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

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

Applied Energy, 2023, vol. 341, issue C, No S0306261923004919

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.

Keywords: Coal consumption characteristics; Economic dispatch; Long short term memory neural network; Simplicial homology global optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.apenergy.2023.121127

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