Journal of Cleaner Production xxx (2017) 1e16
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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
A modelling approach that combines pricing policies with a carbon
capture and storage supply chain network
Ernesto D.R. Santibanez-Gonzalez
Departamento de Ingeniería Industrial, Universidad de Talca, Curico, Chile
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 19 June 2016
Received in revised form
10 March 2017
Accepted 27 March 2017
Available online xxx
This article examines the interaction between two strategies to reduce carbon dioxide (CO2) emissions to
the atmosphere: the imposition of pricing policies on carbon dioxide emissions and the decision on the
network infrastructure for carbon dioxide capture and storage (CCS). The uncertainty of the storage
capacity of geological reservoirs for sequestering carbon dioxide has been noted as an important issue in
the deployment and cost of a CCS infrastructure. To analyse the relationships between these strategies
and the uncertainty of the storage capacity of reservoirs, we propose a novel stochastic mixed-integer
linear optimisation model. It minimises investment and construction costs and the costs of capture,
transport, and storage of CO2 plus the cost of emitting CO2 to the atmosphere. The model considers
technical and economic aspects to resolve both CO2 pricing and the design of a supply chain network for
capturing, transporting, and sequestering CO2 in geological reservoirs. We present results for a sample
case from the Brazilian cement industry using a CO2 tax as currently established in other countries. We
verify that, while the CO2 tax increases, it also increases the complexity of the pipeline network of the
supply chain and the amount of CO2 that is captured and stored.
© 2017 Published by Elsevier Ltd.
Handling Editor: R.L. Lozano
Keywords:
Capture and sequestration
Carbon emission price
Stochastic mixed integer linear optimisation
Supply chain
Climate change
1. Introduction
This article analyses two strategies for reducing CO2 emissions
to the atmosphere in the presence of uncertainty in the storage
capacity of geologic reservoirs: policies providing economic incentives to reduce carbon dioxide emissions in the atmosphere and
the decision on the pipeline network infrastructure for carbon dioxide capture and storage (CCS). We propose a stochastic mathematical optimisation model that simultaneously analyses the
effects of a pricing policy on carbon dioxide emissions (carbon dioxide tax) and implementing a supply chain network to capture,
transport, and sequester CO2. We present the results of a case study
of the cement industry in Brazil to validate the proposed model.
This article contributes to the existing literature because of the
following three aspects that are widely recognised by the scientific
community:
a. - Carbon dioxide capture and storage is one of the strategies that
have been proposed to mitigate the economic, environmental,
and social impacts of climate change. The design of the CCS
E-mail address: santibanez.ernesto@gmail.com.
network is an approach to address the reduction of CO2 emissions and related to other operational research problems in the
literature is a complex problem of combinatorial optimization.
b. - Economic incentives to reduce carbon dioxide emissions are
being implemented in different countries with different levels of
success, with Ireland, Norway, Switzerland, and Taiwan as
examples.
c. - Uncertainty in the storage capacity of geologic reservoirs has
been recognised as an important issue because of its impact on
the CCS infrastructure network and the resulting costs.
Climate change is one of the most important concerns for the
world today, as its consequences will extend beyond the bounds of
individual businesses and national borders, affecting the global
economy, the environment, and human health. The continuously
increasing concentrations of carbon dioxide in the atmosphere
have made the multidisciplinary problem of global warming a topic
of frequent discussion and debate. To solve this problem and to
achieve acceptable levels of carbon dioxide in the atmosphere,
scientists agree on the need to implement three strategic options to
reduce carbon dioxide emissions: (1) improve the efficiency of
energy generation and usage, (2) develop energy sources with
lower levels of carbon dioxide emissions and (3) implement carbon
http://dx.doi.org/10.1016/j.jclepro.2017.03.181
0959-6526/© 2017 Published by Elsevier Ltd.
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
2
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
dioxide capture and storage projects. Undoubtedly, the success of
these efforts in reducing carbon dioxide emissions and mitigating
climate change will depend on the coordinated implementation of
these three approaches. According to Pacala and Socolow (2004),
reducing annual carbon dioxide emissions by 3.7 Gt carbon dioxide
would require the capture and sequestration of the carbon dioxide
emissions of a group of coal plants with the capacity to generate
800 GW of energy, which represents 2.5 times the total electricity
generating capacity of the coal-fired plants of the United States
considering 2010 as the base year (EIA, 2011). Carbon dioxide
capture and storage (CCS) is a climate change mitigation strategy
that can significantly reduce CO2 emissions while the world is still
heavily relying on fossil fuels and it makes the transition to a lowcarbon future more difficult (Dooley et al., 2006). According to the
International Energy Agency (IEA), about 20% of the total greenhouse gas emission reductions required by 2050 could be provided
by CCS (IEA, 2009). Depending on the size of each CCS project, this
means about 3400 CCS projects would be needed by 2050,
involving an investment of billions of dollars and thousands of
miles of dedicated carbon dioxide pipelines (Condor et al., 2009).
Infrastructure on this scale needs careful and comprehensive
planning. Practitioners, academicians, stakeholders and policymakers need to understand how to deploy such a largescale CCS
infrastructure and to make informed decisions based on the solutions provided by the optimization model that takes into account
detailed costs, engineering, and environmental concerns
(Middleton et al., 2012c).
In a nutshell, the geological-technological problem of carbon
dioxide capture and storage consists of three basic stages: (a)
capturing carbon dioxide from various emissions sources (e.g.,
plants generating electricity based on coal, gas, or oil; cement, steel,
or iron manufacturing companies, refineries, and other sources),
which requires separating the carbon dioxide from other gas
components; (b) compressing and transporting the captured carbon dioxide, and finally (c) injecting and sequestering the carbon
dioxide in different underground geological reservoirs (e.g., deep
saline aquifers located at several different depths, wells depleted of
oil or gas, and deep-ocean reservoirs). Note that the technology
required to perform this type of operation already exists, and there
are also several projects throughout the world currently in operation at different scales and at varying levels of advancement. For
example, there are projects underway in the Netherlands, Europe,
Norway, and the United States, among others (Global CCS Institute,
2014).
This article aims to propose a mathematical model which analyses and explores the relationship between establishing a pricing
policy (tax) on carbon dioxide emissions to the atmosphere and the
design of a supply chain (SC) network to capture and sequester
carbon dioxide in geological reservoirs. The economic impact of
these policies is taking into account. In this paper, the uncertainty
in the storage capacity of the reservoirs and its impacts on the
whole CCS infrastructure network is modeled and analysed. We
explore the uncertainty using the reservoir capacity as a variable.
The uncertainty in geologic properties and processes associated
with CO2 injection and storage have been studied by several authors (Eg., Bachu, 2003; Buscheck et al., 2011; Iea Ghg, 2009).
Morbee et al. (2012) present a model that considers future capture
scenarios as stochastic, and determine the optimal network that
would be robust against such uncertainties is a computationally
very intensive approach. A natural extension of a deterministic
model is to use a stochastic model. Stochastic mathematical programming models, in particular facility location models, “capture
the complexity inherent in real-world problems through probability distributions of random variables or considering a set of
possible future scenarios for the uncertain parameters” (Snyder,
2006; Snyder et al., 2006). The effects of reservoir uncertainty on
CCS network infrastructure costs have been studied by McCoy and
Rubin (2009). They concluded that “uncertainty in reservoir geology and petrophysical properties controlled overall levelized cost
for storage”. Thus the variation in the “storage capacity of CO2 may
propagate throughout a CCS infrastructure network and end up
driving how this CCS infrastructure is deployed” (Middleton et al.,
2012a).
With that aims, it is proposed a Stochastic Mixed Integer Linear
Programming Problem (SMILP). The objective of the model is to
minimise the investment, operating, and transportation costs as
well as the taxes paid by companies as a mechanism of reducing
CO2 emissions to the atmosphere. Several technical constraints are
taking into account including the number and location of CO2
capture plants and reservoirs to be opened and the deployment
(and size) of pipelines among others. In some ways, the approach
used here to model the CCS infrastructure problem can be
compared to those used to model facility location problems and
network flow design problems (e.g., Melkote and Daskin, 2001).
