Optimizing heat integration in a flexible coal–natural gas power station with CO2 capture

Abstract

Computational optimization is used to simultaneously determine the design and planned operating profile of a flexible coal–natural gas power station with CO₂ capture, under a CO₂ emission performance standard. The facility consists of a coal-fired power station undergoing retrofit with CO₂ capture. The CO₂ capture energy demand is provided by a specially designed combined cycle gas turbine (CCGT). The heat recovery steam generator (HRSG) component of the CCGT is modeled and optimized in detail, with explicit treatment of the discrete aspects of the HRSG configuration, including the number and sequential arrangement of HRSG internal components. Variable facility operations are represented by discrete operating modes selected based on the electricity price–duration curve. Two objectives, the minimization of capital requirement and the maximization of net present value, are considered in a bi-objective mixed-integer nonlinear programming formulation. Pareto frontiers, which define the optimal tradeoffs between these two objectives, are generated for six scenarios constructed from recent historical data from West Texas, the United Kingdom, and India.

For a 440 MW coal plant in a scenario based on 2011 West Texas data, the minimum effective net present cost required for the retrofit (which meets the CO₂ emission performance standard) varies from $278 to 383 million, and the minimum total capital investment requirement ranges from $346 to 517 million. The variations in these optimized values correspond to the range of the Pareto frontier within the bounds of the problem. The net present cost of the retrofit is less than the present value of the existing coal plant, $476 million, indicating that a retrofit is preferred over decommissioning. In the case of very low energy prices, however, decommissioning is shown to be the preferred option. The UK and India scenarios demonstrate that optimal designs can vary greatly depending upon location-specific economic conditions.