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CHEMICAL ENGINEERING TRANSACTIONS

VOL. 39, 2014

A publication of

The Italian Association

of Chemical Engineering

www.aidic.it/cet
Guest Editors: Petar Sabev Varbanov, Jiří Jaromír Klemeš, Peng Yen Liew, Jun Yow Yong

ISBN 978-88-95608-30-3; ISSN 2283-9216 DOI: 10.3303/CET1439250

Please cite this article as: Francisco F.D.S., Pessoa F.L.P., Queiroz E.M., 2014, Carbon sources diagram - a tool for

carbon-constrained energy sector planning, Chemical Engineering Transactions, 39, 1495-1500 DOI:10.3303/CET1439250

1495

Carbon Sources Diagram - A Tool for Carbon-Constrained

Energy Sector Planning

Flavio da S. Francisco, Fernando L. P. Pessoa, Eduardo M. Queiroz*

Universidade Federal do Rio de Janeiro, Escola de Química, Departamento de Engenharia Química, Av. Athos da

Silveira Ramos, 149, CT, Bl. E, Cidade Universitária, 21941-909, Rio de Janeiro, Brasil

mach@eq.ufrj.br

Climate change is increasing as an effect of human activities around the world. The reduction of CO2

emissions by human activities would be the most important measure to reduce this negative effect.

Recently, many countries around the world have committed to reduce his CO2 emissions over time. In this

context, the world has been struggling to balance the growth in energy requirement and environment

conservation for a sustainable future, mainly due the adverse environmental, social and economic impacts

of global warming that are associated with greenhouse gas emissions. In the last decade, some

methodologies based on Pinch Analysis (Linnhoff et al., 1982) were developed as a tool for carbon

emission reductions and planning. Thus, the concepts of Pinch Analysis were applied to solve carbon

transfer, maximum carbon recovery, minimum carbon targets and the design of carbon exchange

networks. Focusing in planning for the power generation sector, a new methodology is presented based in

the Water Sources Diagram - WSD (Gomes et al., 2013). This new methodology is called Carbon Sources

Diagram - CSD. In this work, the Carbon Sources Diagram is used to locate the rigorous targets for both

low and zero-carbon energy sources for carbon-constrained energy planning. The results using the CSD

are similar to the ones obtained in the literature with other procedures for the same problems. However,

the Carbon Sources Diagram method is easier to be applied and presents a smaller number of steps when

compared with the methodologies employed in these works, graphical and algebraic, respectively.

1. Introduction

Recently it has been observed a progressively increase on global energy demand (AEO, 2012) driven by

industrial growth which is connected with the substantial economic growth. The industrial activities are

seen to be a major contributor to global warming, because of the high level of greenhouse gases

emissions, such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (NOx). The industrial growth

substantially increases the total demand for energy in general and electricity, in particular. As is known, the

major source for electricity generation is carbon intensive fossil fuels (coal, oil and natural gas), which are

the main contributors of carbon dioxide (CO2) emissions. In this context, it has been increased research

activities on fuel substitution, technology substitution, efficiency improvements and increased use of low

carbon (e.g., natural gas) or renewable energy (e.g., wind, solar, biomass, hydro, etc…) for the generation

of cleaner electricity (Varun et al., 2009).

Unfortunately, even with the broad public awareness of climate changes, fossil energy still dominates the

power generation sector due to three reasons: 1) Coal is prevalent in developing countries because has a

relatively low cost; 2) recent trends in the production of fossil fuels from nonconventional reserves (e.g.,

shale gas, tar sands, Venezuelan heavy oil), and ; 3) replacing fossil fuels with renewable energy (which is

constrained by various limitations such as cost and low availability) is often an unpopular decision. At the

same time, in many parts of the world, fossil fuel is still more socially accepted by the general public than

low-carbon nuclear energy because of the 2011 Fukushima disaster.

On the other hand, increasing public concerns about climate change in industry and governmental bodies

along with stricter environmental protection norms for sustainable development have created new

challenges for these industries to reduce their CO2 emissions and meet the emission targets set by

1496

environmental legislation (Al- Mayyahi et al., 2013). As a result, studies involving planning to reduce

greenhouse gas emissions at regional and national levels have been developed. The planning involves

minimize the CO2 of the energy used, whilst simultaneous meeting the CO2 emission target (carbon

footprint or emission load limits), and simultaneously fulfilling the energy demands of different economic or

geographic sectors, taking into account all the associated constraints.