Relating this CCS infrastructure problem to other problems in the
literature, such as facility location problems and network flow
optimisation problems with fixed costs, we can say that this is an
NP-Complete combinatorial optimisation problem and that it is
difficult to solve.
Recently, the problem of deploying a CCS infrastructure has
been studied independently by several authors (for instance, Kuby
et al., 2011b; l Middleton and Bielicki, 2009a; Middleton et al.,
2012b; Sun and Chen, 2016; Walsh et al., 2014) with and without
the implementation of economic incentive policies. Some authors
have proposed methodologies that seek to partially solve the CCS
infrastructure problem, while in other cases the methodologies are
developed to solve specific case studies (e.g., MIT CCSTP, 2007).
Some authors have also proposed the use of mathematical models
integrated with information systems to address the complexities of
CCS infrastructure problems (e.g., Middleton et al., 2012b). Kuby
et al. (2011a) recently addressed the CCS network design problem
by extending previous work presented by Middleton and Bielicki
(2009a; 2009b). The model determines the CCS infrastructure
network “given de price per tonne to emit CO2 into the atmosphere” and considering that all costs, variables and parameters are
known with certainty. The model proposed in this paper extends
these studies. To validate the proposed model, we conducted a
sample case study of the cement industry in Brazil with real data.
This article makes two important contributions to the literature.
First, it proposes a novel stochastic MILP model that relates the
establishment of a pricing policy on CO2 emissions to the atmosphere with the deployment of a CO2 capture and storage network
infrastructure. The model considers uncertainty in the storage capacity of geologic reservoirs for sequestering CO2. The model also
allows us to evaluate the impact of limiting the number of plants
that can be installed to capture CO2 and the number of reservoirs
that can be opened to store CO2. To the best of our knowledge, this
is the first stochastic MILP model to address this problem as a
network design supply chain problem. Second, this paper analyses
and validates the proposed SMILP model under different pricing
policies and considers the implications of uncertainty in storage
capacity. We use real data from the Brazilian cement industry to
consider the case of mitigating CO2 emissions.
This paper is organised as follows: Section 2 presents a review of
the literature on previous research related to this paper. Section 3
presents the mathematical formulation of the problem. The case
study of the cement industry in Brazil is presented in Section 4, and
the results obtained from the case study are discussed in Section 5.
Finally, Section 6 presents the conclusions of the research.
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
2. Literature review
We briefly discuss previous pertinent research undertaken in
particular on the topic of designing a network infrastructure for CO2
Capture and Storage. For this purpose, the literature can be classified into two major groups: Economic incentives policies to avoid or
control CO2 emissions generated by companies and/or organizations and methodologies to design a network infrastructure to
capture and storage of carbon dioxide. This paper uses the term
network infrastructure to describe the different physical links between sources, reservoirs, and intermediate points.
2.1. Economic incentive policies
Economic incentive policies are designed to discourage CO2
emissions into the atmosphere by create an additional cost to the
emitter. Two basic types of economic incentive policies have been
proposed in the literature as ways of reducing CO2 emissions to the
atmosphere and are described below (Furman et al., 2007).
(a) Establish a tax e called the carbon dioxide tax e on carbon
dioxide emissions. In this case, the government establishes a
cost for emitting one tonne of CO2 to the atmosphere. This
system is also known as the Pigouvian tax, in honor of the
economist who was the first to propose this mechanism. The
concept is that the producer or consumer pays a tax equal to
the social harm of emitting CO2 to the atmosphere. This social cost is difficult to calculate due to the imperfection and
complexity of the market (Yohe et al., 2007) This paper does
not discuss how to determine the real social cost; instead, we
employ the taxes established in other countries as a reference for this study. Based on this tax (price), producers such
as energy generation plants, refineries, and cement factories
must decide how much CO2 they are going to emit to the
atmosphere. The government of Norway implemented a CO2
tax in 1991 and has one of the longest standing projects
operating under such a policy (MIT CCS Technologies, 2011),
the natural gas exploration project of the Statoil Sleipner West
company, which has captured and sequestered approximately 1 million tonnes of CO2 per year since 1996 (MtCO2/
year).
(b) Establish a cap-and-trade mechanism. In this case, the government issues quotas for allowed carbon dioxide emissions
and establishes a mechanism for the trading of these allotments in which companies buy and sell CO2 credits (quotas).
Thus, companies that want to emit CO2 must buy some of
these shares at a price set by the market. Several such systems are currently in operation, including the Acid Rain
Program in the United States, which aims to reduce the
emissions of sulphur dioxide, and the European Union Emissions Trading Scheme to reduce CO2 emissions. This paper
does not consider this regulatory approach.
2.2. Carbon dioxide capture and storage network infrastructure
Previous studies have investigated the problem of modeling and
solving the CO2 capture and storage network infrastructure. To
present an incomplete summary of the research on models and
solution approaches to CCS, we can classify these models as
follows:
i. Models following the methodology of economic evaluation of
investment projects. These studies have contributed to our understanding of the technical complexity involved in these
3
projects, as well as their complex cost structures. Methodologies
have been proposed that allow decision-makers to analyse the
economic, financial, and technological aspects of projects using
rather simple network topologies (Figs. 1 and 2). These approaches use the discounted expected present value of the
benefits and net costs of an investment project as the basic
criterion to solve the problem of capture, transport, and injection of carbon dioxide.
An example of this type of methodologies is the methodology
proposed by MIT CCSTP (2007, 2009). To define the network
infrastructure, it considers sources and potential reservoirs for
carbon dioxide sequestration, methods of calculating the diameters
of the pipes that must be used. The methodology solves the problem of network design assuming direct routes connecting sources
and CO2 reservoirs for sequestration. The selection and construction of pipes between the source and reservoir depends on the
maximum amount of CO2 that can be stored at the destination (or
captured at the source). Once the cost of installation, construction,
transportation and operation has been calculated and the planning
horizon has been determined, then the investment project is
economically evaluated by calculating the net present value (NPV)
and the internal rate of return (IRR). The focus of this approach is
not to propose a mathematical model that simultaneously considers the various aspects of the problem mentioned above. For
example, using this methodology, decision-makers/practitioners
cannot assess the economic impact that could have the combination of different feasible solution paths for connecting each sourcereservoir pair using different types of tubes.
In Energy (2010), some authors described and investigated the
use of slightly more complex carbon dioxide network transport
topologies to solve the problem of network design (e.g., Fig. 2). This
work defines a central route with a huge transport capacity between two points called hubs. One of these points is located near
CO2 emission sources
Geologic reservoirs
TransiƟon points
Fig. 1. Network Infrastructure CCS supply chain direct connection source-reservoir.
hub
CO2 emission sources
Geologic reservoirs
hub
hubs
Fig. 2. Network Infrastructure CCS supply chain source-reservoir connections through
hubs.
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
4
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
the sources and collects the CO2 that comes from these sources, and
the other hub is located near the reservoirs for CO2 sequestration.
To solve this problem, a high capacity pipeline (trunkline) was built
to transport the carbon dioxide between the hub points. Each of the
sources will be directly connected to the closest hub through
pipelines with smaller carbon dioxide transport capacities. The
same procedure is performed for the target reservoirs. From a
managerial point of view, the use of the abovementioned methodologies can have a serious impact on the network infrastructure
and the associated costs. The economic impact of implementing
such simple network infrastructures, such as those presented
below, compared with other more complex network infrastructures has been studied by authors such as Kuby et al.
(2011b). That research established that the total costs of a simple
network infrastructure can be several times higher than the costs of
an optimised network supply chain infrastructure.
Figs. 1 and 2 show a basic network linking sources of carbon
dioxide emissions (blue circles), intermediate locations (yellow
squares), and potential reservoirs (green triangles). The symbol
sizes reflect the volume of carbon dioxide emission, transmission,
or storage at those locations, respectively.
ii. Methodologies based on Integer Linear Programming Models.
