Global warming potential assessment for operation of ...

45Global warming potential assessment for operation of thermoelectric power plant in Manaus

R. Latino-amer. em Aval. do Ciclo de Vida, Brasília, v. 1, n. 1, p. 45-63, jul./dez. 2017

Global warming potential assessment for operation of thermoelectric power plant in Manaus

Avaliação do potencial de aquecimento global para a operação de uma termelétrica em Manaus

Evaluación de potencial de calentamiento global para la operación de una central térmica en Manaus

Cássio de Almeida1 Vinicius Maciel2

Luiz Cybis1

Abstract

The electric sector is very important to the strategic growing of any country. It isn’t

different in Brazil, which shows a diversified energy matrix, but has a predominance

of hydropower sector. However, the thermoelectric sector has grown in the last years

to guarantee the electrical safety and, in isolated systems, the thermoelectricity is

predominant. It is the case of Amazonas State, which receives energy priority from

thermal power plants in the region. They use, mostly, fossil fuels such as Diesel, Heavy

Fuel Oil (HFO). Nowadays, it has been incorporated into this system the natural gas use

from Amazon oil basin, located in Urucu. In this sense, to analyze the environmental

influence of this change on the thermal power plants, this study intends to employ the

methodology of Life Cycle Assessment (LCA) of the electricity delivered to the grid by

one thermal power plant (TPP), located in Manaus, which uses HFO and Natural Gas as

fuel. For observation of differences, it was performed a comparative study of this power

plant in two situations: using only HFO and using HFO and Natural gas concomitant. The

study was conducted from cradle to gate of the power plant from specific primary data,

1 Institute of Hydraulic Research UFRGS (Federal University of Rio Grande do Sul) e-mail:[email protected]

2 Post-Graduation Program in Materials Engineering and Technology PUCRS (Pontifical Catholic University of Rio Grande do Sul)

Submetido em 9 fevereiro 2017 | Aceito em 17 abril 2017 | Disponível em 07 junho 2017

46 Cássio de Almeida, Vinicius Maciel, Luiz Cybis


provided by the power plant and secondary data from the literature. The Life Cycle Impact

Assessment (LCIA) was calculated from the CML IA baseline with the use of SimaPro

software and it was chosen the impact category of Global Warming Potential (GWP)

for analysis. The conversion bifuel resulted in reduction of the impact of the TPP, which

previously was 590.50 kg CO2eq / MWh and passed to 521.11 CO2eq / MWh. However, the

bifuel power plant has, along the lifecycle, when compared the operation with only HFO,

the same magnitude of GWP due to contributions of, for example, natural gas production.

Keywords: Carbon Footprint; Thermal Power Plant; Life Cycle Assessment; Natural Gas.

Resumo

O setor energético é de suma importância para o crescimento estratégico de qualquer

país. Isso não é diferente no Brasil, o qual apresenta uma matriz energética diversificada,

mas que tem um predomínio do setor hidrelétrico. No entanto, o setor termelétrico tem

crescido nos últimos anos para garantir a segurança energética e, nos sistemas isolados,

a termeletricidade é predominante. Este é o caso do estado do Amazonas, o qual recebe

energia prioritariamente de usinas termelétricas da região. As usinas da região utilizam,

em sua maioria, combustíveis fósseis tais como diesel, óleo combustível pesado (HFO,

em inglês). Atualmente, tem sido incorporada a este sistema a utilização do gás natural

proveniente da bacia petrolífera amazônica, localizada em Urucu. Nesse sentido, para

analisar a influência ambiental desta mudança nas usinas termelétricas, este emprega

a metodologia de Avaliação do Ciclo de Vida (ACV) da eletricidade entregue ao grid por

uma usina termelétrica, localizada em Manaus, que utiliza óleo combustível pesado e

gás natural como combustível. O estudo foi conduzido do berço ao portão da usina a

partir de dados primários da própria usina e dados secundários de bibliografia da área.

