DynLotus
SUPSI-MEMTI
Riccardo Toffanin, SUPSI
Proprietary
DeCarbCH - WP 3
Software for dynamic modelling of district heating systems with heat pump - based central plant and local heat pumps. The modelling output are typically energy balances, efficiencies and total costs. The model has also been used to simulate the electricity grid balancing possibilities by adjusting the thermal control parameters. The model could be improved to include emissions. Future developments will also include the implementation of the cooling demand of the users and the introduction of more complex control strategy for electricity grid flexibility, such as Model Predictive Control (MPC).
Features
- Large range of distribution supply temperatures from 5C to 100C
- Central plant with heat pumps, backup boilers and thermal storages
- The substations are automatically designed to have a heat exchanger or booster heat pumps if needed in order to reach the user distribution and hot water temperatures
- Operating conditions at full and part load
- Simulation of tree-shaped distribution network
- The modeling time step may be reduced to minutes, in order to take into account heat storage systems at plant or at users, as well as the quasi-instantaneous electricity demand, thus allowing for the search of optimal electricity grid balancing strategies
- Resistive-capacitive (R-C) modeling of users buildings allow for dynamic simulation of the thermal needs
- Given a set of users, their distribution temperatures and the network topology, the models calculates energy balances and efficiencies as well as total costs
Facts
| Class | District heating model |
| Type | Deterministic |
| Spatial regions | Switzerland |
| Spatial resolution | Cantonal, Single building level |
| Time coverage | Single year 2050 |
| Time resolution | Hourly |
Sectors | Residential, Industrial, Commercial |
Category | Inputs | Outputs |
|---|---|---|
| Socioeconomy | Climate policy measures Managerial (strategical, business models) Legal | |
| Infrastructure | Electricity - distribution network Thermal network Gas network | Thermal network |
| Environment | CO2 emissions from energy system | CO2 emissions from energy system |
| Energy demand | Space heating Space Cooling Industrial heating Industrial cooling Hot water Total electricity | |
| Energy supply/production | Space heating Space cooling Industrial heating Industrial cooling Heat storage Cold storage Hot water | Space heating Space cooling Industrial heating Industrial cooling Heat storage Cold storage Hot water |
| Resource potential | Solar Water Wind Biomass Hydropower potential Geothermal Artificial thermal sources (e.g. waste heat) | |
| Direct demand of resources | ||
| Trade | ||
| Technologies Inv: Investment costs Eff: Efficiency OM: Operation and Maintenance costs LCA: Life cycle assessment indicators | CHPs (Inv,Eff,OM,LCA) Heat production (Inv,Eff,OM,LCA) Heat storage (Inv,Eff,OM,LCA) Pyrolysis (Inv,Eff,OM,LCA) Wood gassification (Inv,Eff,OM,LCA) Heat Pumps (Inv,Eff,OM,LCA) Thermal Solar (Inv,Eff,OM,LCA) Boilers (Inv,Eff,OM,LCA) Cooling (Inv,Eff,OM,LCA) Geothermal (Inv,Eff,OM,LCA) | |
| Prices | Electricity price Gas price Fuel price | |
| Others |
References
- R. Toffanin et al (2021). Impact of Legionella regulation on a 4th generation district heating substation energy use and cost: the case of a Swiss single-family household. Energy. . https://doi.org/10.1016/j.energy.2021.120473
This page was last modified on 2022.02.22, 13:16