Improving Synchronization and Stability in Integrated Electricity, Gas, and Heating Networks via LSTM-Based Optimization

This paper introduces an innovative optimization framework that integrates Long Short-Term Memory (LSTM) networks to enhance the synchronization and stability of urban integrated multi-energy systems (MESs), which include electricity, gas, and heating networks. The need for a holistic approach to manage these interconnected systems is driven by the increasing complexity of urban energy demands and the imperative to adhere to stringent environmental standards. The proposed methodology leverages LSTM networks for dynamic state estimation, enabling real-time and accurate predictions of energy demands and operational states across the different energy networks. This approach allows for the optimization of energy flows by adapting to fluctuations in demand and supply with high precision, which traditional static models are unable to do. By comprehensively modeling the unique operational characteristics and interdependencies of the electricity, gas, and heating networks, the framework ensures that the integrated system operates efficiently, remains stable under varying loads, and meets regulatory compliance for emissions. A synthesized case study simulating the operation of an integrated MES—including the IEEE 123-bus system for electricity, a modeled Belgian high-caloric gas network, and a Danish district heating system—illustrates the effectiveness of the proposed model. The study results indicate significant improvements in operational efficiency, reductions in emissions, and enhanced system stability. Key contributions of this paper include the development of a multi-layered optimization framework that addresses the dynamics of MESs, integration of environmental and regulatory compliance within the operational strategy, and a robust validation of the LSTM-based model against simulated anomalies and real-world scenarios.