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A hybrid deep learning-based online energy management scheme for industrial microgrid

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  • Lu, Renzhi
  • Bai, Ruichang
  • Ding, Yuemin
  • Wei, Min
  • Jiang, Junhui
  • Sun, Mingyang
  • Xiao, Feng
  • Zhang, Hai-Tao

Abstract

The fluctuations in electricity prices and intermittency of renewable energy systems necessitate the adoption of online energy management schemes in industrial microgrids. However, it is challenging to design effective and optimal online rolling horizon energy management strategies that can deliver assured optimality, subject to the uncertainties of volatile electricity prices and stochastic renewable resources. This paper presents an adaptable online energy management scheme for industrial microgrids that minimizes electricity costs while meeting production requirements by repeatedly solving an optimization problem over a moving control window, taking advantage of forecasted future prices and renewable energy profiles implemented by a hybrid deep learning model. The predicted values over the control horizon are assumed to be uncertain, and a multivariate Gaussian distribution is used to handle the variations in electricity prices and renewable resources around their predicted nominal values. Simulation results under different scenarios using real-world data verify the effectiveness of the proposed online energy management scheme, assessed by the corresponding gaps with respect to several selected benchmark strategies and the ideal boundaries of the best and worst known solutions. Furthermore, the robustness of the scheme is verified by considering severe errors in forecasted electricity prices and renewable profiles.

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  • Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011806
    DOI: 10.1016/j.apenergy.2021.117857
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    5. Jinyeong Lee & Kyungcheol Shin & Young-Min Wi, 2024. "Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea," Sustainability, MDPI, vol. 16(13), pages 1-23, June.
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    7. Sana Qaiyum & Martin Margala & Pravin R. Kshirsagar & Prasun Chakrabarti & Kashif Irshad, 2023. "Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    8. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
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