Spatial energy model for the reduction in CO2 by district heating systems within the built environment in the Netherlands

A more sustainable heat supply of the built environment is needed to reach the policy goal of a climate-neutral economy in the Netherlands by 2050. This paper presents a method using a spatial energy model to calculate the most cost-effective CO2- reduction potential of a sustainable heat supply in the energy-demanding built environment in the Netherlands. The model can be used to evaluate national energy outlooks and to advice policymakers, to improve policy decision-making in the field of energy. Objects of analysis are the CO2-reduction potential and the costs of energy measures. There are two main model options concerning the types of measures that can be run separately or together. These options refer to a reduction in heat demand (energy conservation) achieved through certain building measures, and/or through district heating measures. Options for district heating consist of: waste heat for electricity and industrial plants, geothermal heating, combined heat and power district heating (CHPDH), and systems for ground source heat pumps (GSHP). The potential of district heating systems depends on the local availability of heat sources, on the one hand, and the intensity and extent of heat demand, on the other. For this purpose, the spatial energy model uses highly detailed geographical data on residential buildings and the services sector. With the aid of energy characteristics for the different types of buildings, the heat demand is calculated in each postcode-4 area of the Netherlands. Next the cost-effectiveness of district heating to the nearest option (heat source) of district heating for each postcode-4 area is calculated by the net present value (NPV), viewed from the perspective of the heat supplier. By considering in the calculation algorithm only the nearest heat source for each postcode-4 area calculation time is limited. A disadvantage of this algorithm is that the cost-effectiveness of the postcode-4 area to a heat source at larger distances could be better, in some cases. So the used algorithm yields the locally optimal solution but gives no absolute guarantee to find the general optimal solution for all heat sources. Nevertheless, we conclude that this heuristic approach approximates the globally optimal solution. Therefore, it is what is known as a "greedy" algorithm. The paper also discusses methods to improve this greedy algorithm. Finally, it presents some results of an analysis made with the aid of the model. The cost-effective CO2-reduction potential in a business as usual scenario of energy conservation and district heating, by 2050, will be 15% to 30% of the emissions related to the built environment. This range depends on the assumed energy prices and investment costs of the energy conservation measures. The contribution of district heating is 10 to 15 percentage points.