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Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage

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  • Schellenberg, C.
  • Lohan, J.
  • Dimache, L.

Abstract

Grid-edge technology can unlock flexibility from consumers to contribute to meeting the growing need for flexibility in European energy systems. Furthermore, power-to-heat technology such as heat pumps and thermal energy storage has been shown to both decarbonise heat and enable the cost-effective integration of more renewable electricity into the grid. The consumer's reaction to price signals in this context presents the opportunity to simultaneously unlock operational cost reductions for consumers and localised implicit demand-side flexibility to benefit grid operators.

Suggested Citation

  • Schellenberg, C. & Lohan, J. & Dimache, L., 2020. "Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:rensus:v:131:y:2020:i:c:s1364032120302574
    DOI: 10.1016/j.rser.2020.109966
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    3. Jelić, Marko & Batić, Marko & Krstić, Aleksandra & Bottarelli, Michele & Mainardi, Elena, 2023. "Comparative analysis of metaheuristic optimization approaches for multisource heat pump operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
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    6. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    7. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility," Applied Energy, Elsevier, vol. 314(C).

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