Para a observação das diferenças, fez-se um estudo comparativo entre a mesma usina em

duas situações: utilizando somente óleo combustível pesado e o uso concomitante deste

combustível com o gás natural. A Avaliação do Impacto de Ciclo de Vida foi calculada

pelo método CML IA baseline com o uso do software SimaPro e escolheu-se a categoria

de impacto de Aquecimento Global para análise. A conversão bicombustível resultou em

redução do impacto da usina, que antes era de 590,50 kg CO2eq/MWh e passou para

521,11 CO2eq/MWh, no entanto ao longo do ciclo de vida o resultado se manteve no

mesmo patamar.

Palavras-chave: Pegada de Carbono. Planta Termelétrica, Avaliação do Ciclo de Vida, Gás Natural.

47Global warming potential assessment for operationof thermoelectric power plant in Manaus


Resumen

El sector energético es de suma importancia para el crecimiento estratégico de cualquier

país. Esto no es diferente en Brasil, que tiene una matriz energética diversificada, pero

que tiene un predominio del sector hidroeléctrico. Sin embargo, el sector termoeléctrico

ha crecido en los últimos años para garantizar la seguridad energética y, en sistemas

aislados, termoelectricidad es predominante. Este es el caso de estado del Amazonas, que

recibe energía principalmente de centrales térmicas de energía en la región. Las plantas

de la región utilizan, sobre todo, combustibles fósiles como el diesel, fuelóleo pesado

(HFO en inglés). En la actualidad, se ha incorporado a este sistema, el uso de gas natural

de la cuenca petrolífera del Amazonas, situado en Urucu. En este sentido, para analizar

el impacto ambiental de este cambio en las centrales térmicas, este estudio emplea la

metodología del Análisis de Ciclo de Vida (ACV) de la electricidad entregada a la red

por una central térmica, que se encuentra en Manaus, que utiliza fuelóleo pesado y gas

natural como combustibles. El estudio se realizó a partir de datos primarios de la central

térmica y datos secundarios de literatura del área. Para observar las diferencias, se hizo

un estudio comparativo de la misma planta en dos situaciones: utilizando sólo el fuelóleo

pesado y el uso concomitante de este combustible con gas natural. La evaluación del

impacto del ciclo de vida se calculó por el método de CML IA baseline usando el software

SimaPro y optó por categoría de impacto del calentamiento global para análisis. La

conversión bi-combustible resultó en una redución del impacto de la planta, que antes

era de 590.50 kg CO2eq / MWh y aumentó a 521.11 CO2eq / MWh. Sin embargo a lo largo

del ciclo de vida, el resultado se mantuvo en el mismo nivel.

Palabras clave: Huella de carbono; Planta Térmica; Análisis de Ciclo de Vida; Gas Natural

1. Introduction

Brazil is considered one of the countries with the cleanest electric matrix

in the world with about 71% of its energy from renewable sources, mainly

hydroelectric plants (61%) (EPE, 2015). While 27% of the electric energy

consumed in Brazil is produced by Thermoelectric Power Plants (TPPs)

(ANEEL, 2016a), in the Amazon region, the electricity generation by TPP

represents 87% of the electric energy generation (EPE, 2016). This is due to

geographic characteristics of the region, composed of dense and heterogeneous



forest, as well as large and extensive rivers, which difficult the construction

of transmission lines of great extension that allows the connection to the

National Interconnected System (NIS), which is the system of generation and

transmission of electric power with size and characteristics that allow to

consider it as unique in the worldwide scope (ANEEL, 2008).

In Amazon, the major consumer is Manaus Free Trade Zone (MFTZ),

that was conceived as a free import and export trade area with special

tax incentives (Almeida, 2011). According to the Manaus Free Trade Zone

Superintendence (SUFRAMA, in Portuguese), the Manaus industrial center

has approximately 600 high-tech industries, which generates more than half

million jobs (SUFRAMA, 2016).

With the existence of this area, the Manaus region became quite dependent

on the industries that are there. In 2001, for example, the share of MFTZ’s

industrial activity represented 2/3 of the Amazonas State’s Gross Domestic

Product (GDP) (Filho, 2005). In this sense, a great part of the products placed

in the national market is produced in the Amazon region, since more than 90

% of MFTZ’s products are directed to the national market (SUFRAMA, 2015).

The products, in 2015, with the highest revenues were: telephones, radios,

bicycles, toys, cell phones, televisions, air conditioning, motorcycles, among

others (SUFRAMA, 2015).

