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Online coal consumption characteristics fitting for daily economic dispatch using a data-driven hybrid sequential model

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  • Zheng, J.H.
  • Liang, Z.T.
  • Li, Zhigang
  • Wang, F.
  • Wu, Q.H.

Abstract

To motivate the transition to the future smart and low-carbon energy systems, it is essential to make full use of the online coal consumption data flow to reduce the dispatch cost of thermal power. This paper proposed a hybrid sequential model for coal consumption characteristics (CCC) modeling in daily economic dispatch (ED) based on the online coal consumption data flow. The hybrid sequential model, combined convolution neural network (CNN) and long short term memory (LSTM), is developed to model the temporal and spatial dynamics of actual CCC in arbitrary period and fluctuation. Furthermore, ED based on the proposed sequential CCC model is constructed, and the simplicial homology global optimization (SHGO) method is used to solve the ED optimization, for cost evaluation. Simulation studies are conducted to validate the accuracy and economy of our model and other reference models in terms of CCC regression and daily economic dispatch in plant level respectively. Results indicate that the proposed sequential CCC model shows competitive accuracy and significant energy saving.

Suggested Citation

  • Zheng, J.H. & Liang, Z.T. & Li, Zhigang & Wang, F. & Wu, Q.H., 2023. "Online coal consumption characteristics fitting for daily economic dispatch using a data-driven hybrid sequential model," Applied Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004919
    DOI: 10.1016/j.apenergy.2023.121127
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    References listed on IDEAS

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    1. Stefan C. Endres & Carl Sandrock & Walter W. Focke, 2018. "A simplicial homology algorithm for Lipschitz optimisation," Journal of Global Optimization, Springer, vol. 72(2), pages 181-217, October.
    2. Zhang, Tong & Li, Zhigang & Wu, Qiuwei & Pan, Shixian & Wu, Q.H., 2022. "Dynamic energy flow analysis of integrated gas and electricity systems using the holomorphic embedding method," Applied Energy, Elsevier, vol. 309(C).
    3. Zheng, J.H. & Xiao, Wenting & Wu, C.Q. & Li, Zhigang & Wang, L.X. & Wu, Q.H., 2023. "A gradient descent direction based-cumulants method for probabilistic energy flow analysis of individual-based integrated energy systems," Energy, Elsevier, vol. 265(C).
    4. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
    5. Wang, L.X. & Zheng, J.H. & Li, Z.G. & Jing, Z.X. & Wu, Q.H., 2022. "Order reduction method for high-order dynamic analysis of heterogeneous integrated energy systems," Applied Energy, Elsevier, vol. 308(C).
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