gases (GHGs) from renewable energy generation were identified from the ... AMS I.L (electrification of rural communities using renewable energy) model was ...
1 1
ESTIMATING CARBON CREDITS FROM RENEWABLE ENERGY GENERATION
2
IN ISOLATED COMMUNITIES OF THE AMAZON
3
Abstract: In this study, carbon credits were estimated by implementing a renewable energy
4
generation project in a community of the Amazon. First, models for calculating greenhouse
5
gases (GHGs) from renewable energy generation were identified from the methodology for
6
small-scale Clean Development Mechanism (CDM) projects. Next, the community’s annual
7
energy demand was estimated and verified for all demand types; a total demand of 18.7 kW
8
was obtained for a projected annual consumption of 296.9 MWh. Based on this result, the
9
AMS I.L (electrification of rural communities using renewable energy) model was selected as
10
most reflected the local reality. With this model, a reduction of 337.9 tCO2 emissions per year
11
was observed, achieving a total of 3.379 tCO2 for a CDM project with duration of 10 years.
12
This represent a value of US$ 40,041.15 (corresponding to R$ 90,713.23) could be obtained
13
from sequestered tons of CO2.
14
Keywords: Isolated communities. Estimation of carbon credits. Energy generation.
15
Renewable energy sources. Sustainable development.
16
1. INTRODUCTION
17
The electrification process in the Amazon is essential for achieving sustainable development
18
in the region. However, in many isolated places in the Amazon, there is no electricity,
19
particularly due to the costs and the difficulty of supplying it through the Interconnected
20
National System (Sistema Interligado Nacional – SIN) of energy distribution. The challenge
21
to supply is a result of the great territorial extent, low demographic density, dense
22
hydrographical network, numerous flooded areas and dense forest.
23
The 2000 Census from the Brazilian Institute of Geography and Statistics [1]
24
indicated that in the legal territory of the Amazon, there were 769,270 rural homes with no
25
electricity, and 478,072 were in the Northern Region. Between 2000 and 2010, there was an
2 26
approximately 51.63% decline in non-electrified homes in the region, to a total of 231,220
27
homes. This reduction is a result of the Light for Everyone Program [2] that was implemented
28
by the Federal Government, supplying approximately two million electrical connections in
29
Brazil between 2004 and 2009.
30
One possible solution to this problem would be the implementation of energy
31
generation projects using renewable energy sources, thereby creating Carbon Credit projects
32
with the purpose of obtaining CERs (Certified Emission Reductions). However, renewable
33
energy systems depend on large financial incentives to compete with conventional generation
34
methods. Market-based incentives, including carbon markets have been proposed as solutions
35
to increase renewable energy investment [3]. The commercialisation of Carbon Credits and
36
the generated profits will contribute to investment in and maintenance of these systems,
37
leading to sustainability and autonomy without the need for a great investment by the
38
government after project installation. In the world, eighty-one percent of all proposed CERs
39
are coming from the Asia Pacific. China has the first largest share of CERS with 55.3 %.
40
India has the second largest with 15.5 %. In the Latin America, Brazil has 6.3% of 14% of all
41
proposed CERs [4].
42
The CERs are carbon credits (CC) generated by the Clean Development
43
Mechanism (CDM) of the Kyoto Protocol. According to Ribeiro [5], a CDM can only be
44
implemented in developing countries (such as Brazil). In fact, the CDM is an adaptation of an
45
originally Brazilian concept.
46
These projects can help to reduce the rate of global temperature increases by
47
carbon emission reduction. In this case, an Evaluation Report by the Intergovernmental Panel
48
on Climate Change [6] predicts an increase in global average temperatures between 2.6 and
49
4.8 ºC until the end of the century. In the context of global warming, according to
50
Goldemberg and Villanuez [7], among all GHGs, CO2 contributes 60% to global warming.
3 The study also revealed that 57% of all CO2 emissions in the world originated from the
52
generation of thermal energy, which in the isolated communities of the Amazon come
53
primarily from diesel generators [8]. As a result, this study estimates CCs to determine the
54
GHG emissions reductions renewable energy projects.