In 2010, started the electric energy generation with use of Natural Gas (NG)

from Amazon basin, whose objective was to bring economic and environmental

gains to the region (PETROBRAS, 2016), diversifying the regional energy matrix.

The NG is extracted from Amazon oil basin and produced at Operational Base

Geologist Pedro de Moura (BOGPM, in Portuguese), located in Urucu, which

is unique Petrobras’ Petroleum and Gas Production Asset in the Amazon

(PETROBRAS, 2008) as showed in Figure 1.



Figure 1- Localization of extraction site, gas pipeline, refinery and Thermal Power Plant in the Amazonas State.

Source: Adapted from (BRASIL, 2017a) and (BRASIL, 2017b).

Therefore, given this new supply of NG, the TPPs of the region started

to be converted to use this fuel too. In this sense, this study carried out an

environmental comparison, in terms of Global Warming Potential (GWP),

of a TPP in two situations using only Heavy Fuel Oil (HFO) (monofuel) and

using HFO and NG concomitant (bi-fuel). The comparison of environmental

performances of power plant operation (bi-fuel and monofuel) will be

evaluated by Monte Carlo Analysis.



2. Methodology

To evaluate the differences between these two types of operation

it was used Life Cycle Assessment (LCA) methodology. LCA is a tool to

analyze environmental performances of products, processes or services,

it has been widely recognized as an important tool to understand the

environmental impacts of human activities (Guinée; et al, 2002). In this study,

the environmental profile was performed following ISO 14040 and 14044

specifications (ISO, 2006a, 2006b).

2.1. Goal and Scope definition

The objective of this study is to compare the GWP impacts of the electric

energy production in a TPP in Amazonas. The TPP installed power capacity

is 85 MW (ANEEL, 2016b), and use six Wärtsilä engines which were recently

converted to the bi-fuel operation (Ojutkangas, 2011). With this conversion,

it is possible to operate the system with HFO and NG in different proportions

(WÄRTSILÄ, 2014). According to the manufacturer, the Gas-Diesel (GD) engine

uses the diesel cycle in all operation modes.

The scope of this LCA is a cradle-to-gate system boundary, which is presented

in Figure 2, where the process and flows in green are related only to bi-fuel

operation.

The Functional Unit (FU) for this study is 1 MWh as net electricity generated

from the TPP. All the inputs and outputs are related to the FU.



Figure 2- System boundaries.

2.2. Life Cycle Inventory Data Collection

The data for this study are based on, mostly, primary data. Secondary data

was used to complete the Life Cycle Inventory (LCI) because it was impossible

to obtain all primary data, mainly for upstream processes, such as NG and

HFO production. The company, that has monopoly in fuel production, does not

share data. For these processes, it was used database support from Ecoinvent

v3.0 available in SimaPro® 8.0.3.14. Then, the quantities of products used in

TPP (chemicals, volumetric use of HFO, Lubricating Oil and Natural Gas) were

collected from 2013 operation reports and by personal visits in TPP. As TPP

was still changing its technology, the first three months was used only HFO

(monofuel operation) as fuel and the rest of this year it was used NG and HFO

to produce energy (bi-fuel operation).

Emissions to air were calculated from quality air reports, which were

produced under the responsibility of TPP due to regulations in Brazil

(Brasil, 2011).



The emission rate was calculated by equation (1), with the average

concentrations and exhaust gas flow. The numbers of engines and the

operation time were considered.

Equation 1

Where:

n= Numbers of Engines;

TE = Emission Rate (kg/h);

C = Average Concentration (mg/Nm³)

Q = Exhaust Gas Flow (Nm³/h);

For the pollutants not listed in the reports (CO and CO2), it was used emission

factors presented by the responsible company, which are presented in Table 1.

Table 1- Emission Factors for NG and HFO (kg/MWh)

Emission Factors (kg/MWh)

Pollutant NG HFO

CO2 1.82E+2 2.54E+2

CO 6.21E-2 5.12E-2

NG: Natural Gas; HFO: Heavy Fuel Oil

Source: Adapted from (Wärtsilä, 2013)



Finally, the emission rates are calculated by equation (2).

