Abstract
Fossil fuels have been heavily exploited since the Industrial Revolution. The resulting carbon emissions are widely regarded as being the main cause of global warming and climate change. Key mitigation technologies for reducing carbon emissions include carbon capture and storage (CCS) and renewables. According to recent analysis of the International Energy Agency, renewables and CCS will contribute more than 50% of the cumulative emissions reductions by 2050. This paper presents a new mathematical programming model for multi-footprint energy sector planning with CCS and renewables deployment. The model is generic and considers a variety of carbon capture (CC) options for the retrofit of individual thermal power generation units. For comprehensive planning, the Integrated Environmental Control Model is employed in this work to assess the performance and costs of different types of power generation units before and after CC retrofits. A case study of Taiwan’s energy sector is presented to demonstrate the use of the proposed model for complex decision-making and cost trade-offs in the deployment of CC technologies and additional low-carbon energy sources. Different scenarios are analysed, and the results are compared to identify the optimal strategy for the energy mix to satisfy the electricity demand and the various planning constraints.
Highlights
Climate change is largely due to the relentless rise in carbon dioxide (CO2 ) levels in the atmosphere since the Industrial Revolution, stemming from the world’s heavy reliance on fossil fuels
The capacity of thermal power plants for carbon capture and storage (CCS) is significantly increased when the gas plant capacity factor increases to 70%, there is no need for auxiliary steam turbines or additional renewable energy (RE)
A generic mathematical model for multi-footprint energy sector planning with CCS and renewables deployment has been developed in this paper
Summary
Climate change is largely due to the relentless rise in carbon dioxide (CO2 ) levels in the atmosphere since the Industrial Revolution, stemming from the world’s heavy reliance on fossil fuels. Bandyopadhyay and co-workers developed an algebraic pinch-based targeting technique for grid-wide CCS retrofit planning [28] and an optimisation model for low-carbon power generation planning [29]. The latter contribution overcame a limitation of an earlier study [30], in which it was assumed that no existing power plants could be retrofitted for CCS. Other works on the use of optimisation models addressed economic impacts in achieving CO2 emissions reduction targets [37], parametric uncertainties in technological and cost coefficients [38], uncertain future electricity demand [39], and multistage generation expansion planning (GEP) with CCS [40]. Conclusions and prospects for future work are given at the end of the paper
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