55
2. MATERIALS AND METHODS
56
2.1 Certified Emissions Reductions
57
Emissions reductions involve the elimination of pre-existing GHG emissions as a result of
58
CDM project implementation, i.e., they are the difference between a hypothetical (a baseline –
59
Figure 1) and a fact (a verified emission reduction from the project). The baseline indicates a
60
scenario that reasonably represents anthropogenic GHG emissions from sources that do not
61
implement emissions reduction activities. To clarify, the Kyoto Protocol requires that
62
reductions be added to those that are occurring in the absence of the project, i.e., if reductions
63
were already taking place, the projects would need to produce greater reductions [9].
64 65
Emission reduction
Greenhouse gas emissions effects (tCO2)
51
Time (years)
Figure 1 – Baseline. Source: [9].
66
By implementing the project, the CERs are converted to CCs, which are
67
commodities that can be sold on the international market, where each credit corresponds to a
68
ton of carbon dioxide prevented from being emitted into the atmosphere. The amount of
4 69
credits per ton of GHG is determined based on its global warming potential with carbon
70
dioxide as a reference and they are calculated as follows [10]:
71
- Carbon Dioxide (CO2) = 1;
72
- Methane (CH4) = 21;
73
- Nitrous Oxide (N2O) = 310;
74
- Hydrofluorocarbons (HFC) = 140 ~ 11700;
75
- Perfluorocarbons (PFC) = 6500 ~ 9200;
76
- Sulphur Hexafluoride (SF6) = 23900.
77
In addition to the environmental criteria, economic criteria will also likely be used
78
to rank CDM candidate projects. The greater the contribution of CCs to economic viability,
79
the higher the project will rank in the approval process of the Executive Committee of the
80
Framework Convention on Climate Change [11].
81
2.2 Clean Development Mechanism
82
CDM was devised to promote sustainable development in developing countries, using
83
resources from developed countries. Currently, there are four types of project activities in the
84
CDM, which are divided into two different groups, with the objective of reducing GHG
85
emissions.
86
The first is called a large-scale CDM (from the conventional or traditional group).
87
This type of project activity is characterised by the absence of expansion limits. Furthermore,
88
the methodologies used in this type of project must be elaborated by the project manager and
89
made public after their approval. According to the CGEE (Management Center and Strategic
90
Studies) [7], the methodologies for large-scale CDMs are more conservative and are very
91
restrictive. Therefore, aspects such as leakage, feedstock transportation, emissions during
92
construction, emissions from waste disposal, equipment calibration and information
93
registration are treated in greater detail.
5 94
The second type is a small-scale CDM project, which was also in the traditional
95
group and emerged from the perception that all of the procedural costs involved in the
96
development of a large-scale CDM would make their application infeasible for many smaller
97
sized companies. As a result, these projects aim at reducing the transactional costs. The small-
98
scale CDM projects are the focus of this study because they are applicable to the small
99
communities of the Amazon.
100
2.3 Baseline Methodology for CERs Estimates
101
The AMS I.L (Approved Small Scale Methodologies Electrification of rural communities
102
using renewable energy) was used for this project [12] and substitutes the use of fossil fuels.
103
This methodology limits an energy generation system to an installation capacity of no more
104
than 15 MW and is limited to energy facilities and consumers without access to any electrical
105
energy distribution system (national or regional network). Such final use facilities can include
106
residences, public buildings and/or micro-, small and medium-sized enterprises. Electricity
107
use can include interior or exterior lighting, refrigeration, agricultural pumping and drinkable
108
water distribution systems. The methodology stipulates that at least 75% of the electricity
109
consumption should be domestic.
110
In this case, two parameters are needed to determine the baseline:
111
- The amount of electricity consumed by the facilities supplied by the electricity
112
generation system from renewable sources; and
113
- The number of facilities (for instance, residential, business and public buildings)
114
supplied with electricity from the proposed system.
115
Next, the baseline emissions factors are presented, which were established using
116
the present methodology, for each interval of energy consumed every year by the facilities
117
over the credit period.
6 118
- The first 55 kWh of energy consumed by each baseline emissions factor is 6.8
119
tCO2/MWh;
120
- For facilities that consume more than 55 kWh up to 250 kWh per year, the
121
baseline emissions factor is 1.3 tCO2/MWh;
122
- For facilities that consume more than 250 kWh per year, the baseline emissions
123
factor is 1.0 tCO2/MWh.