TE=LHV*FE

Equation 2

Where:

TE= Emission Rate (kg pollutant/ kg or m³ of fuel)

LHV= Lower Heating Value (kcal/kg) or (kcal/m³);

FE= Emission Factor (kg pollutant/ kcal fuel energy)

The emission rate values (kg/h) were related to the operating time of each

month, resulting in the mass emission rates for each month. So, this information

was related to the energy delivered to the grid during those months.

Due unavailability of primary data for wastewater and solid waste, it was

used bibliography data, which analyzed the quality and quantity parameters

of wastewater and solid waste for another TPP in Manaus (França, 2015), that

uses the same reciprocating engine and technology of this work.

2.3. Study considerations and Limitations

The data quality was based on operational conditions and similar technology

used in TPP, in order to show the real implications of the operation.

For upstream processes, it was used Ecoinvent v3.0 database for all processes

that are outside of the TPP boundary. Moreover, Ecoinvent supported enough

data to model emissions amounts in reference to supplies processes, transport

truck and power grid.

Due to the complexity of chemical characteristics of some products used

in TPP, some of them were not available in the Ecoinvent v.3 database. With

the idea to overcome this limitation, it was analyzed similar substances those

used in TPP in order to compose the inventory. When one substance was not

available in the database, its quantity was summed up with another substance

(available in the database), which presents potential impacts, i.e, it was used

a worst case approach.



The transport data is very important, mainly in Amazon region, once it is

a difficult place to transport goods due to the lack of logistic infrastructure

(Ribeiro et al., 2016). Thus, each product has a different route that is presented

in Table 2. The distances were considered by information received in TPP visit.

A portion of HFO is imported (50 % volumetric), that it comes from Venezuela

by navy. The other portion is national and comes from Urucu (in the Amazon),

where is extracted the NG and it is transported by pipeline and ship until

Manaus. After all products arrive in Manaus, they are transported by road to

TPP. The background data (production of inputs used in TPP, and transports)

have been sourced from the Ecoinvent v3.0 database.

Table 2- Transport distances between products production and Thermal Power Plant (TPP).

Product Amount (km) Source Comment

Lubricating Oil 4300 Visit at TPPDistance between Rio de Janeiro

and Manaus

HFO (import) 4837.42 Visit at TPPDistance between ports in

Venezuela and Manaus

Chemicals 3996 Visit at TPPDistance between Cotia (São Paulo) and

Manaus

All products /Landfill 50 Visit at TPPDistance between Manaus and TPP/ Distance between TPP and Landfill

Natural Gas (gas pipeline)

663.2(PETROBRAS,

2008)Extension of Urucu-Coari-Manaus gas

pipeline

HFO (national) 452 Visit at TPPDistance between ports Coari and

Manaus

HFO (oil pipeline) 281Transpetro

(2006)Extension of Urucu-Coari oil pipeline

HFO: Heavy Fuel Oil.

2.4. Impact Assessment and Interpretation

The method for calculating GWP chosen for this study was CML baseline,

which is one of the most widely used (Agrawal et al., 2014; Mori et al., 2014;

Vinodh et al., 2012). Besides that, this method is recommended to application



in Brazil (Mendes et al., 2016). This method, in its last version (4.7) calculates

the climate change based on the GWP factors extracted from the fifth

assessment report of the Intergovernmental Panel on Climate Change (IPCC)

published in 2013 (CML, 2016).

In LCA, there are uncertainties in the various data inputs all add up and

can heavily influence your LCA results (Golsteijn, 2015). Because of this

situation, the comparison monofuel to the bi-fuel operation was performed

by application of stochastic approach based on Monte Carlo (MC) analysis

(BOGUSLAW, 2014), which varies entry data of the model calculation randomly

according to the uncertainty distributions (Winter et al., 2015). The MC

simulation, in SimaPro® 8.0.3.14, takes a random value from the uncertainty

distribution for each uncertain data input and calculates and stores the LCA

results for this set of sampled values.(Golsteijn, 2015). SimaPro® 8.0.3.14

supports four types of distributions: uniform, triangular, normal and log-

normal (PRé, 2016a). The comparative simulation, using MC analysis,

performs the subtraction of two systems, where the results indicate the

probability of one option generating more damage than the other (QUANTIS,

2011) or the number of times that a product system has more or less load than

another (PRé, 2016a). In this work, uniform distribution was used, which

needs the minimum and maximum values for each data (PRé, 2016b). MC

was conducted with 95% confidence level and 1000 cycles. Besides MC, it

was performed a comparison in relation to contribution analysis to show the

processes that have a major contribution for GWP.

