Stochastic MILP model for optimal timing of investments in CO2 capture technologies under uncertainty in prices

Reduction in greenhouse gas emissions of existing coal-fired power plants is a necessary action to attain the global reductions committed in the Kyoto Protocol. In the framework of a cap and trade system, we propose a two-stage stochastic mixed-integer linear programming (MILP) approach for the optimal investment timing and operation of a CO2 capture system under uncertainty in the CO2 allowance price. In the MILP, uncertainties are modeled via scenarios that are generated from a set of probability functions obtained using the Geometric Brownian Motion (GBM) approach in conjunction with Monte Carlo sampling. The model takes into account two economic objectives: the expected net profit and the financial risk. We demonstrate the capabilities of the tool presented through a case study based on a coal fired power plant. Our MILP approach can be applied to a wide range of processes and industries that deal with carbon sequestration issues.

► We search the optimal investment timing and operational decisions in CCS technology.
► We propose a two-stage stochastic mixed-integer linear programming problem (MILP).
► Uncertain CO2 prices are introduced via scenarios using GBM and Monte Carlo sampling.
► Solutions selected must maximize the expected profit and minimize the financial risk.
► This decision tool will help to select between risk-taker and risk-averse solutions.