On the economic potential for electric load management in the German residential heating sector - An optimising energy system model approach

Daniel Fehrenbach*, Erik Merkel, Russell McKenna, Ute Karl, Wolf Fichtner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

Against the background of the ambitious German targets for renewable energy and energy efficiency, this paper investigates the economic potential for thermal load management with virtual power plants consisting of micro-cogeneration plants, heat pumps and thermal storage within the residential sector. An optimising energy system model of the electricity and residential heat supply in Germany is developed in the TIMES (The Integrated MARKAL EFOM System) modelling framework and used to determine capacity developments and dispatch of electricity and residential heat generation technologies until 2050. The analysed scenarios differ with respect to the rate of technological development of heat and power devices, fuel and CO2 prices as well as renewable electricity expansion. Results show that high fuel prices and a high renewable electricity expansion favour heat pumps and insulation measures over micro-cogeneration, whereas lower fuel prices and lower renewable electricity expansion relatively favour the expansion of micro-cogeneration. In the former case heat pump capacities increase to around 67GWel, whereas in the latter case the total capacity of micro-cogeneration reaches 8GWel. With the aid of thermal storage, this provides considerable flexibility for electrical load shifting through heat pumps and electricity generation from micro-cogeneration in residential applications, needed for the integration of fluctuating renewable electricity technologies.

Original languageEnglish
Pages (from-to)263-276
Number of pages14
JournalEnergy
Volume71
DOIs
Publication statusPublished - 15 Jul 2014

Keywords

  • Demand side management
  • Energy storage
  • Heat pumps
  • Micro-CHP
  • Optimising energy system model in TIMES
  • Residential heating

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