3. Results and Discussion

3.1. Life cycle Inventory Results

The LCIs were developed for the two operation modes and are presented

in Table 3. In this table is shown maximum and minimum values, which were

later used for MC comparison. In relation to inputs, there was a decrease of 6



products, with the greatest highlight to Adipic Acid, that use in bi-fuel mode

represents 38 % of monofuel use. On the other hand, lubricating oil had an

increase in use in 20 % due to compressors system, which compresses natural

gas, and uses lubricating oil in operation (França, 2015). In relation to outputs,

there was a reduction in flows of 73% for all compartments, but there were

some increases, such as, for example, CO emission.

Table 3 - Life Cycle Inventory to monofuel and bi-fuel operation

Flow CompartmentUnit/

FU

Monofuel Bi-fuel

AverageAmount

MinimumMaximum Average Amount

MinimumMaximum

Inputs

HFO tecnosphere kg 2.02E+02 1.86E+02 2.11E+02 7.56E+01 4.24E+01 1.54E+02

Natural Gas tecnosphere m³ - - - 1.90E+02 6.61E+01 2.54E+02

Lubricating Oil tecnosphere kg 5.27E-01 4.93E-01 5.95E-01 6.33E-01 5.36E-01 7.73E-01

Sodium Nitrite tecnosphere kg 1.42E-03 0.00E+00 1.70E-03 7.55E-04 9.02E-05 1.62E-03

Phosphoric Acid tecnosphere kg 1.22E-05 1.09E-05 1.45E-05 1.41E-05 7.29E-06 1.82E-05

Sodium Hydroxide tecnosphere kg 6.71E-04 6.79E-05 8.37E-04 2.88E-04 0.00E+00 5.47E-04

Adipic Acid tecnosphere kg 6.11E-06 5.43E-06 7.24E-06 2.34E-06 6.83E-07 4.10E-06

Ammonium Chloride tecnosphere kg 3.17E-06 2.04E-06 4.75E-06 2.34E-06 6.83E-07 4.10E-06

Formaldehyde tecnosphere kg 2.26E-05 1.13E-05 3.39E-05 2.70E-05 1.14E-05 4.56E-05

Monoethanolamine tecnosphere kg 1.58E-04 7.92E-05 2.38E-04 1.89E-04 7.97E-05 3.19E-04

Outputs

Nitrogen Monoxide (NO)

air kg 4.16E+01 2.95E+01 5.04E+01

Nitrogen Oxides (NOx)

air kg 6.43E+01 4.31E+01 7.91E+01 2.51E+01 1.99E+01 2.92E+01

Sulfur Dioxide (SO2) air kg 4.83E+00 2.21E+00 7.73E+00

Sulfur Oxides (SOx) air kg 2.29E+00 1.35E+00 3.05E+00

Particulate Matter air kg 3.41E+00 2.64E+00 4.05E+00 1.10E+00 7.42E-01 1.28E+00

Carbon Dioxide (CO2) air kg 5.90E+02 5.42E+02 6.16E+02 5.22E+02 2.29E+02 8.54E+02

Carbon Monoxide (CO)

air kg 1.19E-01 1.09E-01 1.24E-01 1.47E-01 6.08E-02 2.28E-01

HFO: Heavy Fuel Oil; NO: Nitrogen Monoxide; NOx: Nitrogen Oxides; SO2: Sulfur Dioxide; SOx: Sulfur Oxides; CO2: Carbon Dioxide; CO: Carbon Monoxide; FU: Functional Unit (1 MWh); Source: primary data.



