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Thermal networks require thermal energy storage (TES) provisions for balancing thermal energy sources with variable consumer demand. Harvesting ice is an economical option for latent heat TES systems in cooling networks given the wide availability of the storage medium. This paper presents an artificial intelligence (AI) based model to monitor the state-of-charge (SoC) and the outlet temperature of the heat transfer fluid (T o) of an ice tank under fluctuating operating conditions. The AI model is a non-linear autoregressive network with exogenous inputs (NARX) that was trained and tested with datasets obtained from experimental measurements of a practical ice tank and a physics-based model of the tank. The NARX model was sensitised with physics-informed attributes to recognise different heating and cooling zones. The model exhibits a high accuracy in predicting the operating conditions of the ice tank when benchmarked against both experimental measurements of a practical tank and outputs from the physics-based model. For instance, it achieves R 2 values of 0.9943 and 0.9842 for SoC and T o , with root mean square errors of 1.73% for SoC and 0.3161°C for T o. The NARX model is 86% faster than its physics-based counterpart and its implementation requires limited computational resources—making it suitable as a standalone estimator for the TES operation and the accelerated simulation of energy systems containing latent heat TES units. Furthermore, given the limited availability of NARX models in open-source libraries, the presented NARX model and relevant datasets have been made available alongside this paper to contribute to open-science in energy research and the broader AI community. [Display omitted] • The state of a TES unit is continuously quantified with a non-intrusive NARX model. • The NARX model trained on physics-based data replicates the real system's response. • Heating zone-wise segregation of prediction tasks boosts the model's accuracy. • The NARX model is 86% faster than a physics-based model built for fast computation. • Limited public availability of a NARX model is addressed by code and data sharing. [ABSTRACT FROM AUTHOR]

In light of the global energy and climate crises, integration of low-carbon technologies into energy systems is being considered to mitigate the high energy costs and carbon footprint. The wide range of available capacities, efficiencies, and investment costs of these technologies and their different possible operating schedules can unlock several pathways towards decarbonisation. This paper presents an optimisation framework for a public healthcare facility to determine the optimal operation schedule of the site's energy system. A detailed techno-economic analysis of low-carbon power generation, conversion, and energy storage technologies that can be incorporated into the system based on real historical data was carried out for different scenarios. The results reveal that a heat pump with a capacity of 1800 kW can replace gas boilers on-site to meet the heat demand while recovering the investment in 5 years and providing an operating and carbon cost saving of 22.47% compared to the base case. The analysis shows that a more electrified mode of operation is favoured during high gas prices, thus making electrical energy storage more attractive than thermal energy storage. While handling real data, the optimisation algorithm was sensitised to discriminate conventional energy supplies from clean energy sources by considering their carbon impact so that it minimises energy bills in a smart and eco-friendly way. The optimisation algorithm and the subsequent techno-economic analysis provide a comprehensive framework to decision-makers for facilitating energy investment decisions. The framework can be used based on the short and long term goals of the energy system, visualising the evolution of financial benefits over equipment lifetime, and understanding the environmental impacts of integrating renewable energy. • Heat pumps are the most favourable decarbonisation technology for the hospital. • The moderate variations in energy demand limit the scope of energy storage. • Fuel price surcharge favours EES over TES and reduces payback period of retrofits. • PVT's savings scale linearly with roof space until 3 times the available capacity. • Monetising the carbon footprint shortens the DPP of clean retrofit technologies. [ABSTRACT FROM AUTHOR]

Hot water-based thermal energy storage (TES) tanks are extensively used in heating applications to provide operational flexibility. Simple yet effective one-dimensional (1-D) tank models are desirable to simulate and design efficient energy management systems. However, the standard multi-node modelling approach struggles to reproduce the dynamics of highly thermally stratified tanks due to their artificial numerical diffusion. In this paper, a novel 1-D multi-node modelling approach is introduced for accurately simulating water tanks with a high extent of thermal stratification. A non-linear, hybrid continuous–discrete time model able to capture the sudden temperature change within the tank is presented. The modelling approach was adopted to simulate a commercial TES tank, with the model being implemented in MATLAB/Simulink. Results from experimental tests were compared with simulation results, demonstrating that a hybrid continuous–discrete 12-node model accurately estimates the temperatures of the tank. It is also shown that the hybrid model avoids the numerical diffusion exhibited by standard multi-node models. This has been evidenced by the reduced root mean square and mean absolute errors exhibited by the hybrid model when compared with the experimental data. • A novel 1-D multi-node model for hot water thermal energy storage tanks is presented. • A hybrid continuous–discrete time model with a moving thermocline barrier is used. • Numerical diffusion is reduced for models with a low spatial resolution. • Simulation results show a good agreement with measurements from a commercial tank. • The hybrid model outperforms standard models with a higher spatial resolution. [ABSTRACT FROM AUTHOR]