Dynamic simulation and control of the heat supply system of a civic building with thermal energy storage units

The integration of low-carbon energy solutions into heating and cooling systems is a growing topic of interest towards the decarbonisation of the energy sector. The complexity of interactions between thermal-hydraulic components in these systems requires moving from a steady-state analysis to a dynamic approach. Commercial dynamic process simulators provide a good platform to model and simulate the physics phenomena of these components. However, co-simulation between software engines is often necessary to incorporate process control systems and regulate key system variables. This paper presents the co-simulation of a real heating application from a civil building for health services integrating thermal energy storage (TES) units. A detailed dynamic model of the heating system under study is developed in Apros, a commercial dynamic process simulator. The process control system responsible for regulating the hydraulic pumps, valves, and heat sources is implemented in MATLAB/Simulink. The TES system is operated by the process control system based on a daily schedule, achieving a reduced utilisation of the heat generation units during peak-demand hours

Design and verification of an effective state-of-charge estimator for thermal energy storage

Thermal energy storage (TES) is widely used in district heating and cooling systems (DHCS) to act as a buffer between the supply and demand schedules. The adequate control of charging and discharging modes of TES may improve the overall performance of a DHCS and, to this end, an effective regulation of its state‐of‐charge (SoC) is required. However, the calculation of SoC depends on the availability and accuracy of temperature measurements. A model‐based observer for the calculation of the SoC of water‐based TES tanks is presented. A dynamic model of a one‐dimensional stratified water tank is adopted to develop the observer. Its effectiveness is assessed through ‘model‐in‐the‐loop’ cosimulations, with the observer and the feedback control system being implemented in MATLAB/Simulink and a high‐fidelity water tank component available in Apros being used as the plant model. Simulation results considering three different system configurations demonstrate that the model‐based observer accurately estimates the temperature distribution within the tank, leading to an effective SoC computation and control—even in the case of sensor failure or upon limited sensor availability

Experimental validation of a hybrid 1-D multi-node model of a hot water thermal energy storage tank

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

Effective estimation of the state-of-charge of latent heat thermal energy storage for heating and cooling systems using non-linear state observers

An effective quantification of the energy absorbed and supplied by latent heat thermal energy storage (LHTES) units is critical to maximise their use within thermal systems. An effective control of the charging and discharging processes of these units demands an accurate estimation of the state-of-charge (SoC). However, a direct and reliable SoC estimation requires incorporating internal sensors to monitor the temperature gradient of the phase change material (i.e. the storage medium), resulting in higher instrumentation costs and technical specifications. These issues may be relieved by adopting state observers for SoC estimation to drastically reduce the number of measurements. This paper bridges this gap by presenting a novel and direct method for estimating the SoC of LHTES units, both for heating and cooling applications, based on a non-linear state observer. The observer is based on a simple one-dimensional dynamic model of the thermal store and the thermophysical properties of the storage medium and the heat transfer fluid, which are usually provided by manufacturers. This enables the estimation of the internal temperatures of the LHTES unit and, in turn, SoC calculation. The observer implementation is simple as it requires three measurements only as input variables (i.e. the mass flow rate and the input and output temperatures of the heat transfer fluid). The SoC estimation approach is assessed through dynamic simulations of two LHTES units: one for a heating application and one for a cooling application. The results show that the SoC can be estimated with root mean square and mean absolute errors of less than 4.6% and 3.62%, respectively, compared with experimental measurements

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning

A framework for the assessment of optimal and cost-effective energy decarbonisation pathways of a UK-based healthcare facility

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