Modelling district scale energy systems

When it comes to modelling an energy system of a district or entire community, a suitable software tool should be selected that can include all the individual supply systems, demands, and energy flows of the project under analysis. In addition, it should be able to go into the required level of detail and accuracy in calculations. For example, it is beneficial if a tool can run simulations with a time step equal to 1 hour, or shorter, especially when there are renewable resources integrated in the system.^[1]

Most models today employ a steady state approach to simulate the behaviour of such systems, resulting in quite realistic estimations that can be used in sizing the various individual components included in them.^[2]

The energyPRO software

For this project, energyPRO (version 4.6.81, released in February 2019), was chosen in order to model the two district energy schemes under study. energyPRO is a tool that can simulate both thermal and electrical networks of a complex district or community scale energy system. It includes submodels for various storage technologies (including thermal storage tanks), as well as electric heat pumps and renewable energy systems.^[2],[3] Furthermore, energyPRO has achieved a good rating in recent studies, when ranked against other software tools which are commonly used for the modelling of medium to large scale energy systems.^[1],[2]

For optimisation control, energyPRO includes a production cost optimisation feature which applies non chronological control over the chosen simulation period, in order to provide the system with as ‘cheap’ electricity as possible. It is thus making the assumption that the forecasts of renewable energy generation, grid energy prices, etc., that are being used as inputs for the model are perfectly accurate.^[1]

Modelling approach

A model was created in energyPRO for each of the scenarios explored in this project, Queens Quay Benchmark (QQB) and Clean Heat Vision (CHV). Each scenario includes:

• The building heat demand, split in residential and non residential hourly time series, as generated through the chosen heat demand analysis approach.
• The losses of the district heating network, inputted as a monthly time series that resulted from the piping heat loss calculations.
• The electrical and thermal energy supply systems (water source heat pumps and a natural gas boiler, or renewables).
• The electrical grid, with the hourly time series of UK grid carbon intensity data of 2018 used as an input.
• Appropriate connections between the supply and demand components.

Each model was set up as a 'design' project, with a simulation time step equal to 1 hour and a monthly 'optimisation period'. Additional variations of the two aforementioned scenarios were simulated, in order to further explore the impact of each component of the energy system in the overall carbon emssions and perform a sensitivity analysis.