This group of methodologies uses Linear Programming (LP) and
MILP models to solve the problem of designing a piping infrastructure network, Pipelines of various dimensions and capacities will capture CO2 from the sources and transport it to the
final storage destinations. This article continues this line of
research.
Middleton and Bielicki (2009b) proposed a MILP model to solve
the problem considering some of the issues we discussed above.
They described a scalable infrastructure information system named
SimCCS that allow managers to economically and geo-spatially
optimise the CCS network project. The proposed model allows
determining the network infrastructure of pipelines to be built and
operated to capture, transport, and sequestering a predetermined
volume of CO2. The model is static, i.e., it does not consider a
planning horizon, and it is also deterministic because it does not
consider the uncertainty in any aspect of the problem. The model
allows the user to choose which CO2 sources will be captured,
which reservoirs the CO2 will be injected and sequestered in and at
what quantities, where to build the network of tubes (i.e., the core
network paths that will be used), and the capacity (diameter) of
each tube. The optimisation of the objective function determines
the cost of investment, construction, operation, and transportation
for a pre-set level of CO2 capture. This model was also applied to
solve a network design problem for a hypothetical CCS case in
California, USA (Middleton and Bielicki, 2009a). A total of twelve
energy generation plants, oil refineries, and cement plants were
considered as possible sources of CO2 emissions and two potential
deep-sea basalt sites were considered for CO2 sequestration.
Keating et al. (2011a) presented results for a case study in the
United States, in an area between north-western Colorado and
north-eastern Utah. The proposed methodology integrates three
tools: (a) one to estimate the storage capacity of the potential
carbon dioxide reservoirs (named “CO2-PENS”), (b) another for the
calculation of carbon dioxide emissions originating from different
sources (called CLEAR), and (c) the SimCCS system that was already
explained above. Keating et al. (2011a) used the MILP model originally proposed by Middleton and Bielicki (2009b). Note that this
work considers the impact of the uncertainty in the CO2 storage
capacities of the reservoirs on the infrastructure of the supply chain
network. This uncertainty was analysed using the Monte Carlo
method with the “CO2-PENS” tool.
Middleton et al. (2012b) improved the MILP model presented by
Kuby et al. (2011b) and Middleton and Bielicki (2009b) in several
respects. This new improved version of the model considers that
investments in infrastructure are made over a planning horizon.
The improved model also considers the construction of several
wells (maximum capacity) for each potential reservoir for CO2
storage and the possibility of installing multiple CO2 capture units
for each source of CO2 emissions. The new model was tested for a
case requiring reduced CO2 emissions in an area of Texas, USA
containing nine sources of CO2 emissions and three possible reservoirs for storage. They used a planning horizon of 50 years. In the
same line, Morbee et al. (2012) proposed a MILP model to solve the
problem of planning a network of pipes with the objective to
minimise the cost of investment in upgraded pipelines to be
installed over a pre-set planning horizon.
Unlike the aforementioned approaches, the work by Van den
Broek et al. (2010) and Strachan et al. (2011) combined the use of
geographical information systems and energy planning models
based on linear programming to solve the CCS SC network problem.
The LP model does not optimise the combinatorial nature of
feasible solutions to the network infrastructure; instead, it uses
pre-established feasible network infrastructure solutions and assesses costs accordingly. Van den Broek et al. (2010) applied ArcGIS
and MARKAL (Market Allocation) to plan a network infrastructure
for a large scale CO2 capture and storage network for the
Netherlands, considering a period between 2010 and 2050. In the
same line, Strachan et al. (2011) described the use of the energy
planning models MARKAL and TIMES to design the CCS infrastructure network for the region of Utsira. In that case, the CO2
emissions sources are located in countries such as Germany,
Denmark, the Netherlands, Norway, and the UK.
In a different line, the models proposed by Klokk et al. (2010)
and Han and Lee (2011) maximise a profit function associated
with the CCS network project. Klokk et al. (2010) presented a MILP
model to maximise the net present value of the revenues generated
by the oil recovery and the amount of unpaid fines related to CO2.
The model considers the investment in infrastructure and operating costs over time to optimise the transport network. Han and
Lee (2011) proposed a MILP model to solve a CCS problem with
some interesting variations from the models presented thus far. In
this case, the variables of the model differentiate between industry
location and type of production at each plant. The model also
considers the location of different types of plants for CO2 capture
and ultimately describes the possibility of capturing the CO2 in
different physical states.
Kuby et al. (2011a) presented a MILP model that considers the
cost of CO2 emissions to industry in a tax or a cap-and-trade system.
This model extends the previous MILP model proposed by
Middleton and Bielicki (2009b), explicitly determining the optimal
amount of CO2 to be captured and simultaneously optimising the
various other components of the CCS network infrastructure.
Although several studies have recognised the importance of the
uncertainty in the storage capacity of the reservoirs and how this
capacity can affect the entire infrastructure required for CCS, few
studies have addressed this issue. The level of uncertainty changes
depending on the different components of the CCS supply chain
network. In accordance with Keating et al. (2011b) and Middleton
et al. (2012a), the technological alternatives for separating CO2 at
the plant level are well-defined. In contrast, the real sequestration
capacity of CO2 in saline aquifers (reservoirs) cannot be accurately
determined. Due to the geological heterogeneity that can affect the
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
porosity, permeability, thickness and extension of the aquifers, the
estimated capacity can be far from the real capacity (Buscheck et al.,
2011). In the same line, Deng et al. (2012) studied the effects of
reservoir heterogeneity on CO2 storage capacity, well injectivity
and potential leakage. Therefore, the design and deployment of CO2
supply chain network projects takes place under a high level of
uncertainty in the geological capacity for carbon dioxide storage. It
is important to understand how uncertainty in the capacity of CO2
sequestration can affect the infrastructure of the supply chain
network, its costs, and the use of sources, pipelines, and reservoirs.
More recently, Lee et al. (2014) proposed a mathematical programming model for solving the unified planning problem of CCS
deployment considering both grid implications and source-sink
matching. Mendelevitch (2014) proposed a market equilibrium
model to analyse the interactions of individual supply chain entities
(traders, carbon dioxide emitters, carbon dioxide transmission
network operators, and CO2-EOR and other storage operators) and
their impact on a future CCS infrastructure. Santibanez Gonzalez
(2014) proposed a deterministic MILP model to design a CCS supply chain network. Hasan et al. (2015) proposed a hierarchical and
multi-scale framework to design a CO2 capture and storage and a
capture and utilization supply chain networks with the objective of
minimize investment, operating and material costs. Sun and Chen
(2016) presented a deterministic MILP model for designing a carbon dioxide capture, utilization, and storage network.
Finally, Huang et al. (2013) presented a literature review on
technical developments and economic analysis of CCS using optimization models and algorithms. Recent works in this area that do
not consider the infrastructure (transportation) deployment in the
mathematical models are due to Abadie et al. (2014), Walsh et al.
(2014), among others. In a different stream of research, the problem of reducing CO2 emissions in supply chain has been addressed
by several authors (Eg, Benjaafar et al., 2013; Validi et al., 2015,
2014, 2012). Those studies do not address the problem of
designing a CO2 capture and storage network infrastructure.
In summary, the problem of designing a supply chain network
for CCS has been solved by different methodologies, and the
problem itself appears in different forms. Previous studies have
emphasised some aspects of the problem in isolation, whether to
simplify the model and solution method or to break it down and
solve it separately and then integrate the other factors in the
analysis. Mathematical models optimise the network infrastructure
considering the minimization of costs or maximization of profits
under static, dynamic and deterministic considerations. In addition,
there are a line of proposed methodologies that do not guarantee
the optimal solution of the network infrastructure problem and
consider uncertainty in the storage capacity of the reservoirs.
