In this blog post, I write about a project we supported over the past couple of years involving Buildings thermal management systems modelling with NTU – Nottingham Trent University, UK.
In summary: We’re looking to improve the energy efficiency of buildings thermal management, primarily to meet targets on CO2 emissions to reduce the impact on climate change.
In order to understand the impact of applying certain energy saving technologies or control strategies, you must simulate those scenarios because generating 1:1 scale integrated prototypes of the systems involved, is unfeasible including cost and time.
In this blog post we introduce a particular case study. The case study is the assessment of a decentralised full electric heating system that will serve 39 UK homes. The system makes use of renewable energy sources such as solar PV (photo-voltaic) panels to generate electricity and ground source heat pumps that utilise boreholes as the heat source.
The system model was split into 2 parts. The dwellings within the Energy+ FMU and the Dymola heat source systems forming the rest of the model (See figure 1). The heat source technologies that were modelled with Dymola are photovoltaic cells, ground source heat pumps. The storage devices were electrical batteries and water tanks.
The information exchange at the Dymola – Energy+ FMU interface was the temperature and flow of the hot water. Because FMI does not support acausal physical connector modelling, we need to create a boundary where we consider the flow and across variables as signal connectors with fixed causality. In the case of a fluid that is considered a single mixed substance, this is manageable. In the case where you would be co-simulating a flue gas with many constituents, the interface would be somewhat more complex, but not impossible.
Split and co-simulated closed fluid circuits require attention to enthalpy creep across the interface over long periods of time. This requires numerical corrections at the interface to mitigate this issue. With adequate corrections the phenomenon can be made negligible.
One of the main objectives of the control strategy in addition to maximising the use of renewables, was to avoid the high electricity price in the afternoon in the following ways:
• Use all the photovoltaic production in powering the heat pumps
• Store any surplus electrical energy generated by the PVs in the battery
• Switch off the heat pumps in the hours in which the electricity is more
expensive and store the PV production in those hours
• Use the stored energy to run the heat pumps after 4pm
We have extracted the following conclusions from the material that was presented in Copenhagen . Additional work is published within .
The co-simulation of this energy cluster gives the opportunity to study the system in a very precise and accurate way
• Dymola in particular allows us to simulate all the dynamics of the energy centre with a detailed view of the energy flows through all the branches of the system.
• Dymola offers also the opportunity to create and manage the control strategies of the entire system; this is vital from the point of view of optimization.
• The simulation shows that the PV system is capable to provide almost 50% of the electricity needed by the heating system; furthermore, taking into account the UK’s energy mix, the share of renewable energy goes up to 66%, and 76% of the electricity is carbon-free.
 Cucca G., Ianakiev A., Co-simulation tool for hybrid
energy system optimisation, 5th International Conference on Smart Energy Systems
Copenhagen, 10-11 September 2019
 Cucca G., Ianakiev A., Assessment and optimisation of energy consumption in building communities using an innovative co-simulation tool. Journal of Building Engineering, Volume 32, November 2020, 101681
Written by: Alessandro Picarelli – Engineering Director