Much researches have already been published on management of CO2 emission to meet environmental

targets, whilst simultaneously meeting economic constraints. Mainly of these researches create

methodologies based on Pinch Analysis (Linnhoff et al., 1982), in which a tool for carbon emission

reductions and planning has been proposed.

These methodologies carried out the principle of pinch analysis to target the CO2 emissions associated

with energy and utility systems. The first work using this concept was presented by Dhole and Linnhoff

(1992, 1993), where the Total Site Approach is used to optimize the utility system and target the CO2

associated emissions in the site. However, this approach was restricted to the optimization within industrial

facilities, and not to regional or national energy sectors. Lately, a new application developed by Tan and

Foo (2007), which was denominated as CO2 Emissions Pinch Analysis (CEPA), introduced the first

approach on the use of pinch analysis technique for carbon-constrained energy sector planning. This

technique, a graphical approach, uses energy planning composites curves to determine the minimum

amount of zero-carbon energy sources needed to satisfy the specified carbon footprint limits (Tan and

Foo, 2007). Crilly and Zhelev (2008) extended the work of Tan and Foo (2007) by including time

constraints and energy power demand forecasting. Newly, Crilly and Zhelev (2010) extended his previous

work by including the division of renewables in non-zero (biomass and biogas) and zero (wind, hydro and

landfill gas) emission factors and dealing with combined disturbance induced by new emission factor of

fossil fuels energy resources and pinch jump provoked by the use of new Kyoto limits for energy demand,

making the methodology more flexible, more robust and more resilient to the disturbances of the energy

sector. They use this approach within a larger energy-planning framework in this work.

Nevertheless, graphical pinch analysis has as limitation the accuracy of the solution, which depends on

visual resolution. To overcome this limitation some algebraic approaches was developed Foo and Tan

(2007) which developed a tabular and algebraic approach, based on a sequential analysis, called Cascade

Analysis Technique that locate precise targets for both low and zero-carbon energy sources needed to

meet a national or a regional energy demand quickly and more accurate, while not violating the CO2

emission limits. Lee et al. (2009) constructed Emission Interval Tables to locate the minimum CO2-neutral

and low-carbon sources for energy sector planning. Shenoy (2010) developed a two stage approach

based on an algorithmic procedure associated with graphical representation, called limiting composite

curve, to obtain the target of the minimum clean energy resources and synthesize energy allocation

networks to meet the already established targets.

Afterward, several works have been presented to show the application of the developed tools, based on

CEPA, with CO2 emission constraints in the Irish electricity generation sector (Crilly and Zhelev, 2008) and

New Zeland electricity center (Atkins et al., 2010), determining emissions targets and making energy

planning (Jia et al., 2009), show the applications of CEPA in China for eco-industrial parks indicating

potential Carbon Footprint reductions of 10 % and 30 %. Liang and Zhang (2011) proposed urban energy

planning using the CEPA methodology together with energy input-output model. A review of these

methodological developments divides them in graphical, algebraic and automated techniques (Tan and

Foo, 2009). The concepts of Pinch Analysis have been applied to solve carbon transfer, maximum carbon

recovery, minimum carbon targets problems and to design carbon exchange networks (CENs).

Focusing in planning for the power generation sector, a new methodology is presented in the present

paper based in the Source Diagram Concept (see, for example, the WSD in water system studies with

single contaminant - Gomes et al. (2007), the extended WSD in water systems with multiple contaminants

- Gomes et al. (2013), and the HSD for hydrogen studies in petroleum refineries - Borges et al., (2012)),

which is a heuristic-algorithmic procedure. This new methodology is called Carbon Sources Diagram

(CSD). The CSD was developed to locate the rigorous targets for both low and zero-carbon energy

sources for carbon-constrained energy planning. In other words, this methodology has the objective of

determining the optimal energy resource mix, for national or regional level, based on demand/emissions

targeting including economic constraints, such as the cost of generation and carbon prices.