However, only some of these authors have addressed the problem
of comprehensively optimising the CO2 network infrastructure
design using MILP models and combining those decisions with
pricing policies. This article follows in the line of research established by Klokk et al. (2010) and Kuby et al. (2011a). We adopt a
different emphasis than previous studies, formulating the problem
as one of designing a supply chain network for CCS. To the best of
our knowledge, no previous optimisation models have integrated
storage capacity uncertainty, pricing policies, and the CCS infrastructure network. The combinatorial optimisation problem
considered here is complex to solve, and by association with other
problems it can be classified as an NP-Complete problem. In the
context of strategies for mitigating climate change, the effects of
economic incentive policies such as taxes on carbon dioxide
emissions to the atmosphere have not been adequately explored in
conjunction with a strategy for CO2 capture and storage. Our
contribution in this area is to propose a stochastic MILP model that
integrates uncertainty to solve the problem of pricing carbon
5
dioxide emissions while planning the investment required to
deploy and to operate a pipeline system for capturing and
sequestering the CO2. In addition it is considered pre-established
limitations on the number of plants that can be used to capture
carbon dioxide as well as the number of reservoirs that can be
opened.
3. The problem and mathematical formulation
We propose a supply chain framework that provides a holistic
understanding of the real problem. The proposed mathematical
model and solution of the real problem consider many factors
including the following: various geographically dispersed CO2
sources (emitters), several alternative paths (potential) for the
transport of the captured carbon dioxide from a source to a reservoir, various alternative types and sizes of pipes for the transport of
carbon dioxide, several alternative geographical distributions of
geological sites (reservoirs) for carbon dioxide sequestration, limitations on the number of carbon dioxide capture plants to install,
limitations on the number of reservoirs to activate, and uncertainty
about the real available carbon dioxide storage capacity of the
reservoirs. In addition, the proposed model considers different
pricing policies (CO2 tax) and their implications on the supply chain
network infrastructure.
The problem of setting prices on carbon dioxide emissions and
designing a supply chain network for the capture and sequestration
of CO2 can be defined as follows:
Sets and indices
Nsourc ¼ set of all sources of CO2 emissions, indexed by i;
Nsink ¼ set of all candidate locations (reservoirs) that can
sequester CO2, indexed by j;
N is the set of all candidate locations (emission sources,
reservoirs, intermediate points) that may be part of a path q.
Note that Nsourc and Nsink are subsets of N;
Q is the set of all possible paths connecting a source of CO2
emissions to a reservoir, where q ε Q;
E is the set of all links (edges) e ¼ (i, j) where i, j ε N;
R is the set of all alternative pipes r characterised by their
diameters (capacities);
Q is the set of all possible storage capacity scenarios, indexed
by q.
Parameters
fijr is the installation cost of the tube r between (i, j);
fio represents the fixed costs to install a CO2 capture technology at source i (wi);
fid indicates the fixed costs of opening a reservoir j (wj) for
CO2 sequestration;
cij captures the cost of CO2 transport between i and j;
coi is the cost of capturing CO2 from source i ε Nsourc;
cdj is the cost of operating and injecting CO2 into the reservoir j ε Nsink;
p is the price (tax) per tonne of CO2 emissions to the
atmosphere;
Ki represents the capture capacity of each source i ε Nsourc;
Kjq indicates the CO2 storage capacity of reservoir j ε Nsink for
scenario q;
Kmax
is the maximum flow capacity of tube r;
r
Kmin
is the minimum flow operating capacity of tube r;
r
tsour is the maximum number of CO2 capture plants to be
opened;
tsink is the maximum number of geological reservoirs to be
activated;
ri is the efficiency factor (between 0 and 1) of the CO2 capture
plant installed at source i.
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
6
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
Decision variables
wi, is 1 if a CO2 capture plant is opened in i ε Nsourc, and
0 otherwise;
wj is 1 if a CO2 storage reservoir is opened in j ε Nsink, and
0 otherwise;
yijr is 1 if a tube type (diameter) r is constructed between i
and j, and 0 otherwise;
xijq is the CO2 flow (Kt/year) between points i and j for scenario q.
follows:
Fixed costs
X X
ði;jÞ2E r2R
Prior to presenting the mathematical model of the problem, let
us observe some interesting characteristics regarding the deterministic solution for a given scenario q ε Q:
Eq
3.2. Objective function
Given the uncertainty in the storage capacity of each reservoir,
we establish a two-phase stochastic optimisation model to solve
the CCS-PSC problem. Variables wi, wj and yijr are assigned as first
stage variables, which means that decisions on the installation of
CO2 capture plants, activation of reservoirs, and selection and
construction of pipelines must be made prior to knowing the actual
random parameters. Variables xijq are assigned to second stage
variables. Random parameters are assumed to follow finite discrete
distributions.
The objective function for this problem can be written as
i2Nsour
X X
ði;jÞ2E r2R
fjd wj þ
j2Nsink
X
cijr xijq þ
X
xijq
X
coi xijq þ
ði;jÞ2E
ði;jÞ2E
sour
sink
i2N
ði;jÞ2E
For a path q starting at i and ending at j to be feasible, the
following must be true:
o at least one i ε Nsourc and at least one j ε Nsink;
o the pipes r installed on path q must have sufficient capacity
(Kmax
) to carry a CO2 flow xij and a minimum operating car
pacity of (Kmin
r );
o for all i ε Nsourc, the total outflow from i must be less than or
equal to Ki;
o for all j ε N sink, the total inflow reaching j must be less than or
equal to Kjq;.
o the number of sources i ε Nsourc that have CO2 capture plants
installed must be less than tsourc;
o the number of reservoirs j ε N sink activated must be less than
tsink.
For a predetermined price p on CO2 emissions, the goal of our
CCS supply chain network infrastructure design problem (CCSPSC) is to find a set of feasible paths q and the amount of CO2
emissions that must be captured, transported, and sequestered
to minimise fixed and variable costs while keeping the number
of CO2 capture plants installed and the number of reservoirs
opened less than tsourc and tsink, respectively.
To phrase the problem another way, considering the overall goal
of reducing CO2 emissions to the atmosphere and mitigating
climate change, we want to find a price p and an infrastructure
supply chain network of minimum cost that is capable of
capturing, transporting, and storing CO2. This supply chain must
satisfy the requirements previously mentioned and that we
formalise hereafter.
In the CCS-PSC problem with uncertainty in the storage capacity
of reservoirs, we want to analyse how setting a price p on CO2
emissions affects the infrastructure of the supply chain network
as well as the impact of limiting the number of plants to capture
CO2 (t source) and the number of reservoirs that are activated
(tsink).
X
fio wi þ
Capture, transport, and storage costs
"
3.1. Problem characteristics
X
fijr yijr þ
j2N
cdj xijq þ
X
p Ki
i2Nsour
#
(2)
where Eq[.] denotes the expected value function with respect to
scenario q. The concise form of the objective function can be written
as
X X
X
fijr yijr þ
ði;jÞ2E r2R
X
fio wi þ
i2Nsour
fjd wj þ Eq ½
(2a)
j2Nsink
We model a finite number of discrete scenarios, and we define lq
as the probability of scenario q. The expected value function Eq[.]
can be written as
X X X
ði;jÞ2E r2R q2Q
lq cijr xijq þ
X
X
ði;jÞ2E q2Q
lq coi xijq þ
i2Nsour
X
q2Q
X
lq cdj xijq þ
0
p@Ki i2N sour
X
ði;jÞ2E
j2N sink
X X
ði;jÞ2E q2Q
1
lq xijq A
(2b)
3.3. The SMILP model
Mathematically, we can formulate the CCS-PSC problem as a
Stochastic Mixed Integer Linear Programming (SMILP) model as
follows:
Minimise
Fixed costs (1) þ capture, transport and storage costs (2a).
