Operational optimization of a 4th generation district heating network with mixed integer quadratically constrained programming

Heating and cooling networks: A comprehensive review of modelling approaches to map future directions

“…In Ref. [104], a mixed integer quadratically constrained program is presented for operational optimisation of a 4th-generation HCN in relation to temperatures and use of the thermal inertia in the network and in TES. The quadratic formulations allow simultaneous optimisation of energy balances and temperatures, with respect to efficiency correlations and the temperature threshold of components.…”

Decommissioned open-pit mines are potential geothermal sources of heating or cooling for nearby population centres

“…Regarding transmission operation, steady-state conditions have been considered for the hydraulic balances and calculating thermal transfer between the transmission pipelines and the ground. A more detailed assessment could be performed 38 , although increasing model complexity and computational costs. In the case of ambient temperature networks designed following the 5th generation thermal network definitions 39 , an additional consideration is the demand balancing among the prosumers, which reduces overall costs and increases the system efficiency 40 .…”

“…To maintain linearity of the system constraints, mixed integer linear programming formulations must simplify cooling and (more often) heating as transported thermal energy commodities with a fixed temperature 38 . This simplification disregards the role of carrier fluid https://doi.org/10.1038/s43247-024-01439-y temperature and mass flow on heat losses and the temperature dependence of HP efficiency, particularly relevant for extended pipeline lengths 55 .…”

“…Therefore, the study framework is developed in Python, utilising the COMANDO package. COMANDO has already been successfully used to analyse the design and operation optimisation of district heating networks 38,56,57 . The optimisation problem is defined through the thermal network components, using symbolic expressions that specify constraints or objective functions.…”

“…To reduce the computational load, a time series aggregation is performed using typical days, utilising the freely available tool TSAM 58 . The K-medoid method is selected for typical days determination for its documented high-quality output for energy system modelling 38,59 . Literature suggests at least 12 typical days to model a whole year of operation with good accuracy 60 .…”