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Novel forecast-based dispatch strategy optimization for PV hybrid systems in real time

Author

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  • Rodríguez-Gallegos, Carlos D.
  • Vinayagam, Lokesh
  • Gandhi, Oktoviano
  • Yagli, Gokhan Mert
  • Alvarez-Alvarado, Manuel S.
  • Srinivasan, Dipti
  • Reindl, Thomas
  • Panda, S.K.

Abstract

This paper proposes a new method to optimize the scheduling of off-grid systems composed of solar panels, batteries, and diesel generators in real time. The approach takes into account the load and irradiance forecasted values for the near future to determine the optimal power generation and the operation of the different energy sources which achieve the lowest cost while fulfilling the provided constraints. A real-time simulator is employed to run the simulations with a high degree of accuracy to further validate the obtained results as well as to analyze the grid quality parameters (frequency, voltage, and harmonics). To validate the effectiveness of the proposed forecast-based approach, the performance from two benchmark algorithms commonly applied in these systems are also estimated. The final results reveal that the proposed algorithm is able to achieve 5% cost savings with respect to the benchmark approaches while still fulfilling the grid quality constraints. The proposed method can then be applied for real off-grid systems to further enhance their performance.

Suggested Citation

  • Rodríguez-Gallegos, Carlos D. & Vinayagam, Lokesh & Gandhi, Oktoviano & Yagli, Gokhan Mert & Alvarez-Alvarado, Manuel S. & Srinivasan, Dipti & Reindl, Thomas & Panda, S.K., 2021. "Novel forecast-based dispatch strategy optimization for PV hybrid systems in real time," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001675
    DOI: 10.1016/j.energy.2021.119918
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    References listed on IDEAS

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    1. Gandhi, Oktoviano & Zhang, Wenjie & Rodríguez-Gallegos, Carlos D. & Verbois, Hadrien & Sun, Hongbin & Reindl, Thomas & Srinivasan, Dipti, 2020. "Local reactive power dispatch optimisation minimising global objectives," Applied Energy, Elsevier, vol. 262(C).
    2. Gupta, Ajai & Saini, R.P. & Sharma, M.P., 2011. "Modelling of hybrid energy system—Part II: Combined dispatch strategies and solution algorithm," Renewable Energy, Elsevier, vol. 36(2), pages 466-473.
    3. Rodríguez-Gallegos, Carlos D. & Yang, Dazhi & Gandhi, Oktoviano & Bieri, Monika & Reindl, Thomas & Panda, S.K., 2018. "A multi-objective and robust optimization approach for sizing and placement of PV and batteries in off-grid systems fully operated by diesel generators: An Indonesian case study," Energy, Elsevier, vol. 160(C), pages 410-429.
    4. Tu, Tu & Rajarathnam, Gobinath P. & Vassallo, Anthony M., 2019. "Optimization of a stand-alone photovoltaic–wind–diesel–battery system with multi-layered demand scheduling," Renewable Energy, Elsevier, vol. 131(C), pages 333-347.
    5. Rodríguez-Gallegos, Carlos D. & Gandhi, Oktoviano & Bieri, Monika & Reindl, Thomas & Panda, S.K., 2018. "A diesel replacement strategy for off-grid systems based on progressive introduction of PV and batteries: An Indonesian case study," Applied Energy, Elsevier, vol. 229(C), pages 1218-1232.
    6. Tang, Ruoli & Wu, Zhou & Li, Xin, 2018. "Optimal operation of photovoltaic/battery/diesel/cold-ironing hybrid energy system for maritime application," Energy, Elsevier, vol. 162(C), pages 697-714.
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    Cited by:

    1. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).

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