In optimisation models, the characteristic features of capacity investment and dispatch planning of supply technologies are well explored for electricity systems. Little attention to date has been paid to exploring these features in an adequate manner for heat systems. This paper discusses the implementation of decentralised residential heat supply in optimising energy system models and presents an application in TIMES, an energy system model with broad application. It is shown that standard TIMES does not allow for an appropriate implementation of residential heat systems. Consequently a two-part solution procedure is presented. A special integrated TIMES feature is explored and a tailor-made methodological extension of the TIMES standard code developed. Mixed integer programming is employed to accurately model the capacity planning of residential heat supply systems. Model results yielded by the new approach comply with the realistic capacity planning of heat supply systems in a shortened computation time. A case study for selected residential heat demand categories yields contrasting results for the mixed integer programme and the standard linear programme. The model's extension to cover larger, centralised combined heat and power plants and district heating networks remains for further work; the lower solution times encountered seem promising in this respect.
Bibliographical noteAcknowledgements: This paper was developed within the scope of ESA2. ESA2 is an independent consortium of renowned universities and research institutions from five European countries providing qualified decision support for public and private clients in areas related to energy and environmental policy. ESA2 originated from KIC InnoEnergy within the European Institute of Innovation and Technology (EIT). More information is available at www.esa2.eu.
Finally, the authors would like to thank two anonymous reviewers for their help in improving this article through their constructive and helpful comments and suggestions.
- Capacity planning
- Energy system analysis
- Linear programming
- Mixed integer programming
- Residential heat supply