging in Off-Grid Hybrid Wind-Diesel Systems for Telecoms," Energies, MDPI, vol. 6(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:6:p:3052-3071:d:26579
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    References listed on IDEAS

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    1. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimal sizing of small wind/battery systems considering the DC bus voltage stability effect on energy capture, wind speed variability, and load uncertainty," Applied Energy, Elsevier, vol. 93(C), pages 404-412.
    2. Saheb-Koussa, D. & Haddadi, M. & Belhamel, M., 2009. "Economic and technical study of a hybrid system (wind-photovoltaic-diesel) for rural electrification in Algeria," Applied Energy, Elsevier, vol. 86(7-8), pages 1024-1030, July.
    3. Kaldellis, J.K., 2010. "Optimum hybrid photovoltaic-based solution for remote telecommunication stations," Renewable Energy, Elsevier, vol. 35(10), pages 2307-2315.
    4. Zhou, Li, 2005. "Progress and problems in hydrogen storage methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 9(4), pages 395-408, August.
    5. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    6. Hu, Yu & Solana, Pablo, 2013. "Optimization of a hybrid diesel-wind generation plant with operational options," Renewable Energy, Elsevier, vol. 51(C), pages 364-372.
    7. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    8. Jing Li & Wei Wei & Ji Xiang, 2012. "A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids," Energies, MDPI, vol. 5(12), pages 1-17, December.
    9. Bajpai, Prabodh & Dash, Vaishalee, 2012. "Hybrid renewable energy systems for power generation in stand-alone applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2926-2939.
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    Cited by:

    1. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.

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