Subject to
xijq X
Krmax yijr
cði; jÞ2E; q2Q
(3)
Krmin yijr
cði; jÞ2E; q2Q
(4)
r2R
xijq X
r2R
P
xijq Ki ri wi
c i2 N sour ;
xijq Kjq wj
c j2 N sink ; q2Q
ði;jÞ2E
P
ði;jÞ2E
q2Q
(5)
(6)
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
X
yijr 1 c ði; jÞ2 E
7
(7)
r2R
X
ði;jÞ2E
X
xijq ¼
X
ðj;iÞ2E
i
h
xjiq c j2 N N sour ∪N sink ; q2Q
(8)
CO2 emission sources
Geologic reservoirs
wi tsource
(9)
TransiƟon points
i2Nsour
X
PotenƟal route
wj tsink
(10)
j2Nsink
Fig. 3. Example of a potential network for carbon dioxide capture and sequestration.
xijq 0
c ði; jÞ2 E; q2Q
(11)
yijr 2f0; 1g
c ði; jÞ2 E; r 2R
(12)
wi 2f0; 1g
c i2 Nsour ∪ N sink
(13)
The objective function minimises investment and construction
costs (1) and the costs of capture, transport, and storage of CO2 plus
the cost of emitting CO2 to the atmosphere (2a). Constraints (3) and
(4) guarantee that the CO2 flow between the two points i and j is
only possible if a tube r with sufficient capacity is installed and
operated. Constraint (5) ensures that total emissions captured from
each source are smaller than the total generation capacity of these
CO2 sources. Constraint (6) ensures that the total amount of CO2
stored in the reservoir does not exceed its capacity. Constraint (7)
requires placement of a tube r for almost every connection.
Constraint (8) reflects the balance of flow that must exist in every
place other than sources and reservoirs. Constraint (9) ensures that
the maximum number of CO2 capture plants is not exceeded.
Constraint (10) ensures that the maximum number of reservoirs for
the storage of CO2 is not exceeded. The remaining restrictions,
(11)e(13), are the standard binary and non-negative variables.
3.4. Model observations
As observed by Birge and Louveaux (1997) for a related problem,
the solution obtained for SMILP is not optimal for any individual
scenarios, but it reflects a weighted balance for all the possible
scenarios.
Note that in the case that the original network includes potential
connections between sources of carbon dioxide emissions, this
formulation requires adding an artificial source connected with
each other source (s) at a cost of zero. The same observation
applies to the reservoirs.
There is likely a price p for which the volume of carbon dioxide
captured is zero. This result indicates that at this price it is
cheaper for companies to pay the price (cost, tax) of carbon
dioxide emissions to the atmosphere than to invest in a network
infrastructure to capture, transport, and sequester CO2. In our
case, we analyse various scenarios of uncertainty, and for a
range of prices on CO2 emissions we observe the impact on the
network in terms of infrastructure and costs.
Fig. 3 shows an initial network with various connection options
between sources of carbon dioxide emissions (blue circles),
transition points (yellow squares), and potential reservoirs
(green triangles). The different symbol sizes reflect the volume
of carbon dioxide emission, transmission, or storage, respectively. The transition points are potential sites that may be part
of the path q that connects sources to reservoirs. Based on Fig. 3
(initial network), Fig. 4 presents a feasible solution to the
problem of designing a supply chain network for the capture
and storage of carbon dioxide. Note that the blue lines have
different thicknesses and different points are joined to form a
path from the source to the reservoirs for the storage of carbon
dioxide. The thicknesses illustrate that the tubes increase in
diameter to carry more carbon dioxide.
4. A case study: CO2 capture and storage in Southeastern
Brazil
This section presents the implementation of the proposed SMILP
model for the South-eastern region of Brazil. Our goal is to analyse
the development of the CCS supply chain network in the presence
of uncertainty in the storage capacity of reservoirs and with
different prices of carbon dioxide emissions for the cement industry. A study by the Intergovernmental Panel on Climate Change
in 2005 (IPCC, 2005) pointed out that the main sources of carbon
dioxide emissions worldwide are (in tonnes/year): energy generation plants (10,539 million), cement factories (932 million), and
refinery plants (798 million). The cement manufacturing industry is
a significant industrial CO2 emitter (Benhelal et al., 2012;
Vatopoulos and Tzimas, 2012). According to Gao et al. (2015) this
sector was responsible for about 7% of the total anthropogenic
carbon dioxide emissions worldwide in 2006. For example, the US
cement industry had the third rank among pollutant industries
with emissions of 29.4 teragrams of carbon dioxide equivalent in
2009.
4.1. Data and scenario settings
According to data published by the National Association of the
Cement Industry in Brazil (SNIC), the cement manufacturing industry of Brazil is made up of 14 business groups and a total of 79
plants located in different states. According to the SNIC, a total of
66.80 million tonnes (Mt) of cement were sold between June 2011
CO2 emission sources
AcƟvated geologic
reservoirs
TransiƟon points
PotenƟal connecƟons
AcƟvated connecƟons
Fig. 4. A feasible supply chain network solution for the network in Fig. 3.
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
8
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
and May 2012. In our case study, we assume factories with an
average annual production capacity between 800 thousand tonnes
(t) and 2.5 million tonnes. These data were also based on information published on the SNIC web site (SNIC, 2012).
This article validates the proposed model by considering the
~o Paulo,
states of Minas Gerais and the South-western portion of Sa
~o Francisco Basin and the eastern part of the Parana
including the Sa
Basin. Our purpose is to analyse how the uncertainty in the storage
capacity of reservoirs affects the pricing (p) of CO2 emissions under
different scenarios. In addition, to determine which values of p
make it economically favourable for companies to pay taxes and/or
invest in deployment and management of a supply chain infrastructure network for capturing, transporting, and sequestering
CO2 generated by cement factories located in the study area. A
pipeline network is the most feasible type of infrastructure for
transporting large amounts of carbon dioxide. Note that this is a
sample case study with the objective of validating the capability of
the proposed model rather than of supporting the enactment of
real public policies on the pricing of CO2 emissions for this sector.
Ten cement manufacturing plants emit carbon dioxide in the
case study. The production capacities of the specific cement factories considered in our study were randomly assigned using data
on the annual average production capacity (between 800 thousand
tonnes and 2.50 million tonnes). Other data used in the case study
can be obtained from the author, including the following: data on
energy consumption and carbon dioxide emissions per tonne of
cement produced, formulas for estimating the capacity of the power plant in each cement plant, investment in facilities, and investment in the acquisition and construction of pipelines. We
consider an investment of US$ 2310/kW (75% of the figure published in MIT CCSTP, 2007). Table 1 presents data on investments
and the volume of carbon dioxide emissions of each plant. For all
Table 1
Cement Factories: CO2 emissions and investment in CO2 capture plants.
#
Cement Factory
Emissions KtCO2/year
Investment MUS$
1
2
3
4
5
6
7
8
9
10
Arcos
Barroso
Carandaí
Ijaci
Itaú de Minas
Matozinhos
Montes Claros
Pedro Leopoldo
Santa Luzia
Vespasiano
828.1
1236.9
672
1415.4
1646.4
1243.2
948.5
1652.7
1480.5
959
47443.2
70864.1
38500.0
81090.6
94325.0
71225.0
54341.1
94685.9
84820.3
54942.7
Table 2
Potential reservoirs: capacity, depth, and type.
Reservoir
Capacity KtCO2/year
Depth (m)
Type
~o Francisco-1
Sa
~o Francisco-2
Sa
-1
Parana
10,000
16,000
8000
800
800
800
Aquifer
Aquifer
Aquifer
the capture plants, we assume an efficiency factor (ri) of 1, despite
of the fact that the literature generally recommends a value of 0.9
depending on the type of plant.
