Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities

Arslan Bashir*, Andreas Lund, Mahdi Pourakbari Kasmaei, Matti Lehtonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
94 Downloads (Pure)


To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments aswell as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, theneed for storage is also increasing to maintain a continuous power supply. Hence, this paper presentsa MILP-based model to optimize the size of thermal storage required to satisfy the annual DHdemand of a community solely by P2H conversion employing renewable energy. The DH is suppliedby the optimal operation of a novel 2-km deep well heat pump system (DWHP) equipped withthermal storage. To avoid computational intractability, representative time steps with varying timeduration are chosen by employing hierarchical agglomerative clustering that aggregates adjacenthours chronologically. The value of demand response and the effect of interannual weather variabilityare also analyzed. Numerical results from a Finnish case study show that P2H conversion utilizingsmall thermal storage in tandem with the DWHP is able to cover the annual DH demand, thus leadingto a carbon-neutral DH system and, at the same time, mitigating the curtailment of excessive windgeneration. Compared with the annual DH demand, an average thermal storage size of 29.17 MWh(2.58%) and 13.99 MWh (1.24%) are required in the business-as-usual and the demand responsecases, respectively.
Original languageEnglish
Article number1911
Number of pages20
Issue number7
Publication statusPublished - 30 Mar 2021
MoE publication typeA1 Journal article-refereed


  • power-to-heat
  • sector coupling
  • thermal storage
  • district heat
  • Deep well heat pump
  • hierarchical agglomerative clustering
  • chronology
  • demand respond
  • two-capacity building model


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