CHAPTER 4 DATA COLLECTION AND ANALYSIS

CHAPTER 4 DATA COLLECTION AND ANALYSIS 4.1 Company Profile In the end of 19th century, the electricity history began in Indonesia. Few Dutch companies...
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CHAPTER 4 DATA COLLECTION AND ANALYSIS 4.1 Company Profile In the end of 19th century, the electricity history began in Indonesia. Few Dutch companies built power plants to provide the electricity they need. With the passing time, electricity became the common need. By August 17th 1945, the electrical companies are taken by Indonesia and handed over by government in September 1945. Soekarno as the president of Indonesia established the Bureau for electricity and gas. As time goes by, the electricity and gas bureau was changed into (Badan Pimpinan Umum Perusahaan Listrik Negara) BPU PLN, and then it changed again into PLN. In the end it was changed into PT.PLN (PLN persero) in June 1994, and the government also gave opportunities to private sector to deal in electrical energy supply business. 4.1.1 Vision5) The vision of PT.PLN is to be acknowledged as progressive, reliable and reputable world-class company that derives its capability from human resource potential. 4.1.2 Mission5) The mission of PT.PLN is to run electricity business and other related venture, with focus on the satisfaction of customers, corporate members and shareholders; to provide electricity as a means to improve the quality of life of society; to drive electricity as a generator of economic activities; to run business activities in environment friendly way. 4.1.3 Motto5) “Electricity For A Better Life”

5)

Source: http://www.pln.co.id

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4.1.4 Company Value5) Mutual trust, integrity, care and learning. Sensitive responsive to the customers, it means always try to give satisfying services to the customers, as quickly, accurately, and suitable as possible. Honor the human status and dignity with all their strength and weaknesses, and recognize and protect the human right in doing business. Highly honor the value of honestly, integrity, and objectivity in handling business. Constantly and measurably, increase the quality and reliability of product and protect the quality of environment in doing business. Provide the same opportunity as widely as possible for every member of the company to perform and reach the position according to the criteria and competency required.

4.2 Data Collection Data collection is the data that has been processed to ease the analysis process.

4.2.1 Electric Power System PT.PLN has the goal to fulfill electricity demand, and to fulfill the demand PT.PLN needs many kinds of electricity tools. Based on Marsudi opinions,”Electric power system is a network of electricity centers and electricity main distributors which is connected by transmission into a whole set of interconnection.” (Marsudi, 2006: 7)

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Figure 4.1 Electricity Flow from Power Generator to Consumers

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Source: http://www.pln.co.id Source: Djiteng Marsudi, Operasi Sistem Tenaga Listrik (2006, 4)

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The operation expense from electric power energy is usually the biggest expense. Operation expenses are divided into: -

Buying electric energy expense

-

Employee expense

-

Fuel and material expense

-

Miscellaneous expense

According to Marsudi, operation management electric power system should reflect how economical the supply of electric energy, by also considering about load forecast, perquisite of the equipment maintenance, professionalism, electric power allocation and produce power generator economically. (Marsudi, 2006: 7)

4.2.2 WBP (Waktu Beban Puncak) WBP is the time when electricity use is the highest in a day. WBP usually occurs in the period of time between 17pm – 22pm. The use of electricity is increasing dramatically because in this period of time not only industry but also all the consumers from households use electricity. Although the electricity use from each household is not high but the number of households is the largest, so the demand of electricity in that period of time is increasing. This is the example of the daily loads of Java – Bali systems:

Figure 4.2 a Day Loads Java – Bali System7) 7)

Source: http://pln-jawa-bali.co.id

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4.2.3 DMP (Daya Max Plus) DMP is the regulation that PT.PLN has established. This regulation is especially should be obeyed by industry as one of the consumers of PT.PLN which is included in tariff B3, I2, I3, I4, and P2. DMP regulation is established on 10th August 2005 from director’s decision no. 0016.E/DIR/2005. DMP regulation is really implemented start from the bill on November 2005, based on the SE GM DJBB no. 001.E/GM/DJBB/2005 which is established on 24th August 2005.

