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Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Author

Listed:
  • João Faria

    (Instituto de Telecomunicações and Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • José Pombo

    (Instituto de Telecomunicações and Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • Maria do Rosário Calado

    (Instituto de Telecomunicações and Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • Sílvio Mariano

    (Instituto de Telecomunicações and Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

Abstract

Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

Suggested Citation

  • João Faria & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System," Energies, MDPI, vol. 12(5), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:902-:d:212193
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    References listed on IDEAS

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    Cited by:

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    2. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
    3. Wei Zhang & Ming Zhong & Junfei Han & Yumei Sun & Yang Wang, 2022. "Research on the strategy of lithium-ion battery–supercapacitor hybrid energy storage to suppress power fluctuation of direct current microgrid [Load frequency control of a novel renewable energy in," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1012-1017.
    4. Saif Jamal & Nadia M. L. Tan & Jagadeesh Pasupuleti, 2021. "A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications," Sustainability, MDPI, vol. 13(18), pages 1-31, September.
    5. Tiezhou Wu & Wenshan Yu & Lujun Wang & Linxin Guo & Zhiquan Tang, 2019. "Power Distribution Strategy of Microgrid Hybrid Energy Storage System Based on Improved Hierarchical Control," Energies, MDPI, vol. 12(18), pages 1-14, September.
    6. Takele Ferede Agajie & Armand Fopah-Lele & Ahmed Ali & Isaac Amoussou & Baseem Khan & Mahmoud Elsisi & Wirnkar Basil Nsanyuy & Om Prakash Mahela & Roberto Marcelo Álvarez & Emmanuel Tanyi, 2023. "Integration of Superconducting Magnetic Energy Storage for Fast-Response Storage in a Hybrid Solar PV-Biogas with Pumped-Hydro Energy Storage Power Plant," Sustainability, MDPI, vol. 15(13), pages 1-30, July.
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    8. Zhenxing Zhao & Kaijie Chen & Ying Chen & Yuxing Dai & Zeng Liu & Kuiyin Zhao & Huan Wang & Zishun Peng, 2021. "An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing," Energies, MDPI, vol. 14(18), pages 1-15, September.

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