2. Methodology

This section describes the methodology Carbon Source Diagram applied for energy sector planning with

emissions constraints. Equal to the work of Tan and Foo (2007), this methodology identify 1) the minimum

quantity of zero-emission energy resource necessary to meet the specified energy requirements and

1497

emissions limits of different sector or regions, and; 2) identify the energy allocation scheme to meet the

specified emission limits using the minimum quantity of zero-emission energy resource.

The Water Source Diagram is a method that was developed to determine the minimum flowrate of pure

water in management of water networks. In this work, this method is extended to set the minimum clean

source target and simultaneously provide de carbon emission network (named in this work as scenario).

To show this new tool one example taken from Tan and Foo (2007) is utilized. In Table 1, the data of the

Case Study (Example) are shown. In this case, a low-carbon source of biodiesel is assumed to be in

service with a small value of emission factor, i.e. 25 t CO2/TJ.

The left side of the table shows the emission factor (Ck,i) and the different energy source (Si) available.

These resources are assumed to be available for the horizon planning. The right side of the table shows

three distinct geographic regions that represents the demands (Dj) for the Case Study – Example, each

one with its own expected consumption and emissions limit (DjCin,j). From these data it is possible achive

the desired goals.

The following six steps are used to perform the Carbon Source Diagram:

Step 1) Determine all the emissions factors for source and demand. It is observed that for the resources

the emission factor is given, on the other hand for the demand is only informed the emission limit. To

obtain the respective emission factor for each region it is necessary divide the emission limit by the

expected consumption for each region. The results can be seen in Table 2.

Step 2) This step is similar to the step in WSD where it is used the definition of a concentration interval. In

CSD instead of concentration the emission factors are arranged in increasing order to create the intervals.

If there are more than one emission factor with the same value, only one is represented in the intervals.

Step 3) Represent all the demands in the diagram by arrows, which are delimited by their respective

emission factor and the highest emission factor on the data set. The respective expected consumption is

indicated in a column on the left side of the diagram (see Figure 1).

Step 4) Determine the amount of energy transferred in each interval. It is calculated using the following

expression (Eq(1)).

(1)

where ΔEtransf,j,t is the amount of energy transferred by the demand j in interval t; Dj is the energetic

demand; Ckfinal,t is the emission factor upper limit in interval t, Ckinitial,t is the corresponding emission factor

lower limit in interval t; and j = 1 …. Nd, and t = 1….Nint (Nd is the number of demands (regions) in Table 1

and Nint is the number of emission factors intervals in the CSD). All ΔEtransf,j,t are written in the CSD in

parenthesis over the respective arrow.

Step 5) Represent the sources in the diagram. The sources are represented above their respective

emission factor. The results of Table 1 and Table 2 are presented in the diagram shown in Figure 1.

Table 1: Data for Case Study – Example (Tan and Foo, 2007)

Energy

resource

Emission factor,

Ck,i ( t CO2/TJ)

Available resource,

Si (TJ)

Energy

demand

Expected

consumption,

Dj (TJ)

Emission limit,

DjCin,j (10
6
t CO2)

Coal 105 600,000 Region I 1,000,000 20

Oil 75 800,000 Region II 400,000 20

Natural gas 55 200,000 Region III 600,000 60

Others 0 >400,000

Total

>2,000,000 Total 2,000,000 100

Table 2: Emission factor for demands

Energy

demand

Emission factor,

Ck,j ( t CO2/TJ)

Region I 20

Region II 50

Region III 100

1498

Figure 1: Initial CSD for the data of Study Case - Example

Step 6) In this step the synthesis of the carbon emission network is effectively started, following three

rules:

Rule 1: Always use a source with the highest carbon emission factor, when this same is available. This will

generated a scenario with lower use of a low-carbon source, but not environmental friendly.

Rule 2: If more than one source is available and an environmental friendly scenario is necessary, use the

source with lower emission factor.

Rule 3: Use low-carbon source only when the others sources are not available.

Using these rules in each interval it is possible to calculate the respective low-carbon source consumption.

In some intervals it is possible to choose more than one source and depending on the source used,

different carbon emission networks can be obtained. Note that this CSD feature enables the simultaneous

analysis of different scenarios and the consideration of constrains along the procedure.