Three geological reservoirs were identified as alternatives for
the sequestration of carbon dioxide in saline aquifers, two located
~o Francisco Basin and one in the Parana
Basin - Bauru. The
in the Sa
total storage capacity of carbon dioxide in the basins of Brazil is
~o Francisco holds 10% of
2035 Gt (Rockett et al., 2011). The Basin of Sa
the total capacity for carbon dioxide storage in saline aquifers,
Basin can hold more than 400 Gt. This study
while the Parana
assumed a much smaller capacity, less than 1% of the total storage
capacity distributed among the potential reservoirs. These data are
shown in Table 2. The characteristics of the pipes for transporting
carbon dioxide and other data are shown in Table 3. Note that the
minimum flow capacity of a pipe with an 8 inch diameter was
artificially reduced to 400 Ktonnes (Kt) per year to avoid a situation
in which some plants could not transmit the captured carbon dioxide. The costs of investment, operation, and transport of carbon
dioxide were obtained from various sources such as (Energy, 2010)
and MIT CCSTP (2007). This study used a planning horizon of 25
years with costs updated at an interest rate of 10%.
Fig. 5 shows the study area, identifying the different carbon
dioxide emission sources and potential reservoirs (blue points).
Several cement factories are located within 40 km of one another,
making it difficult to distinguish between them because of the scale
of the map. For this reason, Fig. 6 illustrates the potential network
connections connecting different sources, transition points, and
potential reservoirs, but it is not drawn to scale. In this type of
infrastructure project, it is normal to define a cost function that
considers (and weights properly) the impacts of different factors
(e.g., the type of terrain, the right of way) in the calculation of the
cost of building pipelines to transport carbon dioxide. We omit
these details to simplify the explanation of the case study, although
the proposed model may consider them.
To make the case study easy to understand and to visualise the
potential applications of the proposed model, some data and
technical details are simplified. For example, the pressure of carbon
dioxide is assumed to be suitable for transport (10 MPa), it is
assumed that no pressure drop occurs during transport, the storage
capacity and amount injected in the reservoir as determined by the
geological conditions (e.g., porosity and thickness) are integrated
into the model as part of the uncertainty, and the arrival pressure of
carbon dioxide in the reservoir is assumed to be suitable for injection, avoiding the need to re-pressurise it in another facility. All
these technological issues were resolved using similar problems in
the gas and oil industry as references and may be incorporated into
the model (e.g., Middleton and Bielicki, 2009a, 2009b). For
example, the drop in the pressure of carbon dioxide during transport can be prevented by installing booster stations periodically
throughout the network. Each of the item costs that exist in the
sources and reservoirs were expressed in a single fixed value and
another variable value. Fixed costs include the costs of investment
in a carbon dioxide capture plant, compression, pumping, and other
facilities. In a similar manner, fixed costs of reservoirs include in a
Table 3
Types of tubes, capacities, and costs used in the case study.
Diameter (in.)
Capacity (min) KtCO2/year
Capacity (max.) KtCO2/year
Cost MUS$/km
8
12
16
20
24
30
1000
1600
3200
6400
9600
16,000
2000
4000
8000
12,000
20,000
36,000
400.0
600.0
800.0
1000.0
1200.0
1500.0
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
9
Fig. 5. Area of study considering the locations of cement factories and reservoirs (built using www.googlemaps.com).
single value the costs of screening, evaluation and drilling activities,
construction, and re-pressurising and pumping facilities, among
other costs. The model allows these costs to be divided and analysed in detail if necessary.
5. Results and analysis of the case study
The proposed model was programmed in GAMS and CPLEX was
used (parameters and default setting) as the optimiser. All results
were obtained in less than 3 s using a PC i5 with 2.3 GHz and 4 Gb
RAM.
To illustrate the result of the model, Table 4 presents three
scenarios (A, B, and C) for evaluating the uncertainty of the storage
capacity of the reservoirs. Scenario B is the base case: the storage
~o Francisco
capacity of the two potential reservoirs located in Sa
Basin is equal to 0.2% of the total potential storage capacity (uniformly distributed over 25 years); the storage capacity of the po Basin is 0.1% of the total
tential reservoir located in the Parana
potential storage capacity because this Basin is the largest Basin in
Brazil, crossing several states. Scenarios A and C were obtained by
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
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E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
São Francisco 1 (11)
7
São Francisco 2 (12)
6
CO2 emission sources
8
1
Geologic reservoirs
PotenƟal connecƟons
10
9
5
3
4
Paraná 1 (13)
2
Fig. 6. Basic network showing the locations of cement plants, potential geological reservoirs, and potential connections between points.
Table 4
Scenarios A, B, and C: reservoir storage capacities and probabilities.
Scenario
~o Francisco 1 (11)
Sa
~o Francisco 2 (12)
Sa
1 (13)
Parana
Probability
A
B
C
8000
8000
9600
5000
10000
12000
10000
16000
19200
0.25
0.50
0.25
generating a random number between [0,1] higher or lower than
the base scenario. We evaluated two cases (I and II) for different
prices (p) on carbon dioxide emissions not captured per year in US$
per tonne of carbon dioxide (US$/tCO2) ranging between 30.0 and
60.0 (US$/tCO2).
a. Case I: The maximum numbers of carbon dioxide capture plants
that could be installed and reservoirs that could be activated
was limited to ten and three, respectively. These are not active
constraints for this case study because those numbers correspond to the maximum number of potential sources and
candidate reservoirs.
b. Case II: The maximum numbers of capture plants and reservoirs
that could be activated were limited to ten and one, respectively.
In this case the maximum on the number of reservoirs to be
opened is reached. For these different pricing policies, the model
tries to find the best reservoir to open in terms of the fixed and
variable costs.
Table 5 presents, for the price range of 30e60 US$/tCO2, the
carbon dioxide captured in each scenario, the objective solution
value (total cost) and the total cost of emitting carbon dioxide to the
atmosphere. To illustrate the results, prices p are reported in the
first column and the following columns show the amount of carbon
dioxide captured in Kt/year for each scenario (A, B, and C), the total
cost in thousands of US$ (MUS$) and the total cost paid (MUS$) per
amount of carbon dioxide emitted to the atmosphere (not
captured). For example, for a price of 35US$/tCO2, we observed that
a total of 3.061 million tonnes of carbon dioxide were stored per
year, that the project implementation and operation of the CCS
Table 5
Case I: Volume of carbon dioxide captured, total solution cost (objective function
value), and cost of emissions not captured for different values of p (CO2 tax) and
different scenarios.
Price
US$/tCO2
CO2 captured
Kt/year
A
B
C
30
33
35
40
50
60
0
0
3061.8
6906.2
8889.9
11075.3
0
0
3061.8
6906.2
9734.3
11410.7
0
0
3061.8
6906.2
9734.3
11410.7
Total solution
cost, MUS$
Cost of CO2
Emissions, MUS$
3262329.0
3624810.0
3848358.8
4079742.0
4396979.1
4547619.5
3262329.0
3624810.0
2886700.0
1863500.0
1151800.0
408160.0
supply chain would cost a total of MUS$ 3,848,358.8, and that the
cost of carbon dioxide emissions not captured was MUS$
2,886,700.0. Observe that for a price (tax) less than 33 US$/tCO2, it
is economically favourable for companies to pay for all the carbon
dioxide emissions to the atmosphere rather than capturing them.
Thus, from the point of view of sustainability, this price will not
contribute to reducing the concentration of carbon dioxide in the
atmosphere.
As expected, Fig. 7 illustrates that when the price (tax) on carbon
dioxide emissions to the atmosphere is increased, companies
implement a CCS supply chain network, thereby reducing the cost
paid for carbon dioxide emissions not captured. For example, for a
price p ¼ 60 US$/tCO2, the cost paid for emissions not captured is
MUS$ 408,160.0, much smaller than the amount paid when p ¼ 35
US$/tCO2. In Fig. 8 we observe that the higher the price p, the
greater the amount of carbon dioxide captured per year. The
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
11
5000000.0
4500000.0
4000000.0
3500000.0
Cost MUS$
3000000.0
2500000.0
2000000.0
1500000.0
1000000.0
500000.0
Total Cost MUS$
Total cost to emit CO2
0.0
30
33
35
40
price p US$/tCO
50
60
Fig. 7. Case I: price p of CO2 emissions vs. total cost and the cost to emit carbon dioxide.
