Free Essay

Powerplant Design in Iloilo Province

In: Science

Submitted By gpjardinico
Words 16888
Pages 68
Demand Quantification for Fuel Requirements,
Energy Source, Conversion Technology and Siting, and Powerplant Design in the Province of Iloilo

Design Paper by

Gino P. Jardinico
BS Mechanical Engineering Student: 2011-15731

Submitted to the Department of Mechanical Engineering
College of Engineering
University of the Philippines

In Partial Fulfilment of the Requirements of
ME 188
Power Plant Engineering

Department of Mechanical Engineering
College of Engineering
University of the Philippines Diliman
Quezon City

December 2015

ii

Abstract

The study is aimed (1) to quantify the demand for fuel requirements and powerplant capacity in the province of Iloilo for the next 30 years, and (2) prioritize the energy sources, conversion technologies and powerplant sites available in the province.
Electricity consumption from the five sectors (residential, commercial, transportation, industrial, and agricultural) were calculated using different models and assumptions. These values were projected to the year 2045 in order to predict the total electricity demand on the daily, weekly, monthly and yearly bases. There is an average of
262.28 MW demand from the computed consumptions which was translated to fuel requirements considering the existing, upcoming, decommissioning, and the reserve. A total of 284.77 MW plant capacity was calculated for 2045. Assuming that bituminous coal will be used as the main fuel for running a Coal-fired powerplant in the future, a fuel requirement of 12.25 kg/s was obtained.
From the different types of the considered fuel sources and technologies, an underfed stoker Coal-fired powerplant appeared to be the best pick for Iloilo based on the
Edward’s model applied to the screening. A site from Barotac Viejo town in Iloilo resulted to be the top location for a coal-fired powerplant for the whole province.

iii

Table of Contents

Abstract

ii

Table of Contents

iii

Nomenclature

vi

List of Figures

vii

List of Tables

vii

List of Equations

ix

1. Introduction

1

1.1

Location Map

2

1.2

Justification

4

1.2.1 Province Justification

4

1.2.2 Powerplant Justification

5

1.3

Statement of the Problem

6

1.4

Significance of the Study

7

1.5

Objectives of the Study

8

2. Demand Quantification

9

2.1

Residential Sector

9

2.2

Commercial Sector

16

2.2.1 Machine Shops

16

iv

2.2.2 Schools
2.2.3 Offices

23

2.2.5 Restaurants and Food Establishments

26

Transportation Sector

28

2.3.1 Roads

29

2.3.2 Bus Stations

31

2.3.3 Railways

33

2.3.4 Airports

35

2.3.5 Seaports
2.4

22

2.2.4 Furniture Shops

2.3

19

36

Industrial Sector

37

2.4.1 Iron & Steel
2.4.2 Food Manufacturing
2.5

38
39

Agricultural Sector

40

2.5.1 Livestock: Cattle

40

2.5.2 Fish Culture

41

2.5.3 Rice Production

42

2.5.4 Corn

43

2.5.5 Poultry

44

3. Total Demand

45

v

4. Powerplant Capacity and Fuel Requirement

46

4.1

Powerplant Capacity

46

4.2

Powerplant Proposal

47

4.3

Fuel Requirement

47

5. Energy Sources, Conversion Technologies and Siting

48

5.1

Energy Sources

48

5.2

Conversion Technologies

54

5.3

Sites

56

6. Powerplant Design

60

6.1

Objectives for the Design

60

6.2

Non-renewable Source Powerplant

61

6.2.1 Thermodynamic States
6.2.2 Components Selection

63

6.2.3 Fine Tuning

69

6.2.3 Costs Analysis
6.3

62

71

Renewable Source Powerplant

73

6.3.1 Design

74

6.3.2 Wind Turbine Selection

74

6.3.3 Fine Tuning

76

6.3.3 Costs Analysis

78

vi

7. Conclusion and Recommendation
7.1
7.2

Conclusion
Recommendation for future work

References

79
79
80
81

vii

Nomenclature
Symbols
Pi
Pf
r
Yf
Yi
%fwh
ms mcoal CVcoal
WT
WP
QA
ƞ

A
V
h

current population population being projected annual rate of population growth year of projection year based feedwater heater efficiency mass flow of steam mass flow of coal calorific value of bituminous coal work of turbine work of pump heat added powerplant efficiency density of air blade swept area wind velocity hub height

Abbreviations
PPC
WPP
GDP
PECO
DOST
MCIA
NAIA
DOTC
ATC
NFA
GBPC
FBF
PCC
IGCC

Philippine Populations Commission
World Population Prospects
Gross Domestic Product
Panay Electric Corp.
Department of Science and Technology
Mactan-Cebu International Airport
Ninoy Aquino International Airport
Department of Transportation and Communications
Air Traffic Contol
National Food Authority
Global Business Power Corporation
Fuel Bed Firing
Pulverized Coal Combustion
Integrated Gasification Combined Cycle

viii

List of Figures
Figure 1.1
Figure 1.2
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10
Figure 5.1
Figure 6.1

Figure 6.2

Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6

Iloilo province projection using 1.44% population growth rate
Political Map of Iloilo
Households Models (From left to Right) Small,
Medium and Large
Total households projection using 2.07% marriage rate Total machine shops projection using 5.5% growth rate Schools projection using 1.44% population growth rate Offices projection using 5.5% growth domestic product Furniture Shops projection using 5.5% growth domestic product
Restaurants projection using 5.5% growth domestic product Road Data of Iloilo City
Bus lines projection using assuming +1 bus line terminal every 4 years
Panay Railway’s Central Train Station in Passi City,
Iloilo, 1980’s
Earthquake-induced landslide hazard map Iloilo with prospected sites
Schematic Diagram of a Rankine Cycle with One
Closed-Type Feed Water Heater with Drain
Cascading Backward
TS Diagram of a Rankine Cycle with One ClosedType Feed Water Heater with Drain Cascading
Backward
(from left to right) Jutao, Yuanda, and Taiguo boilers
(from left to right) Mitsubishi, SST-5000, and SST700 turbines
(from left to right) Jinan, SPX, and Meluck condensers (from left to right) Yuba, Mazda, and Godrej closedtype feedwater heaters

2
3
10
11
17
19
22
25
27
29
32
33
57
61

62

64
65
66
66

ix

Figure 6.7
Figure 6.8
Figure 6.9
Figure 6.10
Figure 6.11
Figure 6.12
Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Figure 7.1

(from left to right) Griswold, Sulzer and
Rhurpumpen pumps
(from left to right) Germany Crusher, B&W, and CI
Inc. pulverizers
(from left to right) Sunsungs, Sichuan, and Liaoning
FGDs
Prospected Design of the Coal-fired Powerplant in
Iloilo
Payback Period
Average Wind Speed in Iloilo
Technical Specifications of Vestas V112
Wind Turbine Tower Spacing
Wind Farm Final Design
Payback Period
Final Coal-Fired and Wind Powerplants Designs

67

Province Justification
Powerplant Justification
Number of households projected from 1977 to 2045
Small household unit demand (Dry Season)
Small household unit demand (Wet Season)
Medium household unit demand (Dry Season)
Medium household unit demand (Wet Season
Large household unit demand (Dry Season)
Large household unit demand (Wet Season)
Residential Sector Quantification Summary
Machine Shop unit demand
Machine Shop Demand Quantification in 2045
Private School Unit Demand
Public School Unit Demand
Schools Demand Quantification in 2045
Number of offices projected from 2011 to 2045
Office unit demand

4
7
11
12
12
13
14
14
15
16
18
18
20
21
21
22
23

68
69
70
72
73
75
77
77
78
79

List of Tables
Table 1.1
Table 1.2
Table 2.1
Table 2.2
Table 2.3
Table 2.4
Table 2.5
Table 2.6
Table 2.7
Table 2.8
Table 2.9
Table 2.10
Table 2.11
Table 2.12
Table 2.13
Table 2.14
Table 2.15

x

Table 2.16
Table 2.17
Table 2.18
Table 2.19
Table 2.20
Table 2.21
Table 2.22
Table 2.23
Table 2.24
Table 2.25
Table 2.26
Table 2.27
Table 2.28
Table 2.29
Table 2.30
Table 2.31
Table 2.32
Table 2.33
Table 2.34
Table 2.35
Table 2.36
Table 2.37
Table 2.38
Table 2.39
Table 2.40
Table 2.41
Table 2.42
Table 2.43
Table 2.44
Table 2.45
Table 2.46
Table 2.47
Table 2.48
Table 2.49
Table 2.50
Table 2.51

Offices Demand Quantification in 2045
Number of furniture shops projected from 1972 to
2045
Furniture shop unit demand
Furniture Shops Demand Quantification in 2045
20 Number of food establishments projected from
1997 to 2045
Restaurant unit demand
Restaurants/Food Establishments Demand
Quantification in 2045
Commercial Sector Demand Quantification
Summary
Length of Roads for the four districts of Iloilo
Road unit demand
Roads Demand Quantification
Number of bus stations projected from 2011 to 2045
Bus station unit demand
Bus Lines Demand Quantification in 2045
Railway unit demand
Railway Demand Quantification in 2045
Airport unit demand
Airport Demand Quantification in 2045
Seaport unit demand
Seaport Demand Quantification in 2045
Transportation Sector Demand Quantification
Summary
Iron & Steel unit demand
Iron & Steel Demand Quantification in 2045
Food Manufacturing unit demand
Food Manufacturing Demand Quantification in 2045
Industrial Sector Demand Quantification Summary
Livestock unit daily demand
Livestock Demand Quantification in 2045
Fish Culture unit daily demand
Fish Culture Demand Quantification in 2045
Rice Production unit daily demand
Rice Production Demand Quantification in 2045
Corn Production unit daily demand
Corn Production Demand Quantification in 2045
Poultry unit daily demand
Poultry Demand Quantification in 2045

23
24
25
26
26
27
28
28
30
30
31
31
32
33
34
34
35
36
36
37
37
38
38
39
39
40
40
41
41
41
42
43
43
43
44
44

xi

Table 2.52
Table 3.1
Table 3.2
Table 4.1
Table 4.2
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6
Table 5.7
Table 5.8
Table 5.9
Table 5.10
Table 5.11
Table 5.12
Table 5.13
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5
Table 6.6
Table 6.7
Table 6.8
Table 6.9
Table 6.10
Table 6.11
Table 6.12
Table 6.13
Table 6.14
Table 6.15
Table 6.16

Agricultural Sector Demand Quantification
Summary
Demand Summary from the five sectors
Total Demand from the five sectors combined
Existing Powerplants in Iloilo
Plant Capacity needed for 2045
Table 5.1 Energy Sources First Screening
(Efficiency > 35%)
Energy Sources Second Screening (Reliability >
50%)
Energy Sources Second Screening (Availability)
Energy Sources Attributes Ranking
Energy Sources Prioritization
Conversion Technologies First Screening (Efficiency
> 30%)
Conversion Technologies First Screening (Efficiency
> 30%)
Conversion Technologies Attributes Ranking
Conversion Technologies Prioritization
Sites First Screening (Distance from Fault Line > 10 km) Sites Second Screening (Distance from Sea < 3 km)
Sites Attributes Ranking
Sites Prioritization
States in the Coal-Fired Powerplant’s Rankine Cycle
Boiler Selection
Turbine Selection
Condenser Selection
Feedwater Heater Selection
Pump Selection
Pulverizer Selection
Flue Gas Desulfurization Selection
Re-calculated Design Parameters
Final Design Parameters
Capital Costs
Operation and Maintenance Costs
Settlement of Objectives
Wind Turbine Selection
Equipment, Operation and Maintenance Costs
Settlement of Objectives

45
45
46
46
47
50
51
52
53
53
54
55
55
56
58
58
59
59
62
64
65
66
66
67
68
68
70
71
71
72
73
75
78
79

xii

List of Equations
Equation [1]
Equation [2]
Equation [3]
Equation [4]
Equation [5]
Equation [6]
Equation [7]
Equation [8]
Equation [9]
Equation [10]
Equation [11]
Equation [12]
Equation [13]
Equation [14]
Equation [15]
Equation [16]
Equation [17]
Equation [18]
Equation [19]
Equation [20]
Equation [21]
Equation [22]

Population Growth
Daily Consumption
Weekly Consumption
Monthly Consumption
Yearly Consumption
Average Demand
Number of Streetlights
Number of Traffic Lights
Powerplant Capacity
Conversion Factor of MW to kJ/hr
Fuel Requirement
Ranked attributes (for parameters we want to maximize) Ranked attributes (for parameters we want to minimize) Feedwater Heater Efficiency
Power Generated
Mass Flow Rate
Work of Turbine
Work of Pump
Heat Added
Powerplant Efficiency
Wind Power
Wind Velocity

9
9
9
9
9
9
30
30
46
48
48
52
52
63
63
63
63
63
63
63
74
74

1

1.

