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Spatial energy model for the CO2-reduction of the built environment by district heating in the Netherlands

Author

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  • Sebastiaan van Bemmel
  • Rob Folkert
  • Ruud van den Wijngaart

Abstract

A more sustainable heat supply of the built environment is needed to reach the policy-goal of a climate neutral economy in The Netherlands in 2050. This paper presents a method of a spatial energy-model to calculate the economic profitable 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 policy-makers to improve policy decision making in the energy-field. Objects of analyze are the CO2-reduction potential and costs of energy measures. Two main options concerning the type of measures are possible in the model which can be run separately or both. Reduction of the heat demand (energy-conservation) by building measures and the use of the potential of district heating measures. Options for district heating consists of: waste heat for electricity and industrial plants, geothermal heating, combined heat and power district heating (CHPDH) and systems for ground source heat pump (GSHP). The potential of district heating depends on the local availability of the heat sources on one hand and on the intensity and extent of the heat demand on the other hand. For this purpose the spatial energy-model uses highly detailed geographical data of residential buildings and the service sector. With the aid of energy characteristics for the different types of buildings the heat demand is calculated in each zipcode 4-area of the Netherlands. Next the cost-effectiveness of district heating to the nearest option (heat source) of district heating for each zipcode 4-area is calculated by the net present value (NPV) viewed from the perspective of the heat supplier. By considering only the nearest heat source for each zipcode 4-area calculation time is limited. A disadvantage of this algorithm is that the cost-effective of the zipcode-4 area to a heat source at larger distance could be better in some cases. So the used algorithm yields the locally optimal solution but gives no guarantee to find the global optimal solution for all heat sources. Nevertheless we conclude that this heuristic approach approximate the global optimal solution. Therefore it is a so called greedy algorithm. We also discuss methods to improve the greedy algorithm. Finally we present some results of an analysis made with the aid of the model. The economic profitable CO2-reduction potential of energy conservation and district heating is 15 - 30 percent of the emission of the built environment in 2050. The range depends on the assumed energy prices and investment costs of the energy conservation measures. The contribution of district heating is 10 to 15 percent point.

Suggested Citation

  • Sebastiaan van Bemmel & Rob Folkert & Ruud van den Wijngaart, 2013. "Spatial energy model for the CO2-reduction of the built environment by district heating in the Netherlands," ERSA conference papers ersa13p81, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa13p81
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    1. Maarten Hilferink & Piet Rietveld, 1999. "LAND USE SCANNER: An integrated GIS based model for long term projections of land use in urban and rural areas," Journal of Geographical Systems, Springer, vol. 1(2), pages 155-177, July.
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