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

Xiaoyu Wu, Yuchen Cao, Hengtian Wu, Shaokang Qi, Mengen Zhao, Yuan Feng and Qinyi Yu ()
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Xiaoyu Wu: State Grid Energy Research Institute Co., Ltd., Building A, No. 18, Binhe Avenue, Future Science City, Changping District, Beijing 102209, China
Yuchen Cao: State Grid Energy Research Institute Co., Ltd., Building A, No. 18, Binhe Avenue, Future Science City, Changping District, Beijing 102209, China
Hengtian Wu: State Grid Energy Research Institute Co., Ltd., Building A, No. 18, Binhe Avenue, Future Science City, Changping District, Beijing 102209, China
Shaokang Qi: Economics and Management School, North China Electric Power University, No. 2, Beinong Road, Changping District, Beijing 102206, China
Mengen Zhao: Economics and Management School, North China Electric Power University, No. 2, Beinong Road, Changping District, Beijing 102206, China
Yuan Feng: Economics and Management School, North China Electric Power University, No. 2, Beinong Road, Changping District, Beijing 102206, China
Qinyi Yu: Economics and Management School, North China Electric Power University, No. 2, Beinong Road, Changping District, Beijing 102206, China

Energies, 2025, vol. 18, issue 3, 1-25

Abstract: 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.

Keywords: integrated multi-energy systems; LSTM; dynamic state estimation; energy system synchronization; operational efficiency; emission reductions (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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