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Techno-economic assessment of solar assisted precinct level heating systems with seasonal heat storage for Australian cities

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  • Chu, Shunzhou
  • Sethuvenkatraman, Subbu
  • Goldsworthy, Mark
  • Yuan, Guofeng

Abstract

District or precinct level heating systems are a promising way to incorporate solar for decarbonising the building heating sector. Despite growing interest in these technologies in Europe and China, there are no large-scale district heating systems in Australia. This study shows that a combined solar heat driven Domestic Hot Water (DHW) and Space Heating (SH) system with seasonal storage is technically and economically feasible for major Australian cities. A precinct scale solar-assisted domestic hot water and space heating system with seasonal heat storage has been designed and evaluated for Australian conditions. Technical performance was evaluated using solar fraction, storage efficiency and heat collection efficiency parameters. Economic performance was evaluated using the Levelized Cost of Heat for solar thermal (LCOHst) and whole system Levelized Cost of Heat (LCOH) with a combination of component cost data from China and Europe and energy cost data from Australia. A parametric study to identify optimal collector area and storage volume sizes shows that the proposed system could achieve technical and economic targets in all five Australian cities considered. The lowest LCOHst is 0.0597 USD/kWh in Hobart. Sydney and Perth could use nearly 100% solar energy to cover the heating demand with the proposed design.

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  • Chu, Shunzhou & Sethuvenkatraman, Subbu & Goldsworthy, Mark & Yuan, Guofeng, 2022. "Techno-economic assessment of solar assisted precinct level heating systems with seasonal heat storage for Australian cities," Renewable Energy, Elsevier, vol. 201(P1), pages 841-853.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:841-853
    DOI: 10.1016/j.renene.2022.11.011
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    References listed on IDEAS

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    2. Behzadi, Amirmohammad & Sadrizadeh, Sasan, 2023. "Grid-tied solar and biomass hybridization for multi-family houses in Sweden: An optimal rule-based control framework through machine learning approach," Renewable Energy, Elsevier, vol. 218(C).

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