Introduction

The population in the Philippines is estimated to reach its 100 million mark by the end of 2014 according to the Philippine Populations Commission. Philippine has the 1.38% of the total world population in the present, and it was elaborated by the World Population
Prospects that in 2045, it will be placed 11th in the global rank of countries. Growth of population must always be accompanied by the development of the country’s economy since increasing number of people apparently leads to swelling demands for their needs such as in electricity.
Iloilo Province, known as the heart of the Philippines, located in the Panay Island in Western Visayas region, is selected to be the target for this demand quantification as it is leading in its population, growth rate, agricultural production, average family expenditure, number of business establishments and literacy rate, against its neighbouring provinces, Aklan, Antique, Capiz and Guimaras. Iloilo is known for its delicious cuisines, historical buildings, cultural traditions, captivating landscapes, and exciting festivals as its contributions in the Philippine’s tourism industry. Known for the annual Dinagyang festival and home to many old commercial and institutional buildings, Iloilo serves as a gateway to Western Visayas region as a stopover for tourists heading to the beaches of
Boracay and nearby coasts. These occasions result to crowding the absolute population of the province, increasing the demand from the people.

2

Population Projection in Ilolo Province
4000000
3500000
3000000
2500000
2000000
1500000
1000000

500000
0
1980

1990

2000

2010

2020

2030

2040

2050

Figure 1.1 Iloilo province projection using 1.44% population growth rate

Thirty years from now, the number of people residing in Iloilo, as projected, will approximately be 3,678,462 from the most recent 2010 census data of 2,230,195 with 1.44 growth rate. Iloilo province recently depends on six powerplants from which five are expected be decommissioned by the year 2045.

1.1

Location Map

Iloilo is the largest in terms of size in the Panay Island. It has a total land area of
5,079.17 km2 which ranked 23rd out of 81 provinces in the Philippines.

3

Figure 1.2 Political Map of Iloilo

The province constitutes the 42% of the Panay Island. It is subdivided into 42 municipalities, one component Passi City, and one highly urbanized Iloilo City. Iloilo City is independent from the Iloilo Province but remains the capital of it. It has a population of
419,611 (2010 Census), land area of 78.34 km2 and noted to have income class of 1st class being the one highly urbanized city in the province.

4

1.2

Justification

The Ilonggos’ good political will and cooperation allows the economy of Iloilo to boom and provide thousands of jobs for its people that makes the province one of the most competitive in the country. Improving Iloilo to catch up with the demands in the future already gives a response to the enquiry about the need to build a power generation plant.
Justifications for the province and powerplant are presented in Sections 1.2.1 and 1.2.2.

1.2.1

Province Justification
Iloilo was chosen to be the featured province in the study because of its prominence

among the provinces in Western Visayas region which quantified in terms of the six parameters on Table 1.1.
Table 1.1 Province Justification
Aklan

Population
Growth Rate
Agricultural
Production (Palay in metric tons)
Average Family
Expenditure
No. of Business
Establishments’
Registration
Literacy Rate

495,122
1.29
103,625

Antique
515,265
1.19
211, 466

Capiz
701,664
0.97
335,608

Guimaras
151,238
0.93
42,716

Ilo-Ilo
1.691 M
1.44
659,970

89,858

83,246

86,212

85,522

91,900

1462

868

1092

325

3829

92.5

91.78

92.04

94.97

93.79

5

Iloilo’s high number of population and growth rate yielded its dominance in agricultural production where it produces an average of 659,970 metric tons of palay. It is considered to have a middle class society for it has the biggest average family expenditure among the five provinces in Panay, meaning to say that the residents of Iloilo expend a lot of money for their personal consumptions. The number of business establishments’ registration only shows that many investors want to put their money in Iloilo since most the tourists pass through Iloilo for their trips in several tourist attractions. The literacy rate of 93.79% was also at the highest next to Guimaras Province.

1.2.2

Powerplant Justification
Comparing the powerplant with different projects justifies the need to build a

powerplant in Iloilo. There can be a net income of 5.08 billion pesos from the construction of a powerplant and associating it with Shopping Mall, Mining, and Hotel and Restaurant, powerplant has the highest benefit-cost ratio which means that it gives greater benefits relative to the costs of building and facilitating the project.
Powerplants also get ahead in having the fastest payback period of six years, and having the least number of competitors which is currently six (Panay DPP1, Panay DPP3,
Power Barge 103, Panay Power Corp, PECO, and GBPC) from which five (all except
GBPC) are anticipated be decommissioned by the year 2045.
They have an expected life of 35 years and since it produces the basic need for electrical power, it has a demand of 98% of the population.

6

Table 1.2 Powerplant Justification

Php 5.08
Billion
3.98

Shopping
Mall
Php 4.9
Billion
2.87

Php 2.1
Billion
3.54

Hotel and
Restaurant
Php 1.2
Billion
2.56

6 years

8 years

8 years

7 years

6 currently, and 1 in 2045
35 years
98 %

16 currently, and 25 in 2045
25 years
85 %

23 currently, and 63 in 2045
8 years
50 %

178 currently
& 887 in 2045
20 years
30 %

Powerplant
Net Income
Benefit-Cost
Ratio
Payback
period
Number of competitors Expected Life
Demand

1.3

Mining

Statement of the Problem

The general problem of the study is the task to quantify the demand for fuel requirements of a particular powerplant to be built in Iloilo 30 years from now.
Specifically, this study pursues quantitative data analyses in deciding a powerplant in the year 2045 by following these guides:
1. Justification of the province selected and the powerplant to be built using different parameters. 2. Electricity consumption calculation from the residential sector using three house models (small ,medium, and large households)

7

3. Electricity consumption calculation from the commercial sector using five models
(machine shops, schools, offices, furniture shops, and restaurant/food establishments.
4. Electricity consumption calculation from the transportation sector using five models
(roads, bus stations, railways, airports, and seaports)
5. Electricity consumption calculation from the industrial sector using two models: (iron and steel, and food manufacturing)
6. Electricity consumption calculation from the agricultural sector using five models
(livestock, fish culture, rice production, corn, and poultry); and
7. Total electricity demand computation on daily, weekly and yearly bases, translated to fuel requirements considering the existing, upcoming, and decommissioning powerplants in 2045.

1.4

Significance of the Study

The study will primarily guarantee to the industries, businesses, and residences of
Iloilo that there will be a valid detailed quantification of the projected future demands to be able to help the people build a justified powerplant in order to answer these claims.

8

1.5

Objectives of the Study

The general purpose of the study is to generate a quantified data of the demands on electrical energy in the year 2045 and to be able to construct a powerplant that will response to the increasing number of population.
Specifically, the researcher would like to achieve the following objectives:
1. To project the overall population of Iloilo in 2045 using different models of the five sectors (residential, commercial, transportation, industrial, and agriculture).
2. To calculate these demands based on the assumptions of electricity usages from different sectors and models.
3. To decide what type of powerplant is best to carry these loads.

9

2.

Demand Quantification
To be able to obtain the average demand in 2045, the following equations were

used for all the sectors included in the study. Daily, weekly, monthly and yearly consumptions depend on the assumptions set on each sector, the goal is to determine the average demands that will be totalled later for the overall demand quantification.
Population Growth = (1 + ) −

[1]

Daily Consumption (kWh)
( ) ( ) ( ) ( )
1000

[2]

Weekly Consumption (kWh)
( 5) + ( 2) [3]
Monthly Consumption (kWh) 4.286

[4]

Yearly Consumption (kWh)
(ℎ ℎ )

[5]

Average Demand (MW) (365 24 1000)

2.1

[6]

Residential Sector
Number of households were first projected to obtain the residential sector

electricity consumption demand in 2045. The researcher used the marriage rate of 2.07% since it is safe to assume that household population is proportional to the number of

10

marriages in the province, for people will merely decide to settle down only when they realized to form a family.
Using the growth equation from Equation [1], it can be predicted from census data that in 2045, there will be an approximate of 751,240 total households residing in Iloilo
Province. With the three models as small, medium, and large houses based on the size of land owned by the family, it was assumed that 60% of the total belong to the small house models, 30% to the medium and the remaining 10% to the large.

Figure 2.1 Households Models (From left to Right) Small, Medium and Large
Households were grouped into three models and those who have less than 30m2 land areas are considered to be part of the Small Households as samples are shown in Figure
1.3. Medium ranges from 30m2 to 90m2, while above 90m2 land area are requisite to be considered as Large.
It was estimated, from calculations, to have totals of 450,744 small households,
225,372 medium households, and 75,124 large households in the year 2045. Projection for the overall household population is presented in Figure 2.2 and Table 2.1.

11

Table 2.1 Number of households projected from 1977 to 2045
Year
1977
1982
1987
1992
1997
2002
2007
2011
2015
2019
2023
2027
2031
2035
2039
2043
2045

Number of Households
186512
206632
228922
253617
280976
311286
344866
374320
406289
440989
478652
519532
563904
612065
664339
721078
751240

Number of Households

800000
700000
600000
500000
400000
300000
200000

100000
0
1960

1970

1980

1990

2000

2010

2020

2030

2040

Figure 2.2 Total households projection using 2.07% marriage rate

2050

12

Assumptions for the residential demands are that there will be no changes in the household devices of specific models for the next 30 years, wet and dry season of 6 months for each will be considered, and weekdays and weekends will be treated differently. The demand quantification for small, medium and large households are tabulated in Tables 2.2 to 2.7 considering wet (December to May) and dry (June to November) seasons.
Table 2.2 Small household unit demand (Dry Season)

Appliances

25
80
50

3
1
2

1
0.8
0.9

Hours
Used
WD
4
3
5

280

1

0.75

0.5

45

1

0.75

1

130

1

0.6

24

Rating(W) Quantity

Lights
Television
Electric Fan
Washing
Machine
DVD Player
Refrigerator
(8cu.ft.)

Usage
Factor

Hours
Used
WE

kWh
WD

kWh
WE

4
5
10

0.3
0.192
0.45

0.3
0.32
0.9

1

0.105

0.21

4 0.03375

0.135

24

1.872

1.872

TOTAL

2.95275

3.737

Table 2.3 Small household unit demand (Wet Season)
Appliances
Lights
Television
Electric Fan
Washing
Machine
DVD Player
Refrigerator
(8cu.ft.)

Hours
Hours
Used WD Used WE
4
4
3
5
2
0.5

kWh WD

kWh WE

0.3
0.192
0.18

0.3
0.32
0.045

0.5

1

0.105

0.21

1

4

0.03375

0.135

24

24

1.872

1.872

2.68275

2.882

TOTAL

13

Small household models are presumed to have appliances of lights, television, electric fan, washing machine, DVD player, and 8ft3 refrigerator. Number of appliances, usage factors, and hours used during weekdays and weekends were estimated to compute for the consumptions in kWh. Same process on medium and large households but with different appliances and parameter values.
Table 2.4 Medium household unit demand (Dry Season)

Appliance
Lights
Television
Electric Fan
Air
Conditioner
Flat Iron
Washing
Machine
DVD
Player
Refrigerator
(11cu.ft.)

Rating(W) Quantity

Usage
Factor

Hours
Used
WD

Hours
Used
WE

kWh
WD

25
110
50

4
1
2

1
0.8
0.9

5
5
6

5
7
10

944

1

0.6

4

6

2.2656 3.3984

600

1

0.75

0.25

1

0.1125

0.45

280

1

0.75

0.5

2

0.105

0.42

45

1

0.75

1

4 0.03375

0.135

170

1

0.6

24

24

0.5
0.44
0.54

kWh
WE

2.448

0.5
0.616
0.9

2.448

TOTAL 6.44485 8.8674

In medium households, air conditioner and flat iron were added to the appliances and refrigerator was increased to 11ft3 size with higher rating.