124
Throughout the entire project, the baseline emissions are calculated using
125
Equation 1:
BE y BE55, y BE 250, y BE morethan250, y
(1)
126
wherein
127
BEy – Baseline emissions during year y, in tCO2/y;
128
BE55,y – Reference emissions for facilities that consume less than 55 kWh of energy from
129
renewable source projects in year y, in tCO2/y;
130
BE250,y – Reference emissions for facilities that consume more than 55 kWh up to 250 kWh of
131
energy from renewable source projects during year y in tCO2/y; and
132
BEmore than250,y – Reference emissions for facilities that consume more than 250 kWh of energy
133
from renewable source projects in year y, in tCO2/y;
134 135
For facilities that consume up to 55 kWh per year, the baseline is calculated using Equation 2: N
BE 55, y EG x , y EFCO 2,55
(2)
x
136
wherein
137
EGx,y – Electricity from a renewable electricity generation system is delivered to facility x,
138
where the energy is lower than 55 KWh in year y, in MWh;
139
EFCO2,55 – 6.8 tCO2/MWh;
7 140
x – Facilities supplied with renewable electricity from electricity generating systems with
141
consumption lower than 55 kWh in year y; and
142
N – Number of project facilities with consumption lower than 55 kWh/year.
143 144
For facilities that consume more than 55 and up to 250 kWh per year, the baseline is calculated using Equation 3: M
BE 250, y (( EG z , y 0.055 ) EFCO 2, 250 C )
(3)
z
145
wherein
146
EGz,y – Electricity delivered from a renewable electricity generating system to facility x, when
147
the energy delivered is more than 55 kWh but equal to or less than 250 kWh in year y, in
148
MWh;
149
EFCO2,250 – 1.3 tCO2/MWh;
150
z – Facilities supplied with renewable electricity from electricity generating projects with
151
consumption higher than 55 kWh and smaller than 250 kWh in year y;
152
C – 0.374 tCO2, a constant calculated as 0.055 MWh x 6.8 tCO2/MWh; and
153
M – Number of project activity facilities with consumption higher than 55 kWh and smaller
154
than 250 kWh in year y.
155 156
For facilities that consumed more than 250 kWh per year, the baseline is calculated using Equation 4: P
BE morethan250, y (( EG w, y 0,250 ) EFCO 2,morethan250 D)
(4)
w
157
wherein
158
EGw,y – Electricity delivered by the renewable electricity generation system to facility x, when
159
the energy delivered is higher than 250 kWh in year y, in MWh;
160
EFCO2,more than 250 – 1.0 tCO2/MWh;
8 161
w – Facilities supplied with renewable electricity by an electricity-generating project with
162
consumption higher than 250 kWh in year y;
163
D – 0.6275 tCO2, a constant calculated by 0.055 MWh x 6.8 tCO2/MWh + 0.195 MWh x 1.3
164
tCO2/MWh;
165
P = Number of project facilities with consumption higher than 250 kWh/y.
166
In this study, the project emissions are considered to be zero (PEy = 0). In regard
167
to leaks (LEy), if energy generation equipment is transferred from one activity to another,
168
leaks should be considered; otherwise, they should be considered as having a LEy = 0.
169
The yearly emissions reductions (ERy) are calculated as follows:
ER y BE y PE y LE y 170
wherein
171
ERy – Emission reductions during year y, in tCO2/y;
172
BEy –Baseline emissions in year y, in tCO2/y;
173
PEy – Project emissions in year y, in tCO2/y; and
174
LEy – Leaked emissions in year y, in tCO2/y.
175 176
Figure 2 – Location of the small community of Marinho. Source: [13].
(5)
9 177
2.4 Study Area
178
The data for this study was from the Água Branca do Cajarí Rural Community, in Laranjal do
179
Jarí city, in the state of Amapá in Brazil (Figure 2). This community is located in an
180
extractive reserve (Resex Cajarí). Therefore, natural resource use is restricted; the majority of
181
habitants survive almost exclusively on extraction.
182
The community (composed of 90 residences) has the following infrastructure:
183
water delivery, public lighting, two schools, one health centre and two government buildings.
184
Furthermore, there are two factories: one produces Brazil nuts and the other produces manioc
185
flour (Table 1).
