Smart Design, Control, and Optimization of Thermal Energy Storage in Low-Temperature Heating and High-Temperature Cooling Systems

QC 20250805

Abstract

Heating and cooling account for about 55% of energy used in buildings worldwide and are a leading source of operational CO2 emissions. In cold‑climate regions such as Sweden, supplying this thermal demand efficiently and sustainably is crucial for meeting national and global climate goals. Low‑Temperature Heating (LTH) and High‑Temperature Cooling (HTC) systems, with minimal temperature difference between energy supply and demand, are modern solutions that work well with low‑exergy and renewable energy sources. When paired with Thermal Energy Storage (TES), especially deep seasonal boreholes, these technologies can boost energy flexibility, lower carbon intensity, and improve long‑term system resilience. Yet an important question remains: How can we push these already ultra‑efficient LTH‑HTC‑TES systems even further? The answer lies in adding intelligent control and optimization, the final layer needed to unlock their full operational, economic, and environmental potential.

This thesis investigates how smart design, advanced control, and optimization can make an already smart LTH‑HTC‑TES design even smarter. The study centers on Juvelen, a 10,000 m² commercial building in the city of Uppsala in Sweden, famous for its “deep‑green” thermal energy concept: it drills deep into the ground to utilize the ground's heating and cooling potential directly through borehole TES without any extra machinery while also using passive cooling, energy‑recovery ventilation, and smart interaction with the district‑heating network. This makes Juvelen a perfect testbed for exploring how AI‑driven optimization and control can further improve next‑generation thermal systems in commercial buildings. The research is carried out in three successive stages. First, a comprehensive literature review identifies the knowledge gaps in how TES is integrated, controlled, and optimized within LTH and HTC systems. Second, a detailed dynamic model of Juvelen and its thermal system is developed in TRNSYS and validated against real‑time data from the TEKLA building‑management system. Third, this validated model is used to investigate several smart performance‑enhancement approaches: (i) multi‑objective optimization with an artificial neural network (ANN) surrogate and evolutionary algorithms such as Grey Wolf, Non-dominated Sorting Genetic Algorithm‑II, particle swarm, and dragonfly, (ii) modifying the radiator temperature through an optimal adaptive radiator control fine‑tuned through particle swarm, (iii) evaluation of an alternative system configuration integrated with a ground‑source heat pump, and (iv) a forecast‑driven model predictive control (MPC) tested under realistic uncertainty.

The baseline study shows that, even without mechanical chillers or heat pumps, the existing system meets the entire cooling load and about one‑third of the heating demand directly through the borehole thermal storage, with the remaining heat supplied by the district heating network. Over its ten‑year payback period, the system avoids more than 140 tonnes of CO2, making it an ideal candidate for exploring how even the best-performing systems can be enhanced through smart design, predictive control, and optimization. Building on this foundation, the continuation of the research demonstrates how intelligent methods, and smart configurations can unlock further gains. Among several neural‑network training options, the Levenberg–Marquardt algorithm combined with a cascade‑forward architecture produced the smallest prediction errors and shortest computation times, making it the most dependable for optimization. Using this model, ANN‑based optimization lowered the levelized cost of thermal energy by 14.5%, reduced CO₂ intensity by 6%, and increased delivered thermal energy by 11%. An optimal adaptive radiator‑supply control, derived from a revised temperature equation considering solar radiation, ventilation status, and internal gains, improved comfort by 72.7% on the weighted temperature deviation scale and cut heating bought from the network by about 13.2%. Further benefits came from adding two 40 kW heat pumps, which increased seasonal flexibility. A new seasonal heat‑management scheme lets the district heating network recharge the boreholes each September, keeping the ground in thermal balance and boosting long‑term sustainability. This integration lowered annual operating costs by 9.4% and CO₂ impact from 23.9 tonnes in the existing system to 1.6 tonnes, aligning the proposed smart integration with Sweden’s net-zero building goals. Finally, a forecast‑driven model‑predictive controller achieved more than 13% and 5% extra market-responsive and operational cost savings and shortened payback by about four years, while Monte Carlo tests confirmed its robustness against forecasting errors.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-368012