14

Table 2.5 Medium household unit demand (Wet Season)
Hours
Used
WD

kWh WD

5
5
3

5
7
3

0.5
0.44
0.27

0.5
0.616
0.27

2

2

1.1328

1.1328

0.5

1

0.225

0.45

1

4

0.21

0.84

0.5

2

0.016875

0.0675

24

Lights
Television
Electric Fan
Air
Conditioner
Flat Iron
Washing
Machine
DVD Player
Refrigerator
(11cu.ft.)

Hours
Used WE

24

2.448

2.448

TOTAL

Appliance

kWh
WE

5.242675

6.3243

Table 2.6 Large household unit demand (Dry Season)

Appliance
Lights
Television
Electric Fan
Air
Conditioner
Flat Iron
Washing
Machine
DVD Player
Refrigerator
(14cu.ft.)
Rice Cooker
Microwave
Oven

Rating
(W)

Quantity

Usage
Factor

25
210
50

8
2
3

1
0.8
0.9

Hours
Used
WD
6
6
6

944

2

0.6

600

1

280

Hours
Used
WE

kWh
WD

kWh
WE

6
9
10

1.2
2.016
0.81

1.2
3.024
1.35

4

6

4.5312

6.7968

0.75

0.25

2

0.1125

0.9

1

0.75

0.5

4

0.105

0.84

45

2

0.75

1

4

0.0675

0.27

215

1

0.6

24

24

3.096

3.096

450

1

1

0.5

0.5

0.225

0.225

1000

1

0.75

0.05

0.1

0.0375

0.075

TOTAL

12.2007 17.7768

15

Table 2.7 Large household unit demand (Wet Season)

Appliance

Hours
Used
WD

Hours
Used WE

kWh WD

kWh WE

Lights
Television
Electric Fan

6
6
3

6
9
4

1.2
2.016
0.405

1.2
3.024
0.54

Air Conditioner

2

2

2.2656

2.2656

0.5

2

0.225

0.9

0.5

4

0.105

0.84

1

4

0.0675

0.27

24

24

3.096

3.096

0.5

0.5

0.225

0.225

0.1

0.1

0.075

0.075

TOTAL

9.6801

12.4356

Flat Iron
Washing
Machine
DVD Player
Refrigerator
(14cu.ft.)
Rice Cooker
Microwave
Oven

Large households’ appliances were added by rice cooker, microwave oven, and air conditioner size was increased to 14 ft3. The tabulated data show that electric consumptions during weekdays are much lower than during weekends, and dry seasons usually have higher consumptions than wet. This is due to the assumptions that many people stay at home during Saturdays and Sundays when generally there are no classes or work, and that appliances are much used during dry season especially aircons and electric fans when the weather is hot.
Summary of computations for residential sector are arranged in Table 2.8 where average demand of 151.02 MW is obtained for the three models by 2045

16

Table 2.8 Residential Sector Quantification Summary

Daily (Weekday) (kWh)
Daily (Weekend) (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.2

DRY
3699988.783
5018358.753
28536661.42
122308130.8

WET
3117993.715
3658576.674
22907121.92
98179924.56
1322928332
151.019216

Commercial Sector
Machine shops, schools, offices, furniture shops, and food establishments were

treated as the models for the commercial sector demand quantification. All models are projected using the growth domestic product since it is expected from commercial sectors to have balance with the nation’s total economic activity. GDP represents the monetary value of all goods and services produced within the nation’s geographic borders so increasing this value can lead to the rise of commercial establishments in different places.
In 2008, it was released by NSCB that Western Visayas is the fourth largest contributor to the country’s GDP among the 17 regions.

2.2.1

Machine Shops
Machines Shops are good to be modelled in electricity demand quantifications like

this because they use high rating equipment on their operations. Assumed appliances

17

include lights, welding machine, lathe machine, sharper, milling machine, drill press, and grinder. As of 2011, there are records of 28 machine shops in Iloilo. It was assumed that machine shops are open 8 hours for weekdays, 6 hours on Saturdays and closed during
Sundays. Table 2.9 and Figure 2.3 show the projection of machine shops in Iloilo for the next 30 years.

Number of Machine Shops
200
180
160
140
120
100
80

60
40
20
0
1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Figure 2.3 Total machine shops projection using 5.5% growth rate

Calculations yielded a total of 184 machine shops in Iloilo by 2045. This large number is acceptable for it defends the upgrades of particular machine shops in the future.
That is when one machine shop this 2015 can actually act like three in 2045 when it expands its equipment and facilities.

18

Table 2.9 Machine Shop unit demand

Appliance
Lights
Welding
Machine
Lathe
Machine
Shaper
Milling
Machine
Drill Press
Grinder

Rating
(W)

Hours
Used
WD

Hours
Used
WE

Quantity

Usage
Factor

kWh
WD

kWh
WE

40

10

1

8

6

3.2

2.4

10000

1

0.8

3

1

24

8

7500

2

0.6

3

1

27

9

7500

1

0.3

2

1

4.5

2.25

7500

2

0.5

2

1

15

7.5

1000
1000

1
1

0.7
0.3

2
2

1
1
TOTAL

1.4
0.6
75.7

0.7
0.3
30.15

Table 2.10 Machine Shop Demand Quantification in 2045
Daily (Weekday) (kWh)
Daily (Weekend) (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

13896.34499
5534.67373
75016.39867
321520.2847
3858243.416
0.440438746

Machine shops were predicted to demand a nearly average of 0.44 MW from Iloilo in the next 30 years.

19

2.2.2

Schools
There is a total of 1108 schools (private, public, primary, secondary and college)

from the data of 1997 in Iloilo. Schools don’t increase in numbers rapidly but they expand easily as the years go by, similar to machine shops, the number of schools predicted supports the expansions such that in 2045, different equipment in schools are also expected to advance. Figure 2.4 shows the estimated equivalent demand from the schools in the year
2045, projected with 1.44% population growth rate since the expansion of schools is relative to the increasing number of students in the province.

Estimated Number of Schools
2500

2000

1500

1000

500

0
1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Figure 2.4 Schools projection using 1.44% population growth rate
Assumptions are there no classes during weekends, an estimate of 35 rooms per school (30 for students and 5 for faculty), and academic year has 10 months from June to
March.

20

Private and public schools were quantified differently where private schools have appliance of lights, aircons, LCD Projectors, computers, laboratory equipment, and refrigerators Table 2.11 Private School Unit Demand
Hours
Used
WD

Rating
(W)

Quantit y Usage
Factor

40

4

1

8

Aircon
LCD
Projector
Computer

944

2

0.75

160

1

225

Laboratory

1000

CLASSROOM

Lights

Hours
Used WE

kWh
WE

1.28

0

0

8

11.328

0

0

1

2

0.32

0

0

1

1

2

0.45

0

0

2

0.75

19.378

6

2.56

TOTAL
30 Class rooms 6

0

4

Appliances

kWh
WD

1.92

Lights

40

8

1

Aircon

944

2

0.75

8

11.328

6

8.496

Refrigerator

120

1

0.75

24

2.16

24

2.16

Computer

225

3

1

4

2.7

4

TOTAL

18.748

TOTAL

2.7
15.27
6

5 Faculty rooms FACULTY

TOTAL
30 Class rooms 8

93.74

5 Faculty rooms 581.34

0

76.38

Table 2.11 shows zero consumptions in classrooms during weekends, however there are records of consumptions, only lesser, in faculty rooms. This is due to the assumptions that professors or maintenance people still work in weekends or the fact that refrigerators are never shut.

21

Table 2.12 Public School Unit Demand
Appliance

4

Usage
Factor
0.75

Hours
Used WD
8

kWh
WD
1.32

Hours
Used WE
0

kWh
WE
0

40

4

1

8

1.28

0

0

Computer

225

1

1

2

0.45

0

0

Laboratory

1000

1

0.75

TOTAL

6.05

TOTAL

0

4

CLASSROOM

3

181.5

0

2.56

30 Class rooms 6

1.92

Electric Fan
Lights

Rating
(W)
55

Quantity

Lights

40

8

1

Electric Fan

55

8

0.75

8

2.64

6

1.98

Refrigerator

120

1

0.75

24

2.16

24

2.16

Computer

225

2

1

4

1.8

4

1.8

TOTAL

9.16

TOTAL

7.86

5 Faculty rooms FACULTY

30 Class rooms 8

45.8

5 Faculty rooms 39.3

Figure 2.12 has similar assumptions to private schools during weekends. Public schools are only assumed to have just electric fans, lights, computers, laboratory equipment and refrigerator in small quantities.
Combining public and private schools electricity demand for the year 2045 will result to an average of 15.4 MW, tabulated in Table 2.13.
Table 2.13 Schools Demand Quantification in 2045
Daily (Weekday) (kWh)
Daily (Weekend) (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

592640.0888
94132.57352
3151465.591
13507181.52
135071815.2
15.41915699

22

2.2.3

Offices
From the records of 1149 offices in 2011, it was predicted to have a total of 7094

offices in 2045. Calculations are limited to the assumptions of 24/7 functioning security cameras and operating hours of 8 hours every weekdays only. The projection used the 5.5% gross domestic product and was presented in Figure 2.5.

Number of Offices
8000
7000
6000
5000
4000
3000
2000
1000
0
2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Figure 2.5 Offices projection using 5.5% growth domestic product
Table 2.14 Number of offices projected from 2011 to 2045
Year
2011
2015
2019
2023
2027
2031
2035
2039
2043
2045

Number of Offices
1149
1423
1763
2184
2706
3353
4153
5145
6374
7094

23

Table 2.15 Office unit demand
Appliance

6

Usage
Factor
0.7

Hours
Used WD
8

kWh
WD
31.7

Hours
Used WE
0

kWh
WE
0

30
10
10

1
0.7
1

8
8
24

7.2
22.4
16.8

0
0
24

0
0
16.8

TOTAL

Air
Conditioner
Lights
Computers
CCTV Camera and Monitor

Rating
(W)
944

Quantity

30
400
70

78.1

TOTAL

16.8

Typical office appliances such as, aircons, lights, computers and cctv cameras and monitors were used and it showed up to have an average demand of 17.66 MW in 2045.
Table 2.16 Offices Demand Quantification in 2045
Daily (Weekday) (kWh) 554057.3112
Daily (Weekend) (kWh) 119182.62265
Weekly (kWh)
3008651.801
Monthly (kWh)
12895081.62
Yearly (kWh)
154740979.5
Average Demand (MW) 17.66449537

2.2.4

Furniture Shops
Furniture shops are used as models since Iloilo province is known for its grand

vintage stuff and antiques matched with its historical buildings and Spanish-colonial infrastructures. 24

Shops are assumed to open 8 hours per day and still operate on weekends. From 49 noted furniture shops in 2011, it was predicted to have a total of 303 shops in 2045 using the 5.5% GDP.
Table 2.17 Number of furniture shops projected from 1972 to 2045
Year
1972
1977
1982
1987
1992
1997
2002
2007
2011
2015
2019
2023
2027
2031
2035
2039
2043
2045

Furniture Shop
5
6
9
11
15
20
27
37
49
61
75
93
115
143
177
219
272
303

Similar to machine shops, schools and offices, this large number can also allot for the expansions of furniture shops in the future. Projections are shown in the graph of Figure
2.6.