186
187
Table 1 - Power demand of the Água Branca do Cajarí community. Description
Quantity
Power Demand (KW)
Residences
90
0.25 - 0.40
Public lighting
10
0.10
Schools
2
1.00
Health centres
1
0.50
Government buildings
2
0.50
Water collection and treatment systems
1
19.00
Brazil nut production factory
1
1.00
Manioc flour factory
2
1.50
Source: [13]
188
Electricity is supplied to the community by a diesel generator that works four hours
189
per day. Currently, another problem with energy generation is the high operational cost.
190
Although its installation cost is relatively low, the operational costs are high because the price
191
of fuel (diesel oil) used to generate electricity is also high; this cost, combined with the
10 192
logistics needed to supply fuel the community (the reality of many communities in Amazon),
193
increases the price even more.
194
In this study, to the carbon credit estimate, the community was considered to be
195
supplied with renewably sourced energy, without taking into account the currently installed
196
system (diesel generator).
197
2.5 Estimate of Annual Electric Power Demand by the Community
198
With the quantification of the electricity demand, a methodology that is most adequate for
199
determining the baseline for the Água Branca do Cajarí community was verified. The results
200
were obtained from a study by Quintas et al. [13], where the energy demand was estimated
201
using Technological Innovation and Document Management (TIDM) [14], which includes a
202
more detailed analysis of energy demand. This method was selected because in small isolated
203
rural systems energy demand estimates are performed based on an installed capacity, i.e., the
204
maximum energy demand assumes all energy consumers are working simultaneously. This
205
method is used because the systems do not meet demand over 24 hours, increasing the
206
demand to a maximum whenever the system is operated.
207
Not all demands occur simultaneously; in fact, according to the degree of poverty
208
or development, industrial consumption is absent at night, as are public services. This results
209
in the consideration of different day-time and night-time demands [13]. Two other factors are
210
also included: simultaneity and the degree of home appliance use, described as follows:
211
- Simultaneity Factor (fs): the possibility that a number of users are
212
simultaneously using electrical equipment, varying from 0 to 1;
213
- Use Factor (fu): intensity characteristics of the equipment, varying from 0 to 1.
214
In the case of day-time and night-time loads, the demand values were obtained by
215
multiplying each demand type by a factor of fs and fu. The highest of these two demands was
216
added to the loss due to transmission (5 to 10%), resulting in a total system demand.
11 217
From the results presented in Table 1, the total power demand in the community
218
(Table 2) was estimated. In the case of residential power demand, a maximum value of 0.40
219
kW was adopted. For losses due to transmission, an average value of 7.5% was selected.
220
Factors of fs and fu ranged between 0.5 and 1.0. Using the result for community demand, the
221
installed power of the renewable energy system is assumed to be equal to or slightly higher
222
than the community demand. As a result, the community energy demand is indicative of the
223
range of the installed capacity determined using baseline methodologies.
224
Table 2 - Total energy demand by the Água Branca do Cajarí community. Demand type
Installed power (kW)
fs
fu
(kW)
fs
fu
(kW)
29.25
0.6
0.5
8.8
0.8
0.7
16.4
3.5
1
1
1
0
0
0
Lighting
1
0
0
0
1
1
1
Industrial
4
1
1
1
0
0
0
Total
16.3
Domestic Institutional
Day-time Load
Night-time Load
Loss due to transmission Total demand of Água Branca do Cajarí community
Total
17.4 7.5% 18.7
225
Source: [13]
226
3. RESULTS AND DISCUSSION
227
3.1 Estimate of Annual Energy Consumption by the Community
228
According to the results presented in Figure 3 and Table 3, the residential demand consumes
229
the largest amount of energy, with an annual consumption of 223.5 MWh, corresponding to
230
75.28% of the energy consumed by the community. The hours of energy consumption per
231
day, adopted and presented in Table 3, were selected in an attempt to obtain results closest to
232
the community context.
12
233 234
Figure 3- Annual energy consumption by demand type.
235 236 237
Table 3 - Estimate of annual electricity consumption in the Água Branca do Cajarí community.
