Research article
An optimization model for carbon capture & storage/utilization vs. carbon trading: A case study of fossil-fired power plants in Turkey

https://doi.org/10.1016/j.jenvman.2018.03.054Get rights and content

Highlights

  • A mathematical model was developed to choose between CCS/CCU or carbon trading.

  • A case study was realized by analyzing two real-life coal power plants in Turkey.

  • The effects of carbon price, transportation and storage distances were studied.

  • As long as there is enough demand, CCU was found to be more preferable over CCS.

  • When carbon prices fall below $15/ton, carbon trading became more feasible.

Abstract

We consider fossil-fired power plants that operate in an environment where a cap and trade system is in operation. These plants need to choose between carbon capture and storage (CCS), carbon capture and utilization (CCU), or carbon trading in order to obey emissions limits enforced by the government. We develop a mixed-integer programming model that decides on the capacities of carbon capture units, if it is optimal to install them, the transportation network that needs to be built for transporting the carbon captured, and the locations of storage sites, if they are decided to be built. Main restrictions on the system are the minimum and maximum capacities of the different parts of the pipeline network, the amount of carbon that can be sold to companies for utilization, and the capacities on the storage sites. Under these restrictions, the model aims to minimize the net present value of the sum of the costs associated with installation and operation of the carbon capture unit and the transportation of carbon, the storage cost in case of CCS, the cost (or revenue) that results from the emissions trading system, and finally the negative revenue of selling the carbon to other entities for utilization. We implement the model on General Algebraic Modeling System (GAMS) by using data associated with two coal-fired power plants located in different regions of Turkey. We choose enhanced oil recovery (EOR) as the process in which carbon would be utilized. The results show that CCU is preferable to CCS as long as there is sufficient demand in the EOR market. The distance between the location of emission and location of utilization/storage, and the capacity limits on the pipes are an important factor in deciding between carbon capture and carbon trading. At carbon prices over $15/ton, carbon capture becomes preferable to carbon trading. These results show that as far as Turkey is concerned, CCU should be prioritized as a means of reducing nation-wide carbon emissions in an environmentally and economically rewarding manner. The model developed in this study is generic, and it can be applied to any industry at any location, as long as the required inputs are available.

Section snippets

Introduction and literature review

Greenhouse gas (GHG) emissions have been increasing steadily since the beginning of industrial revolution. Over the last decade, annual GHG emissions have increased by an average of 2.7% (Cuéllar-Franca and Azapagic, 2015). Since 1990s two major worldwide gatherings took place, one in Kyoto and the other one in Paris, in 1997 and 2015, respectively. In these meetings, it has been scientifically suggested that the average global temperature increase as a result of climate change should be

Problem description and methodology

We give the problem definition and the mathematical model in this section.

Case study

We consider two power plants located in Soma (west of Turkey) and Afsin (southeast of Turkey). They are indicated as stars in Fig. 2. These power plants are lignite-fired power plants with installed capacities of 1000 MW and 1800 MW, respectively. Both plants operate with a capacity factor of 85%. Although there is currently no operational ETS in operation in Turkey, the Directorate General of Environmental Management under the Ministry of Environment and Urbanization is currently investigating

Results

We implemented the model on General Algebraic Modeling System (GAMS) on a machine with Intel Xeon 2.27 GHz CPU, 24 GB RAM and Windows 2008 Server R2 operating system. As explained in the previous section, the objective function includes a nonlinear term, which is the installation cost of carbon capture units. Therefore, we first select BARON (Sahinidis, 2015), which guarantees to find global optimal solution for MINLPs, as the solver. However, the solver experienced numerical difficulties and

Conclusion

In this study we developed a mixed-integer programming model that aims to minimize the cost of decisions on the carbon capture unit for a set of thermal power plants and pipeline cost for transporting carbon less the revenue that would be obtained by selling carbon to entities and/or in an emissions trading scheme. Based on economic conditions, such as carbon price and carbon cap, the power plant can choose to install a carbon capture unit or not. The latter option involves purchasing credits

References (49)

  • T. Kruger

    Conflicts over carbon capture and storage in international climate governance

    Energy Policy

    (2017)
  • R.S. Middleton et al.

    A scalable infrastructure model for carbon capture and storage: SimCCS

    Energy Pol.

    (2009)
  • R.S. Middleton et al.

    The complex future of CO2 capture and storage: variable electricity generation and fossil fuel power

    Appl. Energy

    (2013)
  • R.S. Middleton et al.

    Generating candidate networks for optimization: the CO2 capture and storage optimization problem

    Comput. Environ. Urban Syst.

    (2012)
  • R.S. Middleton et al.

    A dynamic model for optimally phasing in co 2 capture and storage infrastructure

    Environ. Model. Softw.

    (2012)
  • R.E. Ooi et al.

    Planning of carbon capture and storage with pinch analysis techniques

    Chem. Eng. Res. Des.

    (2013)
  • R.T. Porter et al.

    Cost and performance of some carbon capture technology options for producing different quality CO2 product streams

    Int. J. Greenh. Gas Control

    (2017)
  • P.S. Roychaudhuri et al.

    Financial pinch analysis: minimum opportunity cost targeting algorithm

    J. Environ. Manag.

    (2018)
  • E.S. Rubin et al.

    The cost of CO2 capture and storage

    Int. J. Greenh. Gas control

    (2015)
  • E.D. Santibanez-Gonzalez

    A modelling approach that combines pricing policies with a carbon capture and storage supply chain network

    J. Clean. Prod.

    (2017)
  • P. Stauffer et al.

    System integration linking CO2 sources, sinks, and infrastructure for the Ordos Basin, China

    Energy Procedia

    (2014)
  • R.R. Tan et al.

    P-graph and Monte Carlo simulation approach to planning carbon management networks

    Comput. Chem. Eng.

    (2017)
  • F.G. Üçtuğ et al.

    Deciding between carbon trading and carbon capture and sequestration: an optimisation-based case study for methanol synthesis from syngas

    J. Environ. Manag.

    (2014)
  • S. Valiani et al.

    Optimization of pre-combustion capture for thermal power plants using pinch analysis

    Energy

    (2017)
  • Cited by (47)

    View all citing articles on Scopus
    View full text