25

Furniture Shops
350
300
250
200
150
100
50
0
1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Figure 2.6 Furniture Shops projection using 5.5% growth domestic product

Appliances in furniture shops include electric fans, lights, electric saw, power drills, sanders, and grinder. Quantified using their ratings, the average total demand in 2045 is expected to be 0.18 MW.
Table 2.18 Furniture shop unit demand
Appliance
Electric
Fan
Lights
Electric
Saw
Power
Drill
Sander
Grinder

Rating
(W)

Quantity

Usage
Factor

Hours
Used WD

kWh
WD

Hours
Used WE

kWh
WE

55

4

0.75

8

1.32

6

0.99

40

6

1

8

1.92

6

1.44

1600

1

0.7

4

4.48

2

2.24

750

2

0.6

4

3.6

2

1.8

650
650

2
1

0.6
1

4
4
TOTAL

3.12
2.6
17.04

2
2
TOTAL

1.56
1.3
9.33

26

Table 2.19 Furniture Shops Demand Quantification in 2045
Daily (Weekday) (kWh)
Daily (Weekend) (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.2.5

5155.244861
2822.67808
31421.58047
134672.8939
1616074.727
0.184483416

Restaurants and Food Establishments
Restaurants are booming in Iloilo province because of its brand for delicious

cuisines. There is a total of 304 restaurants from the Iloilo government’s annual report in
2011 and calculations were done by assuming that establishments are open every day for
12 hours.
Table 2.20 Number of food establishments projected from 1997 to 2045
Year
1997
2002
2007
2011
2015
2019
2023
2027
2031
2035
2039
2043
2045

Restaurants
127
170
227
304
377
467
578
716
887
1099
1361
1686
1877

27

Restaurants
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Figure 2.7 Restaurants projection using 5.5% growth domestic product

Table 2.21 Restaurant unit demand
Appliance
Rating (W) Quantity Usage Factor Hours Used kWh Computer
180
4
0.8
12
6.912
Air Conditioner
500
10
1
12
60
Fluorescent
20
20
1
12
4.8
Refrigerator
540
4
1
24
51.84
Microwave Oven
1000
2
0.8
12
19.2
Water Dispenser
500
5
0.7
6
10.5
TOTAL
153.252

Appliances include computers, air conditioners, fluorescents, refrigerators, microwave ovens and water dispensers. Assumptions of number quantities and usage factors per unit model are used to compute the consumption based to the ratings.
Table 2.22 shows the demand from restaurants/food establishments projected in
2045.

28

Table 2.22 Restaurants/Food Establishments Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

287649.3269
2013545.288
8630055.105
103560661.3
11.8219933

Table 2.23 Commercial Sector Demand Quantification Summary
Daily (Weekday) (kWh)
1165748.98984
Daily (Weekend) (kWh)
221672.54798
Weekly (kWh)
6266555.37141
Monthly (kWh)
26858456.32188
Yearly (kWh)
295287112.81784
Average Demand (MW)
33.70857

2.3

Transportation Sector
Transportation sector demand quantification focused on roads, bus stations,

railways, airports, and seaports in Iloilo as its models. Electricity use in transportation is small compared with the other sectors but since electrified vehicles now like electric trains are prospected to be seen in Iloilo, and great improvements on airports are promised in the near future, it is wise to include the transportation sector in Demand Quantification of the province. 29

2.3.1

Roads
Looking at Figure 2.8 and Table 2.24, the length of roads (primary and secondary)

in meters are noted for the four districts including the capital Iloilo City. There are totals of 344.54 km (38%) primary roads and 562.46 km (62%) secondary roads for the whole province of Iloilo from which street and traffic lights are installed.

Figure 2.8 Road Data of Iloilo City
Similar figures for Iloilo’s 1st to 4th districts are also collected to determine the length of roads in the whole province. These data were tabulated and thus the total length were obtained. It was also reported that 16 city roads will be upgraded to national roads.

30

Table 2.24 Length of Roads for the four districts of Iloilo
Iloilo

Primary Roads Secondary Roads
63.15
101.29
120.26
240.41
126.15
57.24
18.12
114.78
16.86
48.74
344.54
562.46
38%
62%

1st
2nd
3rd
4th
Iloilo City
TOTAL
%

Assumptions are 50 meter distance intervals of streetlights in primary roads and 75 meters in secondary roads. There is one traffic light for every 10 kilometres and these traffic lights are open 24/7 while streetlights are open 12 hours a day from 6PM to 6AM.
Calculating the electricity consumption in Table 2.25 will yield a value that will be multiplied to the number of streetlights and traffic lights obtained from Equations [7] and
[8].
Table 2.25 Road unit demand
Usage
Traffic Lights
Street Lights

0.75
0.75

Rating
100
80

Number of Streetlights +

75


50

Hours Used
24
12



kWh Used
1.8
0.72

[7]

Number of Traffic Lights ℎ
10

[8]

Road average demand is estimated to be 0.43MW as presented in the next table.

31

Table 2.26 Roads Demand Quantification
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.3.2

10524.252
73669.764
315748.6085
3788983.302
0.43253234

Bus Stations
Buses are the most used transportation means in Iloilo when travelling in the nearby

provinces of the Panay Island. The bus station model used in this study is assumed to be operational 24 hours. There are 11 bus stations in 2011 and it is assumed that there will be an additional of 1 more terminal every 4 years. The projections are shown in Table 2.27 and Figure 2.9
Table 2.27 Number of bus stations projected from 2011 to 2045
Year
2011
2015
2019
2023
2027
2031
2035
2039
2043
2045

Bus Lines
11
12
13
14
15
16
17
18
19
20

There will be 20 predicted bus stations by 2045, these are quantified in Table 2.28 using different assumed equipment found in usual bus terminals.

32

Bus Lines
25
20
15
10
5
0
2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Figure 2.9 Bus lines projection using assuming +1 bus line terminal every 4 years

Table 2.28 Bus station unit demand
Appliance
Rating (W) Quantity Usage Factor Hours Used
Lights
30
30
1
18
Ceiling Fan
140
8
0.6
16
TV
210
4
0.7
16
Air Conditioner
944
2
0.6
12
Fuel Pump
750
1
0.1
24
Air Compressor
1500
1
0.1
24
Kiosks (5)
Deep Fryer
1500
1
0.5
12
Blender
300
1
0.6
12
Chiller
120
1
0.6
12

kWh
16.2
10.752
9.408
13.5936
1.8
3.6

9
2.16
0.864
12.024
TOTAL Kiosk
60.12
TOTAL Station 115.474

The model was assumed to have kiosks typically found in most bus stations that use deep fryers, blenders and chillers. Lights, ceiling fans, TVs, aircons, fuel pump, and

33

air compressor are the equipment contributing to the demand for one unit of the model. It can be calculated in 2045 that the average electricity demand from bus stations is 0.14 MW based on Table 2.29
Table 2.29 Bus Lines Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.3.3

3511.872
24583.104
105363.1837
1264358.205
0.144333128

Railways

Figure 2.10 Panay Railway’s Central Train Station in Passi City, Iloilo, 1980’s

There was a Panay line in Iloilo back in the 1900’s that connects Iloilo City to
Roxas City in Capiz. But in 1989, its operations ceased, and now many claims say that

34

there are plans to rebuild the line. Many disagree with this decision because of the uneconomical cost of 16 Billion peso railway project to rehabilitate the railway system.
In this paper, instead of restoring the Panay Railway, it was assumed that an electronic train station will be built in Iloilo in the future. These electronic trains were supposed to be similar to the DOST train projects in Metro Manila.
Calculating for the demand of an electronic train station from Table 2.30 with appliances of lights, ticket dispensing machines, air conditioners, ceiling fans, and electric trains, an average demand of 5.32 MW will be obtained.
Table 2.30 Railway unit demand
Appliance

Usage
Factor

Hours
Used

Rating (W)

Lights
Ticket Dispensing
Machines
Air Conditioner
Ceiling Fan
Electric Train

Quantity

30

20

1

16

9.6

80

8

1

16

10.24

944
140
2000000

2
8
10

0.6
0.6
0.4

16
16
16
TOTAL

18.1248
10.752
128000
128048.717

Table 2.31 Railway Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

128048.7168
896341.0176
3841461.504
46609732.92
5.320745767

kWh

35

2.3.4

Airports
The Iloilo International Airport has a strategic location in the central part of the

country which grows a number of direct international and domestic flights making it an excellent alternative to the crowded Mactan International Airport in Cebu and NAIA in
Manila.
There is a 790 Million peso from the DOTC for the expansion of the Iloilo
International Airport as reported on May 2015.The expansion project proposes to overtake
NAIA’s facilities and make Iloilo International Airport the most striking airport in the country. In 2045, it was assumed that Iloilo International Airport will already be improved as the anticipated appliances in normal airport will be considered. Baggage monitoring devices, lights, air conditioners, runway lights, computers, x-ray machines, and ATC radars compose the equipment in the airport.
Table 2.32 Airport unit demand
Appliance
Baggage Monitoring
Device
Lights
Air Conditioner
Runway Lights
Computers
X-Ray Machines
ATC Radars

Rating
(W)

Quantity

Usage
Factor

15000

3

0.9

30
944
100
225
16200
400000

40
6
150
6
3
1

1
0.6
1
0.6
0.9
0.9

Hours
Used

kWh
24

972

24
28.8
10 33.984
14
210
24
19.44
24 1049.76
24
8640
TOTAL
10954

36

Table 2.33 Airport Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.3.5

21907.96
153355.72
657238.8
7974497.44
0.91

Seaports
Seaports are the main course in hopping through the different islands inVisayang

such as in Guimaras or Negros. Iloilo presently has four seaports: the Iloilo-Guimaras Jetty
Ports, Muelle Loney Iloilo River Wharf, the Iloilo Domestic Port, and the Iloilo
Commercial Port Complex. It was assumed Iloilo Commercial Port Complex will be expanded in 2026.
Appliances in seaports include lights, cranes, air conditioners, television sets, computers, light house, radio communication devices, and refrigerators.
Table 2.34 Seaport unit demand
Appliance
Lights
Crane
Air Conditioner
Television
Computer
Lighthouse
Radio Communication
Device
Refrigerator

Rating Quality

Usage
Factor

Hours
Used

kWh

30
10000
350
15
100
8000

30
4
10
5
10
1

1
0.6
0.8
0.8
1
1

12
12
12
24
24
12

10.8
288
33.6
1.44
24
96

2500

3

1

24

180

75

2

0.8

24
2.88
TOTAL 636.72

37

It was projected that there will be an average demand of 0.11 MW from seaports of
Iloilo in the year 2045
Table 2.35 Seaport Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2546.88
18038.16
77306.4
937984.32
0.11

Table 2.36 Transportation Sector Demand Quantification Summary
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.4

163027.8088
1141404.662
4891755.313
59311197.98
6.773278108

Industrial Sector
Increasing demand growth is mainly due to the increasing production factors that

increase the electricity rates. In Iloilo, industries like plants are not actually wanted because it somehow preserves its sceneries for tourism. Only Iron & Steel and Food
Manufacturing are considered in the models of Industrial Sector.

38

2.4.1

Iron & Steel
Currently, there are two plants of Iron & Steel in Iloilo, one is from the known

Colorsteel Systems Corporation in Pavia, which provides roofing for houses. It was assumed that there will be an addition of one new plant for every 10 years having a total of 4 plants in the early 2045.
Table 2.37 Iron & Steel unit demand
Appliance
Electric Arc Furnace
Crane
Bag house Fan
Continuous Caster

Rating
(W)
21363800
540000
1300000
13200

Quantity
1
1
2
2

Usage
Factor
0.3
0.3
0.3
0.9

Hours
Used

kWh

24 153.819
24
3.888
24
18.72
24 0.57024
TOTAL
176.998

Appliances include electric arc furnace, crane, bag house fans, and continuous casters. It was totalled to have an average demand of 29.5 MW in 2045.

Table 2.38 Iron & Steel Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

707990.4
4955933
21239712
2.58E+08
29.5

39

2.4.2

Food Manufacturing
Since Iloilo province is well-known for its delicacies and pasalubongs, many of

these (like piayas, biscochos, baye-baye, barquillios, pancit molo, etc) are prepared and based also in Iloilo. Currently, there are 36 food manufacturers in the province that are assumed to operate every day. The numbers are projected with the use of Industrial Growth
Rate of 4.5% to have a total of 161 in 2045.
Food processing equipment have low ratings since they are likely to create homemade products only. These comprise of electric fans, lights, food processors, refrigerators and heat sealers. Demands are tabulated in Table 2.39 and 2.40
Table 2.39 Food Manufacturing unit demand
Appliance
Rating (W) Quantity Usage Factor Hours Used kWh
Electric Fan
50
6
0.7
12
2.52
Lights
40
18
1
12
8.64
Food Processor
700
4
0.7
6
11.76
Refrigerator
200
2
0.6
24
5.76
Heat Sealer
250
4
0.7
3
2.1
TOTAL
30.78

Table 2.40 Food Manufacturing Demand Quantification in 2045
Daily (kWh)
4955.58
Weekly (kWh)
34689.06
Monthly (kWh)
148677.3
Yearly (kWh)
1784128
Average Demand (MW) 0.203668

Total demands for Industrial Sector are tabulate in Table 2.41

40

Table 2.41 Industrial Sector Demand Quantification Summary
Daily (kWh)
712945.98
Weekly (kWh)
4990621.86
Monthly (kWh)
21388389.3
Yearly (kWh)
259492633
Average Demand (MW) 29.7036675

2.5

Agricultural Sector
Rural agricultural areas of Iloilo constantly produce vegetables, fruits, spices,

poltry, livestock and fishes. The National Food Authority, who oversees the sufficiency of supply, says that Iloilo production has been quite abundant for years. It is known as the
“Food Basket in the Philippines” as every staple food or its ingredients you can find in
Iloilo.
Five models were modeled in this sector, mainly livestock, fish culture, rice production, corn, and poultry.

