Multi-level investment planning and scheduling under electricity andcarbon market dynamics: retrofit of a power plant with PCC (post-combustion carbon capture) processes

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This paper addresses four levels in carbon management decision-making: government, enterprise, plant, and process. Robust decision-making at any level requires consideration of the constraints and requirements of other levels. The focus of the paper is the enterprise level, when a power generating company wishes to develop its long term carbon management strategy. The carbon reduction option is solvent-based PCC (post-combustion carbon capture), which has been discussed as the most accessible option for CCS (carbon capture and storage) objectives. The company desires to know whether/when/how to invest in PCC processes in order to satisfy government emission reduction regulations while achieving the maximum economic benefits over the planning horizon. We have developed a multi-period MILP (mixed-integer linear program) with the objective of maximizing NPV (net present value). The model is capable of finding the best investment decision, i.e. whether to invest in a PCC process or pay for the carbon tax/permit. When a PCC process is beneficial, the program determines the number of PCC trains (of different sizes) and the optimal installation time of each process. The model incorporates dynamic electricity and carbon market prices over the planning horizon. This allows the model to define the best operation strategy of a power plant and PCC process to utilize the maximum benefits of market prices by periodic adjustment of power generation and carbon capture rate. With this information, the company can buy or sell carbon permits over the planning horizon when either is more economical.

Original languageEnglish
Pages (from-to)172-186
Number of pages15
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Carbon tax
  • Coal-fired power plant
  • Decision making
  • Flexible operation
  • Planning and scheduling
  • Post-combustion carbon capture

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