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An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system

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  • Jamal, Taskin
  • Carter, Craig
  • Schmidt, Thomas
  • Shafiullah, G.M.
  • Calais, Martina
  • Urmee, Tania

Abstract

One of the primary technical challenges of integrating high levels of PV generation into standalone off-grid power supply systems is their variable power output characteristics. In dealing with this issue, the integration of reliable PV forecasting techniques and preferably energy storage, are highly effective. Applying a short-term PV forecasting method, together with a compensatory controllable resource, can help in the management of system operation. This study incorporates the development of an energy flow modelling tool that has been used to analyse the benefits of 1-min ahead PV forecasting and battery storage for different system configurations. Based on the five days of 1-min ahead forecasting results analysed, it is found that PV forecasting enables the prosumer to install more than double the PV capacity, compared to the allowed installed PV capacity when no forecasting is employed. This additional PV capacity saves around 24–25% (on average) of diesel fuel per day for the diesel-PV-battery configuration. The outcomes evidently indicate that incorporating 1-min ahead PV forecasting enables a significant increase of PV hosting capacity of the system, without compromising the reliability of the system.

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  • Jamal, Taskin & Carter, Craig & Schmidt, Thomas & Shafiullah, G.M. & Calais, Martina & Urmee, Tania, 2019. "An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919314059
    DOI: 10.1016/j.apenergy.2019.113718
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    4. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    5. Mahmoud M. Gamil & Makoto Sugimura & Akito Nakadomari & Tomonobu Senjyu & Harun Or Rashid Howlader & Hiroshi Takahashi & Ashraf M. Hemeida, 2020. "Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs," Energies, MDPI, vol. 13(14), pages 1-22, July.
    6. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    7. Samar Fatima & Verner Püvi & Matti Lehtonen, 2020. "Review on the PV Hosting Capacity in Distribution Networks," Energies, MDPI, vol. 13(18), pages 1-34, September.
    8. Rodríguez-Benítez, Francisco J. & López-Cuesta, Miguel & Arbizu-Barrena, Clara & Fernández-León, María M. & Pamos-Ureña, Miguel Á. & Tovar-Pescador, Joaquín & Santos-Alamillos, Francisco J. & Pozo-Váz, 2021. "Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery," Applied Energy, Elsevier, vol. 292(C).
    9. Tostado-Véliz, Marcos & León-Japa, Rogelio S. & Jurado, Francisco, 2021. "Optimal electrification of off-grid smart homes considering flexible demand and vehicle-to-home capabilities," Applied Energy, Elsevier, vol. 298(C).
    10. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).

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