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Spatio-temporal potential of a biogenic micro CHP swarm in Switzerland

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  • Buffat, René
  • Raubal, Martin

Abstract

Renewable electricity production from intermittent energy sources, such as wind and photovoltaic, is rapidly increasing worldwide. The Swiss Energy Strategy aims at generating roughly one-third of the current electricity demand through intermittent renewables by 2050. This raises the need for flexible on-demand power production capabilities to balance the fluctuating generation. Next to batteries or pumped-storage hydroelectricity, decentralized gas-fired combined heat and power (CHP) plants have fast ramp-up times and can thus be used to balance the fluctuating production. Using biogas as fuel for the CHP plants minimizes greenhouse gas (GHG) emissions. However, biomass is subject to spatio-temporal variation that can limit the available fuel. In this work, a framework of geographic information system (GIS)-based models to assess the spatio-temporal potential of domestic biogas to balance fluctuating renewable energy production is proposed. Using this framework, the spatio-temporal biogas that can be fed into the gas grid is modeled to be 18.4PJ. By comparing this potential with the spatio-temporal residential heat demand and rooftop PV potential, we identify the spatio-temporal balancing potential of decentralized CHP plants in Switzerland. It is found that the balancing potential varies between districts and over the course of a year. During summer, the potential is limited due to lack of local heat demand.

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  • Buffat, René & Raubal, Martin, 2019. "Spatio-temporal potential of a biogenic micro CHP swarm in Switzerland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 443-454.
    • Handle: RePEc:eee:rensus:v:103:y:2019:i:c:p:443-454
    DOI: 10.1016/j.rser.2018.12.038
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    2. Vanessa Burg & Farzin Golzar & Gillianne Bowman & Stefanie Hellweg & Ramin Roshandel, 2021. "Symbiosis opportunities between food and energy system: The potential of manure‐based biogas as heating source for greenhouse production," Journal of Industrial Ecology, Yale University, vol. 25(3), pages 648-662, June.
    3. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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