3.2. Life Cycle Assessment Results

This study showed that in monofuel operation, the life cycle impact was

690.19 kg de CO2 eq/MWh, while in bi-fuel operation was 690.11 kg CO2 eq/

MWh (value without MC analysis). So, it was considered a tie between modes

of operation because there are differences between values only in the second

decimal place.

However, considering MC analysis, GWP was considered inconclusive, since

there was little difference between the numbers of times the two systems had

a lower environmental load for this impact category. For example, the bi-fuel

operation had a lower load 51% of cases, while in monofuel 49%. Therefore, it

is possible to ensure in statistical terms that there are no significant differences

between the two types of operation for GWP, as it is shown in Figure 3.

Figure 3- Comparison between bi-fuel mode (A) and monofuel (B)

Also a contribution analysis was conducted and its results shows that TPP

has the major contribution for GWP in two modes of operation, although

is important to highlight that the contribution of TPP in GWP impact had a

decrease, since, in monofuel operation, contribution of TPP was 85.56% and



decreased to 75.61 % with the NG use (bi-fuel operation), which is shown in

Figure 4. HFO production had a decrease too, due to the minor use of this fuel.

In bi-fuel operation, there was a considerable contribution from NG chain

(Production and pipeline transport), which together resulted in approximately

19% of contribution for GWP.

Figure 4- Contribution Analysis for GWP impact.

FFor bi-fuel operation, in terms of substances, 90% of the impact is due to

CO2 emission. To produce energy, it’s necessary burning fuel, so TPP contributes

with 83% of this emission followed by on-shore NG production, with 10 % of



emissions. Previous study has pointed out similar contribution, such as 89.1

% for TPP (burning fuel to produce energy) (Phumpradab et al, 2009). Despite

the decrease in the plant contribution other processes had growth, so that the

impact along the life cycle was equal to the monofuel operation. For example,

natural gas production contributed, in this category, with 17.31% of the

impact. Similar value was observed in the work of Phumpradab et al (2009),

which had natural gas production with about 10% contribution in GWP for

combined cycle power plant.

Results found in this work are similar with those searched in Literature.

For example, Turconi et al (2013) analyzed 167 LCA studies about electricity

generation and found that studies that used NG as energy source had life cycle

impact values ranging from 380 to 1000 kg CO2 eq/ MWh (net) while TPP that

use oil as fuel range from 530 to 900 kg CO2 eq/ MWh. Phumpradab et al (2009)

calculated the GWP impact with a value of 689 kg CO2 eq/MWh for a TPP that

uses NG and HFO as fuel and 539 kg CO2 eq/MWh for the combined cycle power

plant (which uses only NG). A brazilian work, that analyses the potential to

reduce GWP in the mix grid energy, calculated the GWP for different types of

generation, being that for NG, the GWP impact was 523 kg CO2 eq/MWh and

for diesel oil 832 kg CO2 eq/MWh (Delgado and Carvalho, 2016), showing the

same magnitude of this work.

Therefore, this study shows that is possible to claim that both operation

modes have similar statistical results for GWP. Besides that, bi-fuel operation

have shown a strong contribution of NG chain (NG extraction and transport)

with almost 19% of contribution, influencing decisively for the tie among

operation conditions.

4. Conclusions

The results show that the change in the operation mode (monofuel to bi-

fuel) did not bring a huge difference in terms of GWP impact. In addition, if



TPP were implemented in other place or there were a major NG use, should

be observed the distance between extraction and final use, because it may

become bi-fuel operation worse than which was modeled in this paper.

Also, in this work MC simulation was very important to show the real tie

between monofuel and bi-fuel operation, which a simple comparison did not

bring the same conclusion, because MC takes into account the uncertainties in

data, improving the robustness to the result.

It is recommended to investigate the NG production in Amazonas state,

which it was tried in this work but not succeed due to unavailability of open

data of the company responsible. This recommendation is important because

NG production had an important role in results, with the greatest contribution

(after the TPP, which is the first one).

Finally, the results can be useful for future researches for comparison with

other feedstocks and others power production technologies. Also, the results

can be useful for future LCA studies mainly those involving materials produced

in Brazil and, more specifically, in the MFTZ, once the products produced

there are fed by the local TPPs. Besides that, this work also contributes to the

knowledge base of LCA and energy systems in Brazil.

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