Influences of climatic environment on the geothermal power generation potential
Influences of climatic environment on the geothermal power generation potential
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DOI:
10.1016/j.enconman.2022.115980
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发表时间:
2022-09
影响因子:
10.4
通讯作者:
Jian Li;Zhen Yang;Zitao Yu;Jun Shen;Yu Duan
中科院分区:
文献类型:
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作者:
Jian Li;Zhen Yang;Zitao Yu;Jun Shen;Yu Duan
Geothermal power generation has wonderful application prospects due to good continuity, little influence by weather, and large source reserves. However, the influences of climatic environment on power generation potential are vague due to few studies, especially for wide latitude range and multiple evaluation dimensions. This work takes organic Rankine cycle (ORC) as the power generation system and selects six cities in different kinds of climatic environments as analysis sites. Quantitative impacts of climatic environment on system performance were evaluated to reveal the key factor affecting the power generation potential. Off-design analysis was carried out to obtain real annual operating performance. Results show that the cooling mode is the key factor determining geothermal power generation potential, and the water-cooling mode is preferred with better thermodynamic and economic performance. The comparative advantage of water-cooling mode is more prominent at the low geothermal temperature. The geothermal power generation potential is not necessarily better with decreasing ambient temperature because it needs to adopt air-cooling mode for ambient temperature below 0 °C, worsening thermodynamic and economic performance. Improving performance of air-cooling system can substantially enhance thermo-economic performance, but the comparison results between air-cooling and water-cooling modes will not be reversed. Reducing the costs of cooling system and turbine are key points of enhancing thermo-economic performance for air-cooling and water-cooling modes, respectively. Off-design analysis is necessary for evaluating power generation potential, especially for air-cooling mode. Only evaluating design-condition performance may underestimate the actual power generation by 7.6%.