2.5.1

Livestock: Cattle
It was assumed that livestock operations will be 24/7, having 1 light bulb for every

5 cattle and 1 milking machine for every 200. It was expected that there will be a total of
600 cattle accommodations in 2045. Demand was projected to be 3.75 MW in 2045.
Table 2.42 Livestock unit daily demand
Appliance
Rating (W)
Lights
60
Milking Machine
190

Quantity Usage Factor Hours Used kWh
120
1
12
0.7
3
0.2
24
2.7
TOTAL
3.456

41

Table 2.43 Livestock Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.5.2

91219.59
638537.1
2736770
32841240
3.749

Fish Culture
Fish pond models used in the study use circulating pumps and aerators. It was noted

to have 12120 MT of fishes in 2011. Assuming 1 pond for every 100 MT, following growth rate for agricultural sector of 4.1%, average demand can be projected to be 18.39 MW in
2045.
Table 2.44 Fish Culture unit daily demand
Appliance
Aerator
Circulating Pump

Rating
(W)
540
190

Usage
Factor
1
0.2

Hours Used

kWh

24
24
TOTAL

12.96
0.92
13.88

Table 2.45 Fish Culture Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

447460
3132221.2
13424700
161096400
18.39

42

2.5.3

Rice Production
Iloilo is historically the chief producer of rice because of the favourable geographic

and climatic factors in the province. According to Regional and Provincial Agriculture office, NFA declares that Iloilo is the top second producer of rice in the whole country.
There are about 194.4 thousand hectares devoted to palay in 1995 which yielded a harvest of 553.5 thousand metric tons. Korean government actually issued 11 Billion to Iloilo for rehabilitation of the Jalaur River Irrigation System by constructing a damn and hydroelectric powerplant.
There is an average of 864,112 metric tons of palay harvested in 2008. Assuming
1 rice mill for a thousand metric tons, and by using appliance like hullers, lights, dryers and rice graders in production, it can be projected to have an average of 1.78 MW demand in 2045.
Table 2.46 Rice Production unit daily demand
Appliance
Rating (W)
Rice Mill
1200
Huller
800
Lights
40
Dryer
400
Rice Grader
1100

Quantity Usage Factor Hours Used kWh
2
0.7
10
16.8
1
0.5
10
4
10
1
10
4
1
0.5
10
2
2
0.5
10
11
TOTAL
37.8

43

Table 2.47 Rice Production Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2.5.4

4474
313222
134247
1610960
1.78

Corn
Corn harvesting in Iloilo covered 5,200 hectares. Iloilo contributes 66% of corn

production in the whole Western Visayas. In 2011, it is the top 10 in Western Visayas to produce 164,839 metric tons according to Bureau of Agricultural Statistics.
It is assumed that there are 3 pumps per hectare and 1000 kg of plant per hectare.
Average demand of 0.086 MW is calculated.
Table 2.48 Corn Production unit daily demand
Appliance Rating (W) Quantity Usage Factor Hours Used kWh
Pump
190
3
0.2
24
2.736

Table 2.49 Corn Production Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

2092.5
14647.69
62780
753360
0.086

44

2.5.5

Poultry
Chickens and poultry farms are raised in Orchard Valley, Tigum Pavia. Since Iloilo

is home of milling feeds to the whole Visayas due to its Corn production, Poultry Farming is very alive. These chickens are distributed in the central market, retail stores, Jolibee,
McDonalds, Mang Inasal, Jo’s Inato, Pecho-pack, Barrio Inasal, Andoks, Kenny Rogers,
KFC and Greenwich. Iloilo is top 6 in Western Visayas to produce 859,660 chickens in
2011 according to Bureau of Agricultural Statistics.
Assuming there are ten chickens in one light, and projected to have 1,751,158 total in 2045, there will be an average demand of 5.25 MW.

Table 2.50 Poultry unit daily demand
Appliance Rating (W) Quantity Usage Factor Hours Used kWh
Lights
60
1
1
12
0.72
Pump
190
3
0.2
24
2.736
Incubator
40
1
1
24
0.96
TOTAL
4.416

Table 2.51 Poultry Demand Quantification in 2045
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

127741.4
894190.4
3832500
45990000
5.25

45

Calculating the overall demand for Agricultural Sector, It is projected to have
29.255 average electricity consumption demand for the year 2045.
Table 2.52 Agricultural Sector Demand Quantification Summary
Daily (kWh)
Weekly (kWh)
Monthly (kWh)
Yearly (kWh)
Average Demand (MW)

3.

672987.49
4992818.39
20190997
242291960
29.255

Total Demand
From the calculations made from the Demand Quantifications in Chapter 2,

Average demands from residential, commercial, transportation, industrial, and agriculture sectors are presented in Table 3.1. Data were totalled in Table 3.2 where it was projected to have 262.28 MW of electricity demand by the year 2045. Calculations were based from the equations and assumptions introduced in Chapter 2.
Table 3.1 Demand Summary from the five sectors
RESIDENTIAL

COMMERCIAL

TRANSPORTATION

INDUSTRIAL

AGRICULTURE

Daily (kWh)

3873729.48

981360.095

163027.8088

709036.92

672987.49

Weekly (kWh)

25721891.6

8280100.66

1141404.662

4963258.44

4992818.39

Monthly (kWh)

110244027.

35488511.4

4891755.313

21271107.6

20190997

Yearly (kWh)
Average
Demand (MW)

1322928332

398847774.

59311197.98

258089438.9

242291960

151.019216

45.5305678

6.773278108

29.70366755

29.255

46

Table 3.2 Total Demand from the five sectors combined
Daily (kWh)
2526412.315
Weekly (kWh) 45099473.82
Monthly (kWh)
192086399
Yearly (kWh)
2281468703
Average (MW) 262.2817295

4.

Powerplant Capacity and Fuel Requirement

4.1

Powerplant Capacity
As of 2015, there are no future plans about potential powerplants to be built in

Iloilo. List of current powerplants operating in the province is tabulate on Table 4.1.1 with approximate life expectancies of 35 years from the commissioned year.
Table 4.1 Existing Powerplants in Iloilo
Power Plants in Iloilo
Source DOE:LIST of Philippine Power Plants
Name of Plant
Commissioned
Approx. Years
Panay DPP1
1981
35
Panay DPP3
1993
35
Power Barge 103
1985
35
Panay Power Corp.
1995
35
PECO
1995
35
GBPC
2010
35

Capacity (MW)
25
40
24
72
14.5
164

The powerplant capacity required to build in 2045 to catch up with the projected demands in Iloilo was computed using Equation [9] with 13% critical level of reserve margin and 39% efficiency.
Powerplant Capacity
( − + + )

1.13
.39

[9]

47

4.2

Powerplant Proposal
With the formula of Powerplant capacity in Equation [9], 284.77 MW capacity was

forecasted to be the need of Iloilo in the year 2045. This demand can easily be sustained by a coal-fired powerplant generation considering that there are many coal fired powerplants already in the Visayas region.
Table 4.2 Plant Capacity needed for 2045
DEMAND (2045)
EXISTING
DECOMMISIONING
FUTURE PLANS
Efficiency
Plant Capacity

262.2817295
339.5
175.5
0
39%
284.7650111

In a coal-fired powerplant, heat is created from the burned coal to turn the water into steam that will run the turbine and work for the rankine cycle. In order to determine how to surpass the required capacity, fuel requirements must be considered.

4.3

Fuel Requirement
In most thermal power plants, coal is generally used as the major fuel because of

its abundance and availability at relatively low cost. Translating the target capacity of
284.77 MW, using the conversion factor in Equation [10], will yield a value of
1,025,154,039.96 kJ/hr

48

Conversion Factor of MW to kJ/hr
1 = 3,600,000 /ℎ

[10]

Assuming that Bituminous Coal is to be used as the type of fuel, Fuel Requirement can be obtained using Equation 11 where the calorific value of bituminous coal is known to be 23250 kJ/kg.
Fuel Requirement ℎ

[11]

A fuel requirement of 44,092.65 kg/hr of coal was calculated, which when converted to in seconds will result to the conclusion of 12.25 kilograms of bituminous coal per second for the requirement of the future powerplant in order to reach the capacity of the projected plant to answer the demands projected in 2045.

5.

Energy Sources, Conversion Technologies and Siting

5.1

Energy Sources
Different fuel and energy sources were selected to establish the screening process

for the best foundation of power generation in Iloilo. These compose of coal, diesel, natural gas, geothermal, solar, bunker oil, wind, hydro, biomass, nuclear, wave, tidal and biofuel.

49

Coal has an abundant supply, currently inexpensive to extract, and is reliable and capable of generating large amounts of power but emits major greenhouse gases like carbon dioxide, nitrous and sulfur oxides, and other airborne particulates. Biofuels are also abundant in supply, it can be used in diesel engines and it has fewer emissions than fossil fuel sources while natural gas, however, is the cleanest burning fossil fuel that is widely available but transportation costs are high.
Uranium or nuclear does not emit greenhouse gases or CO2. It is efficient in transforming energy into electricity and only gets refuelled yearly, unlike coal plants that need trainloads of coal everyday. It has a very high capital cost due to safety, emergency, containment and radioactive wastes, then serious problems occur due to long term storage of these wastes.
Geothermal, unlike the mentioned sources above, gives minimal environmental impacts. Geothermal fields are found only in few areas around the world, promisingly including the Philippines that resides along the pacific ring of fire. It has expensive startup costs, and its wells could eventually be depleted. Hydropower, Solar, and Wind energy, are the most non-polluting, no emission, and most abundant energy sources available. But then they have high initial investments and they require large physical space for PV panels, extensive land use for wind farms, and have environmental impacts by changing the environment in the dam area for hydropower.

