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A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids

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  • Li, Shenglin
  • Zhu, Jizhong
  • Dong, Hanjiang
  • Zhu, Haohao
  • Fan, Junwei

Abstract

The rise of microgrids provides an effective solution to the problem of local consumption of renewable energy sources. However, the power fluctuations are the crucial issue for the widespread adoption of the grid-connected microgrid with renewable energy sources. We aim to economically and locally solve the problem of grid-connected power fluctuations of microgrid. In this paper, a novel rolling optimization strategy considering grid-connected power fluctuations smoothing for microgrids is provided. Firstly, the mathematical model of the microgrid is described, which contains the grid-connected power limits and supercapacitor-battery hybrid energy storage system. Then, a priority-based smoothing method of power fluctuations is proposed for the first time. As a flexible load, the heating/cooling load is used as virtual energy storage to participate in power regulation. Finally, the rolling optimization strategy integrated with the energy trigger mechanism is designed to achieve the operation optimization. The objectives of this paper are to help the microgrids improve grid-connection friendliness and minimize the daily operating costs that include fluctuation penalties. Simulation results on different scenarios for a grid-connected microgrid show that the novel rolling optimization strategy has better performance in any scenario. Compared with the traditional strategy, the total operating costs of the proposed strategy can save 5.67% for a given scenario. We can conclude that the proposed rolling optimization strategy can effectively reduce cost payment and meet the requirement of grid-connected power fluctuations smoothing.

Suggested Citation

  • Li, Shenglin & Zhu, Jizhong & Dong, Hanjiang & Zhu, Haohao & Fan, Junwei, 2022. "A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016664
    DOI: 10.1016/j.apenergy.2021.118441
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    5. Reveles-Miranda, María & Ramirez-Rivera, Victor & Pacheco-Catalán, Daniella, 2024. "Hybrid energy storage: Features, applications, and ancillary benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Liu, Shan & Yan, Jie & Yan, Yamin & Zhang, Haoran & Zhang, Jing & Liu, Yongqian & Han, Shuang, 2024. "Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate," Applied Energy, Elsevier, vol. 357(C).
    7. Yuanyuan He & Luxin Wan & Manli Zhang & Huijuan Zhao, 2022. "Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    8. Zhou, Siyu & Han, Yang & Zalhaf, Amr S. & Lehtonen, Matti & Darwish, Mohamed M.F. & Mahmoud, Karar, 2024. "A novel stochastic multistage dispatching model of hybrid battery-electric vehicle-supercapacitor storage system to minimize three-phase unbalance," Energy, Elsevier, vol. 296(C).
    9. Manduleli Alfred Mquqwana & Senthil Krishnamurthy, 2024. "Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty," Energies, MDPI, vol. 17(2), pages 1-21, January.
    10. Nie, Yonghui & Qiu, Yu & Yang, Annan & Zhao, Yan, 2024. "Risk-limiting dispatching strategy considering demand response in multi-energy microgrids," Applied Energy, Elsevier, vol. 353(PA).

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