With the diagram created, the allocation of carbon sources in each interval can be started. The algorithm

analysis is made on each interval, from the lower emission factor to the higher ones. Performing the

algorithm it is possible to obtain two scenarios presented in Figure 2 and Figure 3.

Figure 2: Final CSD for the Case Study – Environmental friendly scenario

LS S3(*) S2(#) S1(&)

0 20 25 50 55 75 100 105 200

100 R1 (500) (2,500) (500) (2,000) (2,500) (500) (9,500)

40 R2 (200) (800) (1,000) (200) (3,800)

60 R3 (300) (5700)

Emission factor (t CO2/TJ)
E

n
e

rg
y
D

e
m

a
n

d
(

x
1

04
T

J
)

LS S3(*) S2(#) S1(&)

0 20 25 50 55 75 100 105 200

20 100 100 100 100 100

100 R1 (500) (2,500) (500) (2,000) (2,500) (500) (9,500)

6.67 32 40

40 R2 (200) (800) (1,000) (200) (3,800)

6.67

5.33

60 R3 (300) (5700)

# 10

31.33

18.67
#

E
n

e
rg

y
D

e
m

a
n

d
(

x
1

04
T

J
)

Emission factor (t CO2/TJ)

1499

Figure 3: Final CSD for the Case Study – Lower cost scenario

Based on the CSDs obtained in Figure 2 3, the minimum zero-carbon and low-carbon energy source are

targeted as 20 × 10
4
TJ and 92 × 10

4
TJ, respectively. An excess energy source of 41.34 × 10

4
TJ and two

pinch points at 25 and 75 t CO2/TJ, for both scenario, are also determined. The pinch points are indicated

by the higher emission factor of the last interval where zero-carbon and low-carbon source are used in

CSD. The scenario in Figure 2 privileges the use of oil instead coal, for this reason this scenario represent

higher total cost when compared with scenario in Figure 3, where is privileged the use of coal instead oil.

However, in environmental terms, scenario in Figure 2 is “cleaner” than scenario in Figure 3 because uses

energy sources with lower carbon emission factor. The same target of zero-carbon energy source was

obtained by Tan and Foo (2007a) using a graphical method; by Foo and Tan (2007) using the Cascade

Analysis (algorithm technique) and; Shenoy (2010) adopting a two stage approach.

When assessing the possibility to generate different scenario, only the approach of Shenoy (2010) allows

this possibility, however using two stages, one to obtain the target and other to generate the scenarios.

The methodology here presented has only one stage and others scenarios can be obtained with the

introduction of some restrictions, for example: in region 3 coal energy source cannot be used. If one or

more low-carbon source or higher carbon source is available it is easier to use these sources in the CSD,

being only necessary to add another interval with the respective value of carbon emission factor and

recalculate the value of energy transferred in each interval. This tool can also deal with problems involving

segregated planning and fixed zero-carbon energy supply.

3. Conclusions

An algorithmic-heuristic procedure, using the Source Diagram Concept, for planning energy sectors with

emission constraints has been developed, and presented using a case study where the targets for the

minimum quantity of zero- and low-carbon energy resources needed to meet a set of energy demand with

corresponding emission limits were found. The results found by CSD algorithm in the case study-example

show equal values when compared with those obtained by others methodologies proposed in the

literature. However, CSD algorithm has the simplicity to obtain, at the same time, target and carbon

emission network at the same diagram. In others words, only one approach is necessary. The CSD can

easily generate another scenario when solving the energy allocation problem or when some constrains are

imposed. So, the CSD can be seen as an excellent tool to decision-makers. Due to the available space

other examples and the application of the CSD algorithm to another type of problems involving carbon

emissions were not discussed in the present text.

LS S3(*) S2(#) S1(&)

0 20 25 50 55 75 100 105 200

20 100 100 100 100 100

100 R1 (500) (2,500) (500) (2,000) (2,500) (500) (9,500)

6.67 32 40

40 R2 (200) (800) (1,000) (200) (3,800)

6.67

5.33

60 R3 (300) (5700)

# 10

Emission factor (t CO2/TJ)

1500

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