50

From the thirteen selected energy sources; reliability, fuel cost, CO2 emission, fuel handling accidents, energy content, efficiency, power generation, and availability, were considered as measures in deciding the best fuel/energy source in Iloilo.
Criteria

Coal

Diesel

Nat Gas

Geothermal

Solar PV

Reliability (%)

80

70

80

40

50

Fuel Cost (Php/KJ)

0.0002172

0.0011994

0.0001583

0.000000135

2.35E-12

CO2 Emission (ton/GWh)

1300

900

716

400

160

Fuel handling Accident
(Deaths/GWh)

0.003823

0.00342

0.32915

0.24256

0

Energy Content (KJ/kg)

230219

37935.91

38600

15160

1507

Efficiency

45

38

40

23

40

Power Generation (GWh)

17682.9995 4371.9999 3.28E+04 1419.99996 889.999975

Availability

0.7

0.9

0.5

0.6

0.96

Table 5.1 Energy Sources First Screening (Efficiency > 35%)
Bunker
Oil

Wind

Hydro

Biomass

Nuclear

Wave

Tidal

Biofuel

70

50

50

70

85

40

40

50

0.000978

1.7E11

1.55E12

1.3E-06

0.00036

1.2E-11

4.8E12

0.00006
5

CO2
Emission
(ton/GWh)

1090

100

430

531

10

320

365

788

Fuel handling
Accident
(Deaths/GWh)

0.003913

0.059
3

0.00179
9

0.00194

0.47343

0.03291
5

0.0467
3

0.00203
4

Energy
Content
(KJ/kg)

40878.52

5347

7700

22000

50 000

6200

3500

34320

Efficiency

40

30

85

45

80

20

20

45

5408

1353

1469

917

3530

2571

2571

917

0.5

0.5

0.6

0.9

0

0.5

0.5

0.5

Criteria
Reliability
(%)
Fuel Cost
(Php/KJ)

Power
Generation
(GWh)
Availability

51

The efficiency is a very important factor in selecting a type of powerplant because it gives the percentage of the total energy content of a fuel that is converted into electricity.
Using it as the first screening criteria, with efficiency > 35%, geothermal, wave and tidal energy sources will be omitted as seen in Table 5.1.
Next criterion applied was the reliability of the sources. An energy source is considered reliable if it can supply a powerplant to generate a consistent electrical output and is available to meet predicted peaks in demand. Solar, wind, hydro and biofuel from
Table 5.2 was omitted in the process.
Table 5.2 Energy Sources Second Screening (Reliability > 50%)
Criteria

Coal

Diesel

Nat
Gas

Solar
PV

Bunker
Oil

Wind

Hydro

Biomass

Nuclear

Biofuel

Reliability
(%)

80

70

80

50

70

60

50

50

85

50

Fuel Cost
(Php/KJ)

0.00
0217
2

0.0011
994

0.0001
583

2.35E12

0.00097
8

1.7E11

1.55E12

0.000001
3

0.00036

0.00006
5

1300

900

716

160

1090

100

430

531

10

788

0.00
3823

0.0034
2

0.3291
5

0

0.00391
3

0.059
3

0.0017
99

0.00194

0.47343

0.00203
4

2302
19

37935.
91

38600

1507

40878.5
2

5347

7700

22000

50 000

34320

45
1768
2.99
95
0.7

38

40

40

40

30

85

45

80

45

4371.9
999

3.28E
+04

889.99
99751

5407.99
985

1353

1468.9
99959

916.9999
743

3530

917

0.9

0.5

0.96

0.5

0.5

0.6

0.9

0

0.5

CO2
Emission
(ton/GWh)
Fuel
handling
Accident
(Deaths/GW
h)
Energy
Content
(KJ/kg)
Efficiency
Power
Generation
(GWh)
Availability

Third screening criterion was about the availability. From the remaining six sources of energy, it is obvious that nuclear, the most unacceptable source for the people, expensive

52

and not good for the country, has an availability value of 0. When it comes to nuclear plant, it is not only the Department of Energy that will sign its approval but also the Department of Energy and Natural Resources, the mayor, the barangay captain, and all the people residing nearby.
Table 5.3 Energy Sources Second Screening (Availability)
Criteria

Coal

Diesel

Nat Gas

Bunker Oil

Wind

Nuclear

Reliability (%)
Fuel Cost
(Php/KJ)
CO2 Emission
(ton/GWh)

80

70

80

70

60

85

0.0002172

0.0011994

0.0001583

0.000978

1.7E-11

0.0003602

1300

900

716

1090

100

10

0.003823

0.00342

0.32915

0.003913

0.0593

0.47343

230219

37935.91

38600

40878.52

5347

50 000

45

38

40

40

30

80

Fuel handling
Accident
(Deaths/GWh)

Energy
Content
(KJ/kg)
Efficiency
Power
Generation
(GWh)
Availability

17682.9995 4371.9999 3.28E+04 5407.999849 1352.99996
0.7

0.9

0.5

0.5

0.5

3530
0

From coal, diesel, natural gas, bunker oil, and biomass, the attributes were ranked with the weights listed in Table 5.4 using Equations [12] and [13]
Ranked attributes (for parameters we want to maximize) − −ℎℎ

[12]

Ranked attributes (for parameters we want to minimize) −ℎℎ −ℎℎ

[13]

53

Table 5.4 Energy Sources Attributes Ranking
Criteria

Coal

Diesel

Nat Gas

Bunker Oil

Wind

Weight

Normalized
Weight

Reliability (%)
Fuel Cost
(Php/KJ)

1
0.8197
98014

0

90

0.140625

1

80

0.125

0

1

70

0.109375

Fuel handling
Accident
(incident/GWh)

0.9942
45286

0
0.1847925
88
0.2730819
25
0.9939702
33

0

CO2 Emission
(ton/GWh)

1
0.86895
9185
0.75942
7828

0.82469
9734

70

0.109375

Energy
Content
(KJ/kg)

1

0.07653
437

0.07972
3752

0.0906666
54

0

90

0.140625

Efficiency

1

0

0.28571
4286

0.2857142
86

0

90

0.140625

Power
Generation
(GWh)
Availability

0.5262
26953

0.10844
054

1

0.1409570
11

0.01368
4537

90

0.140625

0.5

1

0

0

0

60

0.09375

0
0.52015
605
0.99547
691

0

The attributes for each remaining sources were summarized in Table 5.5 concluding that coal is the best fuel source for conventional powerplants in Iloilo based on the model and assumptions provided, as wind for renewable plants.
Table 5.5 Energy Sources Prioritization
Criteria
Reliability (%)
Fuel Cost (Php/KJ)
CO2 Emission
(ton/GWh)
Fuel handling Accident
(incident/GWh)

Energy Content
(KJ/kg)
Efficiency
Power Generation
(GWh)
Availability

TOTAL

Coal
0.140625
0.102474752
0

Diesel
0
0

0.05689207 0.083062419 0.029868336

0.108745578 0.10888029
0.140625
0.140625

Nat Gas
Bunker Oil
0.140625
0
0.108619898 0.023099074

0

0.074000665 0.01524945

0.040178571 0.040178571
0.140625

0.109375

0.108715494 0.090201533

0.01076265 0.011211153 0.012749998
0

Wind
0
0.125

0.01982208

0
0
0.001924388

0.046875
0.09375
0
0
0
0.753970995 0.28553445 0.524322041 0.234433553 0.326500921

54

5.2

Conversion Technologies
The model includes nine coal burning technologies: Spreader Stoker, Underfeed

Stoker, Direct Fuel Bed Firing, Suspension Firing, Subcritical Pulverized Coal
Combustion, Supercritical Pulverized Coal Combustion, Atmospheric Fluidized Bed
Combustion, Pressurized Fluidized Bed Combustion, and Integrated Gasification
Combined Cycle, listed in Table 5.6
Table 5.6 Conversion Technologies First Screening (Efficiency > 30%)
Criteria

Spreader
Stoker

Underfeed
Stoker

DFBF

Suspension
Firing

Subcritical
PCC

Supercritical
PCC

AFBC

PFBC

IGCC

Efficiency

29%

30%

27%

33%

33%

43%

36%

42%

45%

28

27.6

34.5

18

23.68

24.97

26.01

29.89

33

14

18

13

6

10

10

12

18

12

18.65

16.33

17.8

15.25

19.62

17.97

16.44

15.69

15.33

4.7E-05

0.00002

6E06

0.000012

2.3E-05

0.000033

0.0000
5

0.000
009

1.7E05

650

330

172

632

1000

820

920

790

735

0.4

0.7

0.5

0.9

0.5

0.4

0.4

0.3

0

Capital Cost
($/MWh)

Equipment
Life (years)
Operating
Cost
($/MW)
Death Rates
(death/GWh)

CO2
Emissions
(kg/MWh)
Particulate
Generated
(kg/MWh)

Emphasizing the efficiency, spreader stoker and DFBF will be removed from the first screening. Second screening focused on the capital costs to be limited to 28$ or roughly 1300 pesos per megawatt hour. Data are shown in Table 5.7 where PFBC and
IGCC will be omitted.

55

Table 5.7 Conversion Technologies First Screening (Efficiency > 30%)
Criteria
Efficiency
Capital Cost
($/MWh)
Equipment
Life (years)
Operating
Cost
($/MW)
Death Rates
(death/GWh)
CO2
Emissions
(kg/MWh)
Particulate
Generated
(kg/MWh)

Underfed
Stoker

Suspension
Firing

Subcritical
PCC

Supercritical
PCC

AFBC

PFBC

IGCC

30%

33%

33%

43%

36%

42%

45%

27.6

18

23.68

24.97

26.01

29.89

33

18

6

10

10

12

18

12

16.33

15.25

19.62

17.97

16.44

15.69

15.33

0.00002

1.2E-05

2.3E-05

0.000033

330

632

1000

820

920

790

735

0.7

0.9

0.5

0.4

0.4

0.3

0

0.00005 0.000009 0.000017

Using the remaining 5 technologies, attributes were computed using equations [12] and [13] with the weights and attributes quantified in Table 5.8
Table 5.8 Conversion Technologies Attributes Ranking
Underfed
Stoker

Suspension
Firing

Subcritical
PCC

Supercritical
PCC

AFBC

Weight

Normalized
Weight

Efficiency
(%)
Capital Cost
($/MWh)
Equipment
Life (years)

0

0.230769

0.230769

1

0.461538

50

0.125

0

1

0.408333

0.27395833

0.165625

50

0.125

1

0

0.333333

0.33333333

0.5

60

0.15

Operating
Cost ($/MW)

0.75286

1

0

0.37757437

0.727689

70

0.175

Death Rates
(death/GWh)
CO2
Emissions
(kg/MWh)
Particulate
Generated
(kg/MWh)

0.789474

1

0.710526

0.44736842

0

60

0.15

1

0.549254

0

0.26865672

0.119403

70

0.175

0.4

0

0.8

1

1

40

0.1

Criteria

56

Table 5.9 Conversion Technologies Prioritization
Underfeed
Stoker

Suspension
Firing

Subcritical
PCC

Supercritical
PCC

AFBC

Capital Cost ($/MWh)

0
0

0.028846
0.125

0.028846
0.051042

0.125
0.03424479

0.057692
0.020703

Equipment Life (years)

0.15

0

0.05

0.05

0.075

0.131751

0.175

0

0.06607551

0.127346

0.118421

0.15

0.106579

0.06710526

0

CO2 Emissions
(kg/MWh)

0.175

0.096119

0

0.04701493

0.020896

Particulate Generated
(kg/MWh)

0.04

0

0.08

0.1

0.1

0.615172

0.574966

0.316467

0.4894405

0.401636

Criteria
Efficiency (%)

Operating Cost
($/MW)
Death Rates
(death/GWh)

Based on the model for conversion technologies of coal plants used in this paper, underfeed stoker seemed to be the best fit. In underfeed mode, the coal is fed under the bed, below the point of air admission, where it moves in co-current flow with the combustion air.

5.3

Sites
Seven initial sites were selected in the province of Iloilo. The main standard in

selecting the sites was their distances from the sea. Since a coal plant is decided to be built, it will be economical if the plant will use a once-through system in cooling its condensers by means of the water body nearby.

57

The selected sites are from the town of Guimbal, Miagao, Tigbauan, Ajuy, Barotac
Viejo, Carles, and Barotac Nuevo, numbered from one to seven as seen in Figure 5.1.

Figure 5.1 Earthquake-induced landslide hazard map Iloilo with prospected sites.

The population, land area, income class, distance from sea, accessibility, distance from city, and distance from fault line, were used in the Edward’s model to select the best site for the plant. With the limit of 10 km distance from the fault line in first screening, Site
2 was eliminated.