Demand Type Residences Public lighting Schools Health centres Government buildings Water collection and treatment systems Brazil nut production factory Manioc flour factory
Power Quantity demand (kW)*
Consumption Hours of daily adopted
Daily unit (kWh)
Annual unit (MWh)
Total daily (kWh)
Total annual (MWh)
%
90
0.25 – 0.40
17
6.80
2.482
612
223.5
75.28
10
0.10
12
1.20
0.438
12.0
4.380
1.48
2
1.00
10
10.0
3.650
20.0
7.300
2.46
1
0.50
10
5.00
1.825
5.00
1.825
0.62
2
0.50
10
5.00
1.825
10.0
3.650
1.23
1
19.0
6
114
41.61
114
41.61
14.02
1
1.00
10
10.0
3.650
10.0
3.650
1.23
2
1.50
10
15.0
5.475
30.0
10.95
3.69
296.9
100
Annual consumption by community 238
* Power demand obtained from data from [13]
13 239
3.2 Carbon Credit Estimate
240
To estimate CCs, a methodology from the AMS I.L was applied. This methodology was
241
selected because it was the simplest and most representative of the community and because its
242
characteristics obey the limitations of the methodology. Among such characteristics are the
243
following:
244
- Table 3 demonstrates that 75.28% of the annual electricity consumption is from
245
residences (the methodology highlights that at least 75% of the final use of the
246
facilities connected to the renewable electricity system in the project must be
247
residential);
248
- The studied community is not connected to the SIN (this is a crucial criterion in
249
the methodology);
250
- The community electricity demand is lower than the limit stipulated by the
251
methodology, because even if the installed capacity of a renewable electricity
252
source had a safety margin for community supply, it would not reach 15 MW,
253
which is the value stipulated as the maximum limit.
254
Furthermore, Table 3 demonstrates that any type of demand consumes more than
255
250 kWh per year; therefore, Equation 4 was applied (as a baseline estimate for facilities that
256
consume more than 250 kWh per year). As a result, the data presented in Table 4 were
257
obtained. It should be emphasised that the baseline is the emission that would occur in the
258
absence of the CDM project. Consequently, a value of 337.89 tCO2 per year corresponds to
259
the CO2 emissions equivalent for the operation of an energy generation system operated with
260
fossil fuels such as diesel oil.
261
The project emissions are considered to be zero (PEy = 0) because in this study,
262
the electricity generation system is assumed to use the following renewable sources:
263
photovoltaic energy, hydro-energy (by run-of-river) and biomass energy.
14 264 265
Leakages are not considered (LEy = 0) because the energy generation equipment is assumed be stationary, or the existing equipment has not been transferred to another activity.
266
Therefore, by using Equation 5, ERy = BEy (Table 4) is obtained, and considering
267
a CDM project with a duration of 10 years, the carbon credit estimate would be 3,379 tCO2.
268
At the moment, there is no evidence in the studied literature of any CDM project in Brazil that
269
used this type of methodology to compare results.
270
Table 4 - Baseline estimate. Annual unit of Demand type
Quantity
consumption (MWh)
P
(( EG
w, y
0,250 ) EFCO 2, morethane250 D)
w
(tCO2/y)
Residences
90
2.482
257.4
Public lighting
10
0.438
8.155
Schools
2
3.650
8.055
Health centres
1
1.825
2.203
2
1.825
4.405
1
41.610
1
3.650
2
5.475
Government buildings Water collection and treatment system Brazil nut production factory Manioc flour factory
BEy (tCO2/y)
41.99 4.028 11.71 337.9
271 272
According to http://californiacarbon.info/ [15], the level of emissions permitted in
273
California equalled US$ 11.85/t (August 2014). Hence, a value of US$ 40,041.15
274
(corresponding to R$ 90,713.23) could be obtained by the tons of CO2 that will not be
275
produced due to the project. Even by subtracting the costs associated with the CDM project
276
approval process, considerable value could be obtained for operational and maintenance
277
expenses of the system.
15 278
4. CONCLUSION
279
This study estimated the CCs using a renewable energy generation project in the community
280
of Água Branca do Cajarí, in the Municipality of Laranjal do Jarí, in the state of Amapá in
281
Brazil. The analysed project estimated a value of US$ 40,041.15 (corresponding to R$
282
90,713.23) that could be obtained by the tons of CO2 that will not be produced due to the
283
project. This capital could be invested in electricity systems powered by renewable energy,
284
which are of crucial importance to the sustainable development of the Amazon Region. It
285
would provide greater autonomy to isolated electricity systems, especially with the
286
elaboration of CDM projects, by collecting funds for operation and maintenance of these
287
systems. In addition these projects will help to reduce the emissions of greenhouse gases into
288
the atmosphere.
289
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290
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