Investigation of a model predictive control (MPC) strategy for seasonal thermochemical energy storage systems in district heating networks

Wei, Zhichen; Tien, Paige Wenbin; Calautit, John; Darkwa, Jo; Worall, Mark; Boukhanouf, Rabah

Abstract

This research investigates the integration of model predictive control (MPC) with seasonal thermochemical energy storage systems (STES) within district heating networks, focusing on the Nottingham district heating as a case study. The primary aim is to develop an MPC strategy that utilizes machine learning models for accurate heat load forecasting. This strategy optimizes the charging and discharging cycles of thermochemical energy storage systems to mitigate the mismatch between heating energy supply and demand by storing surplus heat during summer and utilizing it during winter. We employed and validated machine learning models, including support vector regression (SVR), regression trees, and long short-term memory (LSTM) networks, using historical heat load and meteorological data. A validated numerical model of the thermochemical energy storage system (TCES) was integrated into the MPC framework, formulated as a mixed-integer linear program to optimize the STES's operations. The performance of the MPC strategy was benchmarked against a rule-based control approach under varying supply capacities to evaluate scalability and robustness. Our findings reveal that each machine learning model achieved comparable performance, with CVRMSE values within the 9–11% range. The LSTM model, in particular, provided accurate multi-step forecasts essential for the MPC framework. Incorporating these models into the MPC strategy allowed for precise heat demand predictions, enhancing the management of energy storage and distribution. Results confirmed that MPC effectively shifts energy seasonally, reduces reliance on auxiliary heating during winter, and minimizes waste heat. The MPC strategy outperformed the rule-based control by storing a significantly higher percentage of waste heat and meeting a greater portion of the additional heat demand that was not covered by the auxiliary heat supply. The system demonstrated effective performance under varying supply capacities, with the MPC strategy efficiently utilizing stored heat to meet demand at 80% supply capacity, achieving a waste heat reduction to 4% and meeting most of the heat demand. However, performance declined at 60% capacity, indicating the need for careful consideration of supply capacities in system design. This study highlights the potential of integrating machine learning models with MPC to enhance the performance and adaptability of district heating systems with STES, minimizing waste heat and efficiently meeting energy demands.

Citation

Wei, Z., Tien, P. W., Calautit, J., Darkwa, J., Worall, M., & Boukhanouf, R. (2024). Investigation of a model predictive control (MPC) strategy for seasonal thermochemical energy storage systems in district heating networks. Applied Energy, 376(Part A), Article 124164. https://doi.org/10.1016/j.apenergy.2024.124164

• Journal Article Type: Article
• Acceptance Date: Aug 6, 2024
• Online Publication Date: Aug 21, 2024
• Publication Date: Dec 15, 2024
• Deposit Date: Jan 9, 2025
• Journal: Applied Energy
• Print ISSN: 0306-2619
• Electronic ISSN: 0306-2619
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 376
• Issue: Part A
• Article Number: 124164
• DOI: https://doi.org/10.1016/j.apenergy.2024.124164
• Keywords: easonal thermal energy storage, Demand response, Model predictive control (MPC), District heating system, Long short-term memory (LSTM), Thermochemical energy storage systems (STES), Support vector regression (SVR), Regression trees, Built environment, Machine learning
• Public URL: https://nottingham-repository.worktribe.com/output/39159588
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0306261924015472?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Investigation of a model predictive control (MPC) strategy for seasonal thermochemical energy storage systems in district heating networks; Journal Title: Applied Energy; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.apenergy.2024.124164; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier Ltd.