58

Table 5.10 Sites First Screening (Distance from Fault Line > 10 km)
Criteria

SITE 1 SITE 2 SITE 3 SITE 4 SITE 5 SITE 6 SITE 7

Population (as of May 1,
2010)

32,325

64,545

58,814

47,248

41,470

62,690

51,867

Land Area
(as of 2007, in hectares)

4,461

15,680

8,368

17,557

18,578

10,405

9,449

Income Class

4th

1st

2nd

2nd

3rd

2nd

2nd

Distance from Sea (km)

2

5

3

1

1

2

2

Accessibility (distance from
Main Road km)

2

4

2

3

3

10

7

Distance from City (km)

29

40.5

22.5

91.9

60.4

147.6

30

Distance From Fault Line
(km)

13.2

10

28.9

158

143

232

125

Distances from sea were focused next in the second screening, removing Site 3 from the choices.
Table 5.11 Sites Second Screening (Distance from Sea < 3 km)
SITE 1

SITE 3

SITE 4

SITE 5

SITE 6

SITE 7

Population (as of May 1,
2010)

32,325

58,814

47,248

41,470

62,690

51,867

Land Area
(as of 2007, in hectares)

4,461

8,368

17,557

18,578

10,405

9,449

Income Class

4th

2nd

2nd

3rd

2nd

2nd

Distance from Sea (km)

2

3

1

1

2

2

Accessibility (distance from Main Road km)

2

2

3

3

10

7

Distance from City (km)

29

22.5

91.9

60.4

147.6

30

Distance From Fault Line
(km)

13.2

28.9

158

143

232

125

59

Using the previous equations, where population, income class, and the distances from sea, main road and city are minimized, while land area and distance from fault line are maximized, Table 5.12 is produced showing the weights of the different parameters.
Table 5.12 Sites Attributes Ranking
SITE 1

SITE 4

SITE 5

SITE 6

SITE 7

Weight

Normalized
Weight

Population (as of May 1, 2010)

1

0.50854

0.69883

0

0.35643

60

0.15

Land Area
(as of 2007, in hectares) 0

0.92767

1

0.42105

0.35333

60

0.15

Income Class

0

1

0.5

1

1

20

0.05

Distance from
Sea (km)

0

1

1

0

0

80

0.2

Accessibility
(distance from
Main Road km)

1

0.875

0.875

0

0.375

50

0.125

1

0.46964

0.73524

0

0.99156

50

0.125

0

0.66179

0.59323

1

0.51096

80

0.2

Distance from
City (km)
Distance From
Fault Line (km)

Table 5.13 Sites Prioritization

Population (as of May 1, 2010)

SITE 1 SITE 4
SITE 5
0.15
0.076282 0.104825

SITE 6
0

SITE 7
0.053465

Land Area
(as of 2007, in hectares)

0

0.139151

0.15

0.063158

0.053

Income Class

0

0.05

0.025

0.05

0.05

Distance from Sea (km)

0

0.2

0.2

0

0

Accessibility (distance from Main Road km) 0.125

0.109375 0.109375

0

0.046875

Distance from City (km)

0.125

0.058706 0.091906

0

0.123946

Distance From Fault Line (km)

0

0.132358 0.118647

0.2

0.102194

0.4

0.765872 0.799752 0.313158 0.429479

60

Site 5 would be the best location for a coal powerplant in Iloilo. It is located in the town of Barotac Viejo, 60 km from the city.

6.

Powerplant Design
From the demand quantification and sources selection, electricity in Iloilo was

projected to have a deficit of 284.77MW in 2045. Two powerplants with that capacity is decided to be built and operated using the sources of coal and wind energy. The Coal-fired powerplant will cover for the 250MW demand as the Wind farm will provide the other
35MW.

6.1 Objectives for the Design
Five objectives bounded the design for the two (conventional and alternative) powerplants. To (1) provide at least 300MW electricity to the province of Iloilo, (2) have a return of investment or payback period of less than 10 years’ time, (3) have an efficiency of more than 30%, (4) plant sustainability of at least 30 years, and (5) a B/C ratio of greater than 2.0.

61

6.2

Non-renewable Source Powerplant
Coal is the most recommended energy source in Iloilo as agreed from the Edward’s

model created in chapter 5. Assumptions were made for the components of the coal-fired powerplant in order compute for the states, work, and efficiency of the whole process. The values presumed are based on the industrial steam operations such as the usual inlet temperature or pressure of the turbine.
First decision made for the system is the use of Superheat Rankine Cycle with One
Closed-Type Feed Water Heater with Drain Cascading Backward as the schematic diagram is shown in Figure 6.1. The turbine inlet temperature and pressure is assumed to be 500°C
15MPa respectively, while the turbine and pump have polytropic efficiencies of both 85%.

Figure 6.1 Schematic Diagram of a Rankine Cycle with One Closed-Type Feed Water
Heater with Drain Cascading Backward

62

Figure 6.2 TS Diagram of a Rankine Cycle with One Closed-Type Feed Water Heater with Drain Cascading Backward

6.2.1

Thermodynamic States
From the inlet temperature and pressure of the turbine, the different states for the

whole cycle are obtained. Values are tabulated in Table 6.1
Table 6.1 States in the Coal-Fired Powerplant’s Rankine Cycle
State 1 (T1 = 500°C, P1 = 15MPa)

h1 = 3308.6 kJ/kg (s1 = 6.3443 kJ/kgK)

State 3 (T3 = 40°C, η = 0.85)

h3 = 2174.9 kJ/kg

State 4 (T4 = 40°C, x = 0)

h4 = 167.5 kJ/kg, (v4 = 0.0010079 m3/kg)

State 5 (P5 = 15MPa, η = 0.85)

h5 = 185.3 kJ/kg

State 2 (T2 = 191.12°C, η = 0.85)

h2 = 2804.1 kJ/kg

State 7 (T7 = 191.12°C, x = 0)

h7 = 812.5 kJ/kg

State 8 (T8 = 40°C)

h8 = 812.5 kJ/kg

State 6 (TTD = 5°C, P6 = 15MPa)

h6 = 797.1 kJ/kg

63

Feedwater Heater Efficiency
ℎ −ℎ
% ℎ = ℎ6 −ℎ5

[14]

Power Generated = (ℎ1 − ℎ2 ) + (1 − % ℎ )(ℎ2 − ℎ3 ) − (ℎ5 − ℎ4 )

[15]

Mass Flow Rate (ℎ1 − ℎ6 ) = 0.85( )( )

[16]

Work of Turbine = (ℎ1 − ℎ2 ) + (1 − % ℎ )(ℎ2 − ℎ3 )

[17]

Work of Pump = (ℎ5 − ℎ4 )

[18]

Heat Added = (ℎ1 − ℎ6 )

[19]

Powerplant Efficiency − ƞ =

[20]

2

7

From the power requirement of 250MW, mass flow of steam can be computed as
282.49kg/s using equation [15]. Relating it to mass flow rate of Bituminous coal with
23250kJ/kg calorific value in equation [16], mcoal will be yielded as 35.90 kg/s. Through equations [17] to [20], work of turbine, pump, and heat added, can be calculated as
203.85MW, 3.86MW, and 544.44MW respectively, these lead to an efficiency of 36.70% greater than the set standard of 30%.

6.2.2

Components Selection
Boiler selection was essentially based on the rated steam temperatures, years of

experience of the manufacturers in boilers, and structures, since the steam capacity,

64

working pressures, and the likes can be customized depending on the requirement of the plant. The coal-fired powerplant to be built in Iloilo must have working temperature of
500°C, pressure of 15MPa, and steam mass flow rate of 282.49kg/s. Three Coal-Fired
Boilers from three different manufacturers, namely, Guangzhou Jutao Machinery
Equipment Co., Ltd., Henan Yuanda Boiler Co., Ltd., and Henan Taiguo Boiler
Manufacture Co., Ltd., were compared.
Table 6.2 Boiler Selection
Structure
Jutao
Yuanda
Taiguo

Rated steam temperature

Water Tube
Fire, Water Tube
Water Tube

300-375°C
460-510°C
375-425°C

Experience of Boiler
Manufacturing
14 years
58 years
38 years

Figure 6.3 (from left to right) Jutao, Yuanda, and Taiguo boilers
Henan Yuanda Boiler is the most favorable for the coal-fired powerplant agreeing to its working temperature of 500°C, and since the manufacturer already noted a 58 years of experience, it is just practical to use Yuanda as the boiler for the coal plant.

65

Steam turbine selection was also based on three models, one from Mitsubishi Heavy
Industries, Ltd. and another two from Siemens. Comparing the power, working pressure and temperature, Mitsubishi appeared to be the best match for the coal-fired powerplant in
Iloilo.
Table 6.3 Turbine Selection
Power
Mitsubishi Land
Turbine
Siemens SST-5000
Siemens SST-700

Pressure

Temperature

200 to 300 MW

16 MPa

540°C

120 to 750 MW
175 MW

19 Mpa
16.5 Mpa

600°C
585°C

Figure 6.4 (from left to right) Mitsubishi, SST-5000, and SST-700 turbines
Condenser to be selected must have operating conditions under 40°C and
0.007375MPa, and has the capacity to handle 282.49kg/s mass flow of steam based on the computations of states above. Three units were chosen from Jinan Retek Industries,
Modular Condenser from SPX Heat Transfer Inc, and Shanghai MeluckRefrigeration
Equipment, all operates at 40°C.

66

Table 6.4 Condenser Selection

Jinan Retek Industries
Modular Condenser from SPX Heat
Transfer Inc
Shanghai Meluck Refrigeration
Equipment

Operating
Temperature
40°C
40°C

Working
Pressure
0.00814 MPa
.0073 Mpa

Flow
Rate
200 kg/s
300 kg/s

40°C

0.009 Mpa

250 kg/s

Figure 6.5 (from left to right) Jinan, SPX, and Meluck condensers
Closed type Feedwater heater with 66.5 mass flow of steam was selected from
SPX Heaters (Yuba), Mazda Limited, and Godrej Properties Limited. The three were compared in Table 6.5 and it was decided to use the Mazda feedwater heater.
Table 6.5 Feedwater Heater Selection
Yuba
Mazda
Godrej

Temperature
474.37 °C
485.24 °C
426.54 °C

Pressure
0.7 Mpa
0.9 Mpa
0.8 Mpa

Steam Flow
51.45 kg/s
66.25 kg/s
58.13 kg/s

Figure 6.6 (from left to right) Yuba, Mazda, and Godrej closed-type feedwater heaters

67

Pump selection was based on the volumetric flow rate of water, which is calculated to be 1016m3/h, and pump head of 1700m, operating at 40°C and 0.007375MPa. Three pump models were used, Griswold 811 Series, Sulzer Condensate, and ANSI Process
Rhurpumpen Pumps. Griswold pump was decided to be enough for the powerplant cycle.
Table 6.6 Pump Selection
Griswold
Sulzer
Rhurpumpen

Water Flow
1702.5 m3/h
4000 m3/h
1150 m3/h

Pump Head
2910 m
4200 m
235 m

Figure 6.7 (from left to right) Griswold, Sulzer and Rhurpumpen pumps
Last selection is for the pulverizer of the coal power plant. The parameter used was their capacity of mass flow of coal which has a requirement of 27.55 kg/s on the calculations shown in equations [14] to [20]. Three models were used, Germany Crusher
Pulverizer Mill, B&W Roll Wheel, and CI Inc. Vertical coal Pulverizer Mill. The specifications for capacities are tabulated in Table 6.7 selecting the Germany Crusher
Pulverizer Mill with mass flow rate capacity of 2.7kg/s to 40.7kg/s that can carry the
35.90kg/s requirement of the designed plant.

68

Table 6.7 Pulverizer Selection
Mass Flow Rate
2.7 to 40.7 kg/s
1.8 to 24.9 kg/s
0.3 to 16.6 kg/s

Germany Crusher
B&W
CI Inc.

Figure 6.8 (from left to right) Germany Crusher, B&W, and CI Inc. pulverizers
In thermal plants like coal-fired, it is just ethical to release clean or at least treated emission gases to the atmosphere. Thus in this design, Flue Gas Desulfurization is also considered. Three models were selected from Henan Sunsungs Import & Export Co., Ltd,
Sichuan Junhe Environmental Protection Co., Ltd, and Liaoning Mineral & Metallurgy
Group Co., Ltd.
Table 6.8 Flue Gas Desulfurization Selection
Sunsungs
Sichuan
Liaoning

Air Volume
12,000 to 230,000 m3/h
150,000 to 1,500,000 m3/h
50,000 to 150,000 m3/h

Desulfurization Efficiency
More than 90%
95% to 99%
98% to 99%

69

Figure 6.9 (from left to right) Sunsungs, Sichuan, and Liaoning FGDs
Sunsungs FGD was selected since the capacity is already enough for the powerplant and the desulfurization efficiency is acceptable to the environmental rules set by the
DENR. Also, it has the cheapest price among the three.

6.2.3

Fine Tuning
Selection of components was based on the initial computation of states.

Consequently there are almost no changes in the properties of steam and parameters assumed from the original setting. The summary of design parameters are tabulated in
Table 6.9 with the prospected basic design of the plant in Figure 6.10. Clearly, there are no cooling towers selected since the desired scheme for the process is a once-through system using the nearby ocean in the town of Barotac Viejo.

