Multi-objective Optimization of Energy Storage Capacity for Wind-Solar Microgrids Based on Digital Twins and a TCN-SLSTM-MHA Hybrid Model
DOI:
https://doi.org/10.4108/ew.14194Keywords:
wind-solar microgrid, digital twin, TCN-SLSTM-MHA, energy storage capacity, multi-objective optimizationAbstract
To address the challenges in wind-solar microgrids caused by the intermittency and volatility of renewable energy—such as power imbalance, unreasonable energy storage capacity configuration, and the difficulty in achieving coordinated optimization of economy, reliability, and environmental sustainability under limited capacity—a multi-objective energy storage capacity optimization method is proposed based on digital twin technology and a hybrid model combining Temporal Convolutional Network (TCN), Stacked Long Short-Term Memory Network (SLSTM), and Multi-Head Attention (MHA) mechanism (TCN-SLSTM-MHA). First, a digital twin model of the wind-solar microgrid is constructed to enable real-time mapping, monitoring, and simulation analysis between the physical system and its virtual counterpart, overcoming the limitations of traditional models in adapting to dynamic operational scenarios. Second, a TCN-SLSTM model is introduced, enhanced with the MHA mechanism to dynamically assign weights across time steps, thereby improving the accuracy of source (generation) and load forecasting. Finally, a multi-objective optimization function is established that simultaneously considers the minimization of Life Cycle Cost (LCC), maximization of Power Supply Reliability (PSR), and reduction of Carbon Emission Intensity (CEI), enabling the optimal determination of storage capacity. Experimental results demonstrate that, in terms of source-load forecasting, the proposed model improves prediction accuracy by 16.9% and 16.7%, respectively, compared to the conventional TCN-LSTM model. In energy storage capacity optimization, compared to the traditional weighted sum method, the proposed approach reduces LCC by 15.2%, increases PSR by 4.3%, and decreases CEI by 12.5%, validating the effectiveness and superiority of the proposed model.
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Copyright (c) 2026 Tong Xia, Jichao Ye, Yijia Lü, Xinwei Hu, Hui Huang, Yonghai Xu
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