12000
10000
CO2 captured, Kt/ year
8000
6000
4000
2000
Scenario A - CO2 captured Kt/y
Scenario B - CO2 captured Kt/y
Scenario C - CO2 captured Kt/y
0
30
33
35
40
price p US$/tCO2
50
60
Fig. 8. Price p on carbon dioxide emissions vs. carbon dioxide captured for different scenarios.
amount of carbon dioxide captured is zero for rates between 30 and
33 US$/tonne. Note that in terms of carbon dioxide captured, three
scenarios present the same results except for p values of 50 and 60
US$/tCO2, while a minor amount is captured in scenario A.
For different values of p, Tables 6e8 show the evolution of the
infrastructure of the CCS supply chain network in detail. Note that
as the price p per tonne of carbon dioxide not captured is increased,
the infrastructure of the supply chain network becomes more
complex. For example, for p ¼ 35 US$/tCO2, Fig. 9 shows that the
supply chain network is formed by two carbon dioxide capture
1 (13) reservoir
plants installed in cement factories 4 and 5, Parana
is activated and the plants are linked directly to this reservoir using
8 inch diameter pipes (Table 5). However, when p ¼ 60 US$/tCO2
(Fig. 10), the network grows to 9 carbon dioxide capture plants
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12
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
Table 6
Case I (p ¼ 35 US$/KtCO2): capture plants and pipes installed, carbon dioxide captured and flow for different scenarios.
Capture plant
wi
4
5
Connection
yijr
4e13
5e13
Tube
Diameter (in.)
8
8
TOTAL
CO2 captured, Kt/year
Plant wi
Flow, Kt CO2/year, xijs
A
B
C
A
B
C
1415.4
1646.4
1415.4
1646.4
1415.4
1646.4
1415.4
1646.4
1415.4
1646.4
1415.4
1646.4
3061.8
3061.8
3061.8
Table 7
Case I (p ¼ 50 US$/KtCO2): capture plants and pipes installed, carbon dioxide captured and flow for different scenarios.
Capture plant
wi
1
4
5
6
7
8
9
10
Connection
yijr
1e13
4e13
5e13
6e12
7e12
8e12
9e10
10e12
Tube
Diameter (in.)
8
8
8
8
8
8
8
8
TOTAL
CO2 captured, Kt/year
Plant wi
Flow, Kt CO2/year, xijs
A
B
C
A
B
C
828.1
1415.4
1646.4
1039.8
948.5
1652.7
400.0
959.0
828.1
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
959.0
828.1
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
959.0
828.1
1415.4
1646.4
1039.8
948.5
1652.7
400.0
1359.0
828.1
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
2000.0
828.1
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
2000.0
8889.8
9734.3
9734.3
Table 8
Case I (p ¼ 60 US$/KtCO2): capture plants and pipes installed, carbon dioxide captured and flow for different scenarios.
Capture plant
wi
Connection
yijr
Tube
Diameter (in.)
1
2
4
5
6
7
8
9
10
1e13
2e4
4e13
5e13
6e12
7e11
8e12
9e10
10e12
8
8
12
8
8
8
8
8
12
TOTAL
CO2 captured, Kt/year
Plant wi
Flow, Kt CO2/year, xijs
A
B
C
A
B
C
828.1
1236.9
1415.4
1646.4
1243.2
948.5
1652.7
1145.1
959.0
828.1
1236.9
1415.4
1646.4
1243.2
948.5
1652.7
1480.5
959.0
828.1
1236.9
1415.4
1646.4
1243.2
948.5
1652.7
1480.5
959.0
828.1
1236.9
2652.3
1646.4
1243.2
948.5
1652.7
1145.1
2104.1
828.1
1236.9
2652.3
1646.4
1243.2
948.5
1652.7
1480.5
2439.5
828.1
1236.9
2652.3
1646.4
1243.2
948.5
1652.7
1480.5
2439.5
11075.3
11410.7
11410.7
installed (only the third factory has not installed a plant and all the
carbon dioxide emissions of this factory are released directly into
~o Francisco 1 (11)
the atmosphere), three reservoirs are activated, Sa
1 (13), and 3 subnets are established to
and 2 (12) and Parana
capture carbon dioxide from 9 (out of 10) cement factories. Note
that in this scenario it was necessary to use 12 inch diameter pipes
to link 4e13 and 10e12 (Table 8). Observe also that the amount of
carbon dioxide captured from each cement factory is the same for
all the scenarios, except for scenario A and for the carbon dioxide
capture plants installed in cement factories 9 and 10 (Fig. 10 and
Table 8). We do not provide a figure for p ¼ 50 US$/tCO2. However,
observe from Table 7 that the infrastructure of the SC network is
quite similar among all three scenarios. The carbon dioxide
captured from cement factories 6 and 9 is only different for scenario
A. Table 7 illustrates that carbon dioxide capture plants for factories
2 and 3 are not installed. Table 8 shows that a carbon dioxide
capture plant for factory 3 is not installed.
Fig. 11 illustrates for p ¼ 60 US$/tCO2 and scenarios A and B the
contributions of each factory to the total volume of carbon dioxide
captured, transported, and stored. For scenario A, note that plant 4
(green) contributes 13% of the total amount of carbon dioxide
captured, while plants 1 and 5 contribute 7% and 15%, respectively.
For scenario B, the contributions of plants 1, 4 and 5 are 1%, 12% and
15%, respectively. Fig. 12 illustrates for scenario A and the same
value of p ¼ 60 US$/tCO2 the volume of carbon dioxide captured
(red colour) and the total amount of carbon dioxide emitted by each
factory (blue colour). Note that for all cement factories with carbon
dioxide capture plants installed, the entire amount of carbon dioxide is captured, transported, and stored, except for factory 9,
where only 70% of carbon dioxide was captured.
Table 9 presents the results for Case II and different values of p,
including the amount of carbon dioxide captured in each scenario,
the value of the objective function, and the cost of emitting carbon
dioxide to the atmosphere. For the price p ¼ 60 US$/tCO2, the total
cost is MUS$ 5,323,485.7, representing an increase of 17% compared
with the cost obtained (MUS$ 4,547,619.5) for Case I with the same
value of p. The cost of carbon dioxide emissions not captured
increased significantly with respect to Case I. This cost is MUS$
1,907,300.0, representing a 360% increase over the amount paid in
Case I (MUS$ 408,160.0). This increase in cost is explained because
the total amount of carbon dioxide captured per year decreased by
an average of 30% with respect to Case I. For p ¼ 50 US$/tCO2, we
observe that the objective function value, the cost of carbon dioxide
emissions not captured, and the average amount of carbon dioxide
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
13
São Francisco 1 (11)
7
São Francisco 2 (12)
CO2 emission sources
acƟvated
CO2 emission sources
Not-acƟvated
6
Geologic reservoirs
acƟvated
8
1
Geologic reservoirs
Not-acƟvated
AcƟvated connecƟons
10
9
5
3
4
Paraná 1 (13)
2
Fig. 9. The CCS supply chain network for Case I (p ¼ 35 US$/tCO2), scenarios A, B, and C.
3000.0
2500.0
CO2
2000.0
CO2 flow from plan i
scenario A
1500.0
CO2 flow from plant i
scenario B
1000.0
CO2 flow from plant i
scenario C
500.0
0.0
1
2
4
5
6
7
8
9 10
Plants
Fig. 10. CCS supply chain network for Case I (p ¼ 60 US$/tCO2) and carbon dioxide flow for each scenario.
CO2 captured by plant - Scenario B
CO2 captured by plant - Scenario A
9
10%
10
9%
1
7%
1
7%
2
11%
4
13%
8
15%
9
13%
6
11%
5
15%
4
12%
8
15%
7
8%
7
9%
2
11%
10
8%
6
11%
5
15%
Fig. 11. Case I (p ¼ 60 US$/tCO2): contribution of each plant to total carbon dioxide captured for scenarios A and B.
captured are MUS$ 4,877,620.4, MUS$ 2,902,200.0, and 3061.8 Kt
CO2/year, respectively. Comparing these values with those obtained
in Case I, less CO2 is captured and the cost of the solution is higher.
