TY - JOUR
T1 - Design of a biomass-heating network with an integrated heat pump
T2 - A simulation-based multi-objective optimization framework
AU - Chen, Yusheng
AU - Guo, Tong
AU - Kainz, Josef
AU - Kriegel, Martin
AU - Gaderer, Matthias
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11/15
Y1 - 2022/11/15
N2 - 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.
AB - 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.
KW - Biomass heating
KW - Gasifier cogeneration
KW - Genetic algorithm
KW - Heat pump
KW - Integration
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85138771486&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2022.119922
DO - 10.1016/j.apenergy.2022.119922
M3 - Article
AN - SCOPUS:85138771486
SN - 0306-2619
VL - 326
JO - Applied Energy
JF - Applied Energy
M1 - 119922
ER -