| Title | An intelligent platform for evaluating investment in low-emissions technology for clean power production under ETS policy |
|---|---|
| ID_Doc | 14490 |
| Authors | Manaf, NA; Milani, D; Abbas, A |
| Title | An intelligent platform for evaluating investment in low-emissions technology for clean power production under ETS policy |
| Year | 2021 |
| Published | |
| Abstract | This study develops an investment decision making platform for carbon capture and sequestration (CCS) tech-nology using an artificial intelligent (AI) algorithm featuring an optimization via a mixed integer non-linear programming (MINLP) formulation. This computational strategy offers a smart rapid investment decision eval-uation of CCS technology through several economic-environmental-technical-policy (EETP) uncertainties. This is applied to a coal-fired power plant (PP) in Shenzhen, China. Historical (2019) and forecast (2030) operations are evaluated under dynamic and static carbon price regimes. Scenario 1 under dynamic carbon pricing exhibits a positive (sustainable) investment decision for CCS deployment at 28% net revenue gain of selling electricity. Scenarios 2-4 feature negative (unsustainable) investments for CCS technology at 44%, 7% and 66% net revenue loss, respectively. Carbon price is identified to be the dominant variable/uncertainty in recognizing the sus-tainability outcome of CCS investment followed by the combined market trends of coal and electricity prices. This current work demonstrates a computation approach for dealing with all the uncertainties at hand and is therefore necessary and critical for rational future investment decisions and operations in clean power pro-duction (as demonstrated in this PP + CCS context), suggesting the EETP objectives cannot be met without intelligent algorithmic operations. The present analysis exemplifies the trade-offs mainly between the cost of CO2 emission and the cost of PP operation with CCS. It can be used as an indicator on the energy transformation readiness based on current and forecast global conditions. This algorithmic approach can be generalized and extended to other cleaner power production processes and to alternative energy-based industrial symbiosis (IS), which collectively aims to mitigate the use of traditional fuel (i.e. coal) and subsequently stimulating a circular economy energy transition. |
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