Unit-level cost-benefit analysis for coal power plants retrofitted with biomass co-firing at a national level by combined GIS and life cycle assessment

To avoid the irreversible impact of global climate change on human society, many countries have recently put forward ambitious goals to accelerate the low-carbon transition of energy systems. Among low-carbon measures, retrofitting existing coal power plants with biomass co-firing is regarded as a promising cost-efficient option to mitigate greenhouse gas and air pollutant emissions. However, the life-cycle economic cost or environmental benefit of this coal-to-biomass retrofit is not identical for various types of power plants in different regions. To facilitate a more efficient biomass utilization strategy in an energy system, it is necessary to carry out a largescale and high-resolution cost-benefit assessment for the co-firing of biomass and coal in retrofitted plants. Taking China as an example, this study utilized a bottom-up approach and geographic information system, combined with the latest available datasets, to develop a unit-level cost-benefit analysis framework for the coal to-biomass transition. The results indicate that the coal-to-biomass retrofit costs US$18.3-73.0 for each ton of carbon reduction, and US$21.6-806.5 for each kg of SO2 reduction, at a 25% blending ratio. Installed capacity, operating year, and transportation distance are important influencing factors of cost-benefit heterogeneity. The priority of power unit retrofitting in terms of carbon reductions mainly depends on the cost preference, while that for SO2 reduction is mainly determined by benefit preference. The analytical framework proposed in this study can be used in other countries to formulate an efficient biomass development strategy.

Automated summary

To mitigate greenhouse gas and air pollutant emissions, retrofitting coal power plants with biomass co-firing is seen as a cost-efficient option. The study found that the cost-benefit heterogeneity of coal-to-biomass retrofit depends on factors like installed capacity, operating year, and transportation distance, while the priority for carbon and SO2 reductions is determined by cost and benefit preferences, respectively. The analytical framework proposed can be applied in other countries to develop efficient biomass strategies.