Design of a biomass-heating network with an integrated heat pump: A simulation-based multi-objective optimization framework

Yusheng Chen, Tong Guo, Josef Kainz, Martin Kriegel and Matthias Gaderer

Applied Energy, 2022, vol. 326, issue C, No S0306261922011795

Abstract: The integration of compression heat pumps is a promising technology to recover waste heat from exhaust gases of a biomass-based heating network. This requires, however, the consumption of additional electricity, which has a high emission factor or a high price in many countries. In this context, the integration of small-scale gasifier cogeneration is supposed to be superior technology to provide the necessary power for heat pumps. Nevertheless, the multiple possibilities of integrating these components imply a high degree of system complexity and, therefore, higher design requirements. To maximize the benefits of the integrated system, development of an optimization approach at the system level is necessary, so that the connection variants of the heat pump, the installation of gasifier cogeneration, and their optimal design can be adequately planned. This work introduces a multi-objective simulation–optimization framework for the design of the proposed integrated system based on the genetic algorithm, taking into account the complex thermodynamic processes as well as the techno-economic performances and environmental impacts of the concepts. As a case study, an existing biomass heat network located in Germany is investigated to test the capabilities of the proposed approach. The analysis of the optimization results demonstrates that it is possible to ensure the effective utilization of biomass resources while simultaneously achieving the economic and environmental compatibility of the system through an appropriate optimization design. The proposed simulation–optimization framework allows decision-makers to achieve an optimal system design under the given constraints and the chosen objectives.

Keywords: Heat pump; Biomass heating; Gasifier cogeneration; Integration; Optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.apenergy.2022.119922

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