Multi-objective integrated optimization of geothermal heating system with energy storage using digital twin technology

Guo, Yan; Tang, Qichao; Darkwa, Jo; Wang, Haoran; Su, Weiguang; Tang, Dezhao; Mu, Jiong

Abstract

Heat energy storage technology plays a significant role in energy systems, and the various technological solutions brought about by digitalization are especially valuable in the field of energy storage. This article proposes an innovative model based on digital twin technology to solve the supply–demand mismatch problem in geothermal heating systems. This model achieves multi-objective optimization of comprehensive cost, geothermal energy utilization rate, and carbon emission by constructing a heat storage geothermal heating system. Digital twin technology integrates data and information models of public buildings and facilitates their sharing and transmission throughout the entire lifecycle of the geothermal heating system. Initially, the proposed method employs a machine learning-based approach to accurately predict heating demand. Subsequently, the operation of the heat storage water tank and heat pump units is optimized to resolve difficulties in matching energy supply and demand. Finally, the method takes full advantage of time-of-use electricity pricing policies to reduce costs. The data utilized were collected from an office building in China over a period of six months. Experimental results demonstrate that: (1) In terms of predicting heating demand, the improved neural network proposed in this study achieved a prediction accuracy of 98%, which is a 10% improvement over comparative algorithms. Additionally, the experimental comparison of four types of errors showed that the machine learning method proposed had smaller errors across the board. (2) The method realized collaborative multi-objective optimization, and in five scenarios, the comprehensive performance index increased by up to 38.03% compared to the benchmark system. This indicates that intelligent technology is an effective means of enhancing the energy sustainability of geothermal heating systems, and the use of geothermal energy as a clean energy source effectively addresses issues related to the storage, utilization, management, and energy conservation of buildings.

Citation

Guo, Y., Tang, Q., Darkwa, J., Wang, H., Su, W., Tang, D., & Mu, J. (2024). Multi-objective integrated optimization of geothermal heating system with energy storage using digital twin technology. Applied Thermal Engineering, 252, Article 123685. https://doi.org/10.1016/j.applthermaleng.2024.123685

• Journal Article Type: Article
• Acceptance Date: Jun 11, 2024
• Online Publication Date: Jun 14, 2024
• Publication Date: Sep 1, 2024
• Deposit Date: Jul 15, 2024
• Publicly Available Date: Jun 15, 2025
• Journal: Applied Thermal Engineering
• Print ISSN: 1359-4311
• Electronic ISSN: 1873-5606
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 252
• Article Number: 123685
• DOI: https://doi.org/10.1016/j.applthermaleng.2024.123685
• Public URL: https://nottingham-repository.worktribe.com/output/37304367
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S135943112401353X?via%3Dihub