70

Table 6.9 Re-calculated Design Parameters
State

Pressure

Temperature

1

15 Mpa

500 °C

3

0.007375 Mpa

40 °C

4

0.007375 Mpa

40 °C

5

15 MPa

41.09 °C

2

1.282664 Mpa

161.03 °C

7

1.282664 Mpa

191.12 °C

8

0.007375 Mpa

40 °C

6

15 MPa

186.12 °C

h
3308.6
kJ/kg
2174.9
kJ/kg
167.5
kJ/kg
185.3
kJ/kg
2804.1
kJ/kg
812.5
kJ/kg
812.5
kJ/kg
797.1
kJ/kg

v
0.0208
m3/kg
16.31
m3/kg
0.0010079
m3/kg
0.0010018
m3/kg
18.890908
m3/kg
0.0011452
m3/kg
5.2416978
m3/kg
0.0011244
m3/kg

s
6.3443
kJ/kg-K
6.983
kJ/kg-K
0.5723
kJ/kg-K
0.580596
kJ/kg-K
8.682649
kJ/kg-K
2.246285
kJ/kg-K
2.632118
kJ/kg-K
2.178556
kJ/kg-K

quality superheated 0.83
(0) saturated liquid compressed liquid superheated
(0) saturated liquid 0.27 compressed liquid

Figure 6.10 Prospected Design of the Coal-fired Powerplant in Iloilo

71

Table 6.10 Final Design Parameters
Turbine Inlet Temperature
Turbine Inlet Pressure
Turbine Outlet Temperature
Turbine Outlet Pressure
Mass Flow of Steam
Mass Flow of Coal
Power Output
Efficiency

6.2.4

500 °C
15 MPa
40 °C
0.007375 Mpa
282.49 kg/s
35.90 kg/s
250 MW
36.70 %

Costs Analysis
The costs per set of equipment are tabulated in Table 6.11 including the particulars

in building the powerplant such as the earthworks, structural steel, electrical, piping, and general facilities. Capital cost has an estimated total of 835,100,000 pesos.
Table 6.11 Capital Costs
Equipment
Boiler (Yuanda)
Steam Turbine (Mitsubishi)
Condenser (Shanghai Meluck)
Feedwater Heater (Mazda)
Pump (Griswold)
Pulverizer (Germany Crusher)
FGD (Sunsungs)
Total
Particulars
Earthwork/Civil
Structural Steel
Electrical
Piping
General Facilities
Total

Costs
250,000,000 php
470,000,000 php
5,000,000 php
2,500,000 php
350,000 php
5,000,000 php
4250000 php
737,100,000 php
Costs
8,000,000 php
15,000,000 php
10,000,000 php
5,000,000 php
60,000,000 php
98,000,000 php

72

Operation and maintenance budgets per year are projected for the thirty year operation and adding the total to the capital costs will yield an overall of 13,435,100,000 pesos for the whole life of the plant.
Table 6.12 Operation and Maintenance Costs
Operation and Maintenance
Plant
Coal
Total

Costs per year
20,000,000 php
400,000,000 php
420,000,000 php

The 2500MW powerplant which runs 80% of the time is equivalent to a capacity of 1401.6 GWh/year. Assuming that the average billing for electricity for the whole 30 years is 10php/kWh, there will be a corresponding revenue of 1,401,600,000 pesos every year which is equal to 42,048,000,000 pesos revenue for 30 years. Calculating the B/C
Ratio, the revenue over expenses yields a value of 3.13, higher than the required ratio of 2.
Projecting the costs and earnings will show the payback period of the project, which will be around 9½ years as presented in Figure 6.11.
50,000,000,000

Pesos

40,000,000,000
30,000,000,000
20,000,000,000
10,000,000,000
0
0

5

10

15

20
Year

Cost

Revenue

Figure 6.11 Payback Period

25

30

35

73

Table 6.13 Settlement of Objectives
Power
Efficiency
Payback Period
B/C Ratio

6.3

250 MW = 250 MW
36.70 % > 30 %
9½ years < 10 years
3.13 > 2

Renewable Source Powerplant
Wind power is being promoted by the government for its clean and free source of

electricity that reduces our dependence on fossil fuels. In this design, horizontal axis wind turbines are used because they work best on the type of wind in Iloilo displayed in Figure
6.12. Horizontal Axis Wind Turbines have the main rotor shaft and electrical generator at the top of the tower that points into or out of the wind.

Figure 6.12 Average Wind Speed in Iloilo

74

6.3.1 Design
Power of 35MW is required to be provided by the Wind Farm design for Iloilo.
Parameters such as the hub height, wind speed, and blade swept area are considered in calculating the power from Equation [21].
Wind Power
1

= ƞ 3
2

[21]

Wind Velocity
1

2
2

=

ℎ 7
( ℎ2 )
1

[22]

Average air density in the Philippines is 1.225kg/m3 and the efficiency assumed for a wind turbine is 40%. A normal 6.5m/s wind speed was noted in Iloilo for a 15m altitude, assuming that the hub height will be set at 80m (as most wind turbines have 80m hub heights), the velocity at 80m will be calculated as roughly 8.26m/s using Equation [22].
From Equation [21], increasing A directly increases the power generation, thus in selecting the wind turbines for the farm, hub heights and swept areas will be the main criteria.

6.3.2 Wind Turbine Selection
Eight models were selected from GE, Vestas and Siemens. Using the initial screening criteria of more than or equal to 80m hub height, GE1.5s, Vestas V82, and
Gamesa G87 will be eliminated. And from the remaining models, the second criteria which has the highest area swept by blades, the Vestas V112 model will be selected.

75

Table 6.14 Wind Turbine Selection
Model

Capacity

GE 1.5s
GE 1.5sle
Vestas V82
Vestas V90
Vestas V100
Vestas V112
Gamesa G87
Siemens

1.5 MW
1.5 MW
1.65 MW
3 MW
1.8 MW
3 MW
2 MW
2.3 MW

Blade
Length
35.25 m
38.5 m
41 m
45 m
50 m
56 m
43.5 m
46.5 m

Hub Height
64.7 m
80 m
70 m
80 m
80 m
84 m
78 m
80 m

Area Swept by Blades
3.904 m2
4.657 m2
5.281 m2
6.362 m2
7.854 m2
9.852 m2
5.945 m2
6.793 m2

Figure 6.13 Technical Specifications of Vestas V112

Rated Wind
Speed
12 m/s
14 m/s
113 m/s
15 m/s
15 m/s
12 m/s
13.5 m/s
13-14 m/s

76

Figure 6.13 Technical Specifications of Vestas V112

6.3.3 Fine Tuning
Going back to Equation [21] with Vestas V112’s specifications, power of one turbine can be calculated as 1,360.29 kW. With the demand power of 35MW from wind, there will be a need of at least 26 wind turbines in the wind farm. These 26 turbines can produce an average of 35.37MW of electricity.
Usual spacing of towers in a wind farm is five times their rotor diameters. By Vestas
V112’s specific diameter of 102m, spacing can be obtained as 510m for each as pictured in Figure 6.14.

77

Figure 6.14 Wind Turbine Tower Spacing
This spacing in two rows can be economically optimal taking into account the land cost. Final design of the wind turbine is shown in Figure 6.15 which will be planted along the shore of Barotac Viejo, Iloilo.

Figure 6.15 Wind Farm Final Design

78

6.3.4 Costs Analysis
One unit of the Vestas V112 costs 428.76 million pesos. 26 units plus operation and maintenance costs for 30 years will yield a total of 14,331,360,000 pesos as seen in
Table 6.15.
Table 6.15 Equipment, Operation and Maintenance Costs
Vestas V112 Unit (26 turbines)
Operation (for 30 years)
Maintenance (for 30 years)
Total

11,147,760,000 php
255,600,000 php
2,928,000,000 php
14,331,360,000 php

Again, assuming that the average billing for electricity in 30 year is 10php/kWh, with 245.28 GWh/year, an overall of 73,584,000,000 php revenue will be obtained in 30 years creating a B/C Ratio of 5.13.
80,000,000,000
70,000,000,000
60,000,000,000

Pesos

50,000,000,000

40,000,000,000
30,000,000,000
20,000,000,000
10,000,000,000
0

0

5

10

15

20
Years

Costs

Revenues

Figure 6.16 Payback Period

25

30

35

79

Table 6.16 Settlement of Objectives
Power
Efficiency
Payback Period
B/C Ratio

35.37 MW > 35 MW
40 % > 30 %
7 years < 10 years
5.13 > 2

7.

Conclusion and Recommendation

7.1

Conclusion
From the demand quantification and sources selection, a powerplant of 284.77MW

capacity in 2045 is decided to be driven by a combination of coal and wind energy.
Conversion technology for coal’s generation was decided to be an underfeed stoker and the site was located in the town of Barotac Viejo near the large body of water to be used for its cooling.

Figure 7.1 Final Coal-Fired And Wind Powerplants Designs

80

250MW was allotted for the coal source as 35MW for the wind energy. A payback period of 7 to 9½ years was obtained based on the designs made re-presented in Figure 7.1.
The efficiency of the coal-fired powerplant is 36.70% while for the wind farm is 40%, both higher than the set objective of 30%. Also, their B/C Ratios are greater than 2.

7.2

Recommendation for future work
For a more depth demand quantification results, it is recommended to increase the

numbers of models for each sectors. Like in agriculture sector, since Iloilo excels in the production of mango, pineapple, sugarcane, and water melon, these manufactures are also worthy to be included in the analyses. In the second part of the design, “locating the best site for a powerplant”, other parameters might be considered such as crime rates and transmission costs in the area.
For the selection of components, the plant may be improved by increasing the number of models from different manufacturers as prices of equipment may also be lessen.

81

References

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Situationer. Web. <http://www.doe.gov.ph/doe_files/pdf/01_Energy_Situationer/2011Power-Plants-Visayas.pdf>.
Research and Statistics Section. "Annual Provincial Profile 2014." (2015): 1-39. Official
Website of the Province of Iloilo. Provincial Planning and Development Office. Web.
< http://www.iloilo.gov.ph/downloadable-phoca/file/35-2014-iloilo-profile.html>
Union of Concerned Scientists. "How Coal Works." Coal and Other Fossil Fuels. Web.
<http://www.ucsusa.org/clean_energy/coalvswind/brief_coal.html#.VkpDhXarQ2w>.
"Cost and Performance Baseline for Fossil Energy Plants." Volume 1: Bituminous Coal and Natural Gas to Electricity. National Energy Technology Laboratory, 1 Sept. 2013.
Web. <http://www.netl.doe.gov/File Library/Research/Energy
Analysis/OE/BitBase_FinRep_Rev2a-3_20130919_1.pdf>.
Nayak, Jay. "What Coal Is Used as Major Fuel in Thermal Power Plant Only?" Quora.
Web. <http://www.quora.com/What-coal-is-used-as-major-fuel-in-thermal-power-plantonly>.
"NCSB Fact Sheet." Comparative Growth of Regional Economies. 20 July 2009. Web.
<http://www.nscb.gov.ph/ru6/FS2GRDP08final.pdf>.
"Philippine Road Data." Length of Roads by Surface Type and Condition. DPWH, 16 Jan.
2013. Web. <http://www.dpwh.gov.ph/infrastructure/Road Data/2012 Road Data for
Ipad/iloilo_city.htm>.
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Statistics. Databank and Information and Services Division. Web.
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Falling Rain Genomics, Inc. "Directory of Cities and Towns in Province of Iloilo,
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<http://www.fallingrain.com/world/RP/30/>.

82

Marcos, Detourist. "Iloilo Guide for First Time Travelers." Explore Iloilo, 22 July 2015.
Web. <http://www.exploreiloilo.com/guide/iloilo-travel/>.
"Over One and a Half Million Persons in Iloilo." Population and Housing. Philippine
Statistics Authority, 1 July 2002. Web. <http://psa.gov.ph/content/over-one-and-halfmillion-persons-iloilo>.
"Old Iloilo Capitol Now a National Historical Site." Wayback Machine. Philippine
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fi=p100413.htm&no=68>.
Explore
Iloilo.
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International
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22

July

2015.

Web.

"Revival of Panay Railways Pushed; Bidding for P16-B Project in May." Mega Scene.
Fil-Am Weekly. Web. <http://www.megascene.net/?p=6176>.
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