Performance assessment of nonconvex MINLP formulations for district heating model predictive control
Dennis Lottis,
Anna Cadenbach and
Philipp Härtel
Energy, 2025, vol. 335, issue C
Abstract:
This study evaluates the performance of nonconvex Mixed-Integer Nonlinear Programming formulations for optimizing district heating systems under model predictive control. A novel methodology is introduced to assess operational optimization results by benchmarking them with a complex transient thermo-hydraulic simulation model replicating real-world operational conditions with high temporal and spatial detail. The analysis focuses on a low-temperature, multi-source district heating system operating with supply temperatures between 55 and 80 °C. Two optimization approaches, a full problem formulation and a partial problem formulation, are compared against traditional rule-based controls. The case study reveals that optimization-based control strategies achieve cost savings of 1–10€/MWh over rule-based methods. The full problem formulation delivers additional reductions of 2€/MWh during winter by optimizing all mass flows and temperatures. Meanwhile, the partial problem formulation, despite its simplified fixed-temperature approach, consistently delivers high-quality solutions in seconds, making it well-suited for real-time applications. The detailed simulations validate the feasibility of the optimization control strategies, showing their potential to address computational challenges and enhance district heating system performance. Future research should prioritize improving scalability, addressing uncertainties in forecasting, and exploring hybrid approaches that combine machine learning with optimization techniques.
Keywords: District heating systems; Operational planning; Mathematical optimization; Nonconvex MINLP; Rule-based control; Transient simulation; Thermo-hydraulic dynamics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225035959
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225035959
DOI: 10.1016/j.energy.2025.137953
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().