Finally, note that the same amount of carbon dioxide is captured for
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
14
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
Total CO2 emissions
by plant
Total CO2 captured
by plant - Scenario A
For Cases I and II, Fig. 13 shows the cost of the solution (objective
function value) and the cost of emitting carbon dioxide for different
values of p. We conclude that limiting the number of reservoirs that
can be activated could increase the implementation costs of the CCS
supply chain network and also increase costs for the carbon dioxide
emissions not captured.
Tables 10 and 11 illustrate the infrastructure of the CCS supply
chain network for p ¼ 50 US$/tCO2 and p ¼ 60 US$/tCO2. Observe
that no carbon dioxide is captured for plant 10 and scenario A. In
~o Francisco 2 reservoir has an
this case, the storage capacity of the Sa
impact, and all carbon dioxide emissions generated by this plant go
into the atmosphere.
6. Conclusions and future work
Fig. 12. eCase I (p ¼ 60 US$/tCO2): carbon dioxide emissions and capture by plant for
scenario A.
p ¼ 35 US$/tCO2 and p ¼ 40 US$/tCO2 because the infrastructure of
the supply chain network is the same. The difference in the
amounts paid for carbon dioxide emissions not captured results
from the difference between the values of p.
Table 9
Case II: Amounts of carbon dioxide captured, total solution costs, and costs of
emissions not captured for different values of p (CO2 tax) and different scenarios.
Price
US$/tCO2
CO2 captured, Kt/year
A
B
C
35
40
50
60
3061.8
3061.8
5000.0
5000.0
3061.8
3061.8
5844.4
9734.3
3061.8
3061.8
5844.4
9734.4
Total Cost
MUS$
Cost CO2
MUS$
3848358.8
4209194.8
4877620.4
5323485.7
2886700
3247500
2902200
1907300
This paper studied and modelled the interactions between two
strategies proposed by the scientific community to reduce the CO2
emissions to the atmosphere and to mitigate the effects of climate
change: the establishment of prices on carbon dioxide emissions to
the atmosphere and carbon dioxide capture and storage network
infrastructure. Uncertainty in the storage capacities of reservoirs for
carbon dioxide sequestration has been indicated as an important
issue in the deployment and cost of the CCS infrastructure network.
To study and analyse the relationships between those mitigation
strategies and the uncertainty of the storage capacity of reservoirs,
we propose a stochastic optimisation model based on a two-stage
Stochastic MILP. This model considers both technical and economic factors to simultaneously address the effects of carbon dioxide pricing and the development of a network for capturing,
transporting, and sequestering carbon dioxide in geological reservoirs. The proposed model will help to understand and assess how
uncertainty in the storage capacity of carbon dioxide can impact the
costs, the use of sources, pipelines, and reservoirs in the network
infrastructure of the supply chain. The optimisation problem is
6000000.00
5000000.00
Cost MUS$
4000000.00
3000000.00
2000000.00
1000000.00
Total Cost MUS$ Case I
Total cost to emit CO2 Case I
Total Cost MUS$ Case II
Total cost to emit CO2 Case II
0.00
30
33
35
40
price p US$/tCO2
50
60
Fig. 13. Cases I and II: price p of carbon dioxide emissions vs. total cost and cost to emit carbon dioxide.
Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181
E.D.R. Santibanez-Gonzalez / Journal of Cleaner Production xxx (2017) 1e16
15
Table 10
Case II (p ¼ 50 US$/KtCO2): capture plants and pipe installed, carbon dioxide captured, and flow.
Capture plant
wi
6
7
8
9
10
Connection
yijr
6e12
7e12
8e12
9e10
10e12
Tube
Diameter (in.)
8
8
8
8
8
TOTAL
CO2 captured, Kt/year
Plant wi
Flow, Kt CO2/year xijs
A
B
C
A
B
C
1039.8
948.5
1652.7
400.0
959.0
1243.2
948.5
1652.7
1041.0
959.0
1243.2
948.5
1652.7
1041.0
959.0
1039.8
948.5
1652.7
400.0
1359.0
1243.2
948.5
1652.7
1041.0
2000.0
1243.2
948.5
1652.7
1041.0
2000.0
5000.0
5844.4
5844.4
Table 11
Case II (p ¼ 60 US$/KtCO2): capture plants and pipe installed, carbon dioxide captured, and flow.
Capture plant
wi
Connection
yijr
Tube
Diameter (in.)
CO2 captured, Kt/year
Plant wi
A
B
C
A
B
C
1
4
5
6
7
8
9
10
1e12
4e1
5e1
6e12
7e12
8e12
9e10
10e12
12
8
8
8
8
8
8
12
800.0
400.0
400.0
1243.2
948.5
808.3
400.0
0.0
828.1
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
959.0
828.1
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
959.0
1600.0
400.0
400.0
1243.2
948.5
808.3
400.0
400.0
3889.9
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
2000.0
3889.9
1415.4
1646.4
1243.2
948.5
1652.7
1041.0
2000.0
5000.0
9734.3
9734.4
TOTAL
considered NP-Complete, meaning that it is theoretically difficult to
solve. To validate the potential application of the proposed methodology, the cement industry in a large area of Brazil is used as a
case study. In particular, we analyse how pricing policies on carbon
dioxide emissions affect the costs and the deployment of a CCS
supply chain network. This SC network comprises the emissions of
~o
cement factories located between the northwest of the state of Sa
Paulo and the state of Minas Gerais, including potential geological
reservoirs (saline aquifers) located in the eastern part of the Parana
~o Francisco. We present results for different
Basin and the Basin of Sa
prices on yearly emissions not captured ranging between 30 and 60
USD$/tCO2. We consider three scenarios of storage uncertainty and
propose two different analyses for each of the prices in a pre-set
range. As might be expected, we found that an increase in the
price of carbon dioxide not captured provides factories with a
motivation to develop a CCS network infrastructure, increasing the
complexity of the network infrastructure of the supply chain,
reducing carbon dioxide emissions to the atmosphere, and also
reducing the costs of these emissions. This fact can be observed for
p ¼ 60 USD$/tCO2, the highest price, captured plants are installed in
almost all the cement factories except the factory 3, all three reservoirs are opened and two tube diameters has to be installed to
support the CO2 capture and storage supply chain network. On the
other hand, for p ¼ 35 US$/tCO2, the supply chain network is
formed by two carbon dioxide capture plants installed in cement
factories 4 and 5, only one reservoir is activated and the plants are
linked directly to this reservoir using 8 inch diameter pipes. We also
observe that at the same price of carbon dioxide not captured,
limiting the number of reservoirs that can be opened may increase
the amount of carbon dioxide in the atmosphere and significantly
increase the costs paid by companies. For instance, for price p ¼ 60
US$/tCO2, when it is limited the number of reservoirs to open, the
total cost is MUS$ 5,323,485.7, representing an increase of 17%
compared with the cost of MUS$ 4,547,619.5 obtained without
limiting the number of reservoirs that can be open. In addition, the
cost of carbon dioxide emissions not captured increases 360% over
Flow, Kt CO2/year xijs
the amount paid when the number of reservoirs to open is not
limited. Plants for carbon dioxide capture and pipeline construction
costs represent important portions of the total cost of implementing a supply chain network for CCS. Future research should
evaluate different cost structures for these items and how these
topologies are impacted by the prices of carbon dioxide emissions
to the atmosphere and the uncertainty in the storage capacity of
reservoirs. Further research should explore solution methods to
solve this type of stochastic MILP problems. In addition, extensions
to this model should consider that investments and uncertainty in
the storage capacity of reservoirs evolve over a planning horizon.
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Please cite this article in press as: Santibanez-Gonzalez, E.D.R., A modelling approach that combines pricing policies with a carbon capture and
storage supply chain network, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.03.181