DMP regulation is one kind of action that PT.PLN do to reduce the use of electricity in WBP. It means that the use of electricity in WBP does not increase dramatically. If the industries disobey this regulation, the industries will get twice as much as the normal tariff. It will of course increase the income for PT.PLN, but it’s not the aim of PT.PLN. The exact aim of DMP is to reduce the use of electricity in WBP. The principle of DMP is for the customers to decrease the use of electricity down to 50% in WBP period.

Beside of that the DMP regulation makes a possibility that the industry will change the electricity consumption to Luar Waktu Beban Puncak (LWBP). The electricity production at LWBP will be used and become additional income for PT.PLN itself.

4.2.4 Consumers of PT.PLN Consumers of PT.PLN can be divided into 4 main groups based on tariff classification, there are: -

Industries

-

Households

-

Business

-

Social

From these consumers, industries are the biggest consumers of electricity in volume. But by looking at the total number of consumers per group for Java-Bali region, there are 36,867 industries for year 2004 and 37,085 industries for year 2005. This is a small number compared to other type of consumers. On the other hand, the households are the biggest 24

consumers in quantity for the Java – Bali region. There are 21,120,382 consumers in 2004 and 21,802,701 in 2005, but its usage is the least from all of the groups.

4.2.5 Load Power Peak load power usually happen between 6 p.m. to 10 p.m. This is the peak loads that happen from April 2004 till May 2007. Table 4.1 Peak loads 2004 – 2005 Peak Load 2004 (MW)

Month

Max January February March April May June July August September October November December

13,750 13,700 13,800 13,785 14,085 14,250 14,350 14,225 14,225

Average

13,114 13,179 13,338 13,215 13,478 13,667 13,917 12,478 13,554

Min

10,700 11,800 12,200 10,900 11,390 11,370 12,915 9,100 12,225

Peak Load 2005 (MW) Max 14,245 14,275 14,450 14,509 14,675 14,600 14,450 14,600 14,600 14,750 14,500 14,250

Average 13,494 13,652 13,918 14,009 14,159 14,198 13,942 14,109 14,230 14,088 13,037 13,684

Min 11,500 12,550 12,700 12,950 12,900 12,850 12,800 11,920 13,000 12,500 10,250 12,400

Table 4.2 Peak loads 2006 - 2007 Peak Load 2006 (MW)

Month January February March April May June July August September October November December

Max 13,900 14,300 14,325 14,250 14,225 14,420 14,450 14,600 14,850 14,900 15,400 15,200

Average 13,355 13,599 13,599 13,514 13,756 14,035 13,943 13,938 14,375 13,632 14,725 14,572

Min 11,700 12,475 12,141 12,428 12,650 13,000 12,800 12,635 13,200 10,100 13,350 12,500

Peak Load 2007 (MW) Max 15,150 14,925 14,965 15,150 15,500

Average 14,469 14,115 14,361 14,585 14,845

Min 12,100 12,600 13,075 13,400 13,400

4.2.6 Power Plant in Java – Bali Power Plant in Java – Bali system is divided into four regions, there are: - Jakarta and Banten region - West Java region - Middle Java and Yogyakarta region 25

- East Java and Bali region 8)

Table 4.3 Power Plant for Java – Bali System Region

Jakarta and Banten

West Java

Middle Java and Yogyakarta

East Java and Bali

Power Plant PLTA Region 1 PLTU Suralaya Priok PLTGU Priok PLTG Priok PLTP Gunung Salak PLTU Muara Karang PLTGU Muara Karang PLTGU Cilegon K.steel PLTA region 2 PLTA Saguling PLTP Kamojang PLTP Drajat PLTG Sragi PLTA Cirata PLTGU Muara Tawar PLTGU Muara Tawar PLTA Jatiluhur PLTP Wyndu PLTP Drajat PLTA region 3 PLTA Mrica PLTU Tbrok PLTU Cilacap PLTU Tanjung Jati PLTGU Tbrok PLTG Cilacap PLTP Dieng PLTU Paiton PLTGU Grati PLTU Perak PLTG Glimanuk PLTG Psran PLTG Pmron PLTD Pmron PLTA region 4 PLTA Sutami PLTU Gresik PLTU Paiton PLTGU Gresik PLTG Gresik PLTