Multi-Energy-Microgrid Energy Management Strategy Optimisation Using Deep Learning

Wenyuan Sun, Shuailing Ma, Yufei Zhang, Yingai Jin () and Firoz Alam
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Wenyuan Sun: National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130022, China
Shuailing Ma: National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130022, China
Yufei Zhang: National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130022, China
Yingai Jin: National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130022, China
Firoz Alam: School of Engineering (Aerospace, Mechanical and Manufacturing), RMIT University, Melbourne, VIC 3000, Australia

Energies, 2025, vol. 18, issue 12, 1-28

Abstract: Renewable power generation is unpredictable due to its intermittency, making grid-connected microgrids difficult to operate, control, and manage. Currently used prediction models for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with the carbon trading mechanism are inefficient as they cannot account for all eventualities and are not well studied. Therefore, a two-stage robust optimisation model based on Bidirectional Temporal Convolutional Networks (BiTCN) and Transformer prediction for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with a carbon trading mechanism is proposed to solve this problem. First, BiTCN extracts implicit wind speed and wind power output sequences from historical data and feeds it into the Transformer model for point prediction using the attention mechanism. Ablation computation modelling is then performed. The proposed prediction model’s Mean Absolute Error (MAE) is found to be 1.3512, and its R 2 is 0.9683, proving its efficacy and reliability. Second, the proposed model is used to perform interval prediction in two typical scenarios: high wind power and low wind power. After constructing the robust optimisation model uncertainty set based on the prediction results, simulation experiments are performed on the proposed optimisation model. The simulation results suggest that the proposed optimisation model enhances renewable energy use, emissions reductions, microgrid operating costs, and system reliability. The study also reveals that the total system cost and carbon emission cost in the low wind scenario are 283% (2.83 times) and 314% (3.14 times) higher than in the high wind scenario; hence, a significant percentage of renewable energy is needed for microgrid stability.

Keywords: energy management; wind power Forecasting; deep learning model; multi-energy complementarity; carbon trading mechanisms; robust optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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