Operational power plant scheduling with flexible carbon capture: A multistage stochastic optimization approach

Abstract To mitigate CO2 emissions, it is often suggested that power plants deploy carbon capture systems. However, high cost is an impediment for the deployment of these systems. To counter this, a power plant can be integrated with a flexible capture unit which varies its load with fluctuating electricity prices. In this work, a multi-stage stochastic programming approach is applied to optimally schedule power production and carbon capture operations to maximize daily profit in a market with uncertain hourly electricity prices. Low-complexity surrogate models are developed for optimal action policy at each stage, which reduce the computational complexity of estimating profit for different price scenarios. The expected value of perfect information obtained is within 25% of the maximum achievable profit while meeting the CO2 emission constraints. Moreover, the profitability improves by 40% compared with the deterministic case assuming expected values of stochastic parameters. This demonstrates the quality of the stochastic solution.