Strategic outline and sizing of district heating networks using a geographic information system

The implementation of district heating networks into cities is a main topic in European Union policy plannings looking for sustainable solutions to reduce CO2 emissions and improve global energy efficiency of heating systems. Compared to decentralized heating production units, district heating networks provide higher energy efficiencies due to larger units’ sizes. However, their development into cities is generally limited by high initial investment costs and a long return on investment period. Therefore, policymakers and investors are reserved about new district heating networks projects because of the risks associated with the profitability of the project.

The development of optimization methods intended to draft efficient systems using heating consumption profiles into a prescribed geographic area are useful in this purpose. A Multi-Period Mixed Integer Linear Programming (MILP) model for the optimal outline and design of a district heating network based on a Geographic Information System (GIS) is described in this thesis. The optimal outline aims to determine the location of the heating sources and the pipes into the network while the optimal design aims to size the pipes and the heating sources. This model can be used as a decision tool based on the maximization of the net cash flow (NCF) generated by the system from user-defined economic and physical parameters. This methodology aims to be applicable for a large range of problem sizes from small-scale to large-scale case studies while guarantying numerical robustness. This thesis provides new insights for the optimization of heating network systems:

1. A global review of the district heating network sector regarding the advantages, the technical breakthroughs and the economic features of these heating networks. Existing optimization models for heating networks are compared to identify their main attributes and to highlight the novelty and the benefits of this work compared to the existing ones.
2. Implementation of a new decision tool based on a methodology enabling to design any new heating network from scratch based on a geographic information system and user-defined economic and urbanistic parameters.
3. Applications of the methodology to small-scale and large-scale case studies to show the replicability of the decision tool to a large range of heating networks.
4. Optimized heating networks from the decision tool are modelled in a dynamic way to illustrate the limits of the decision tool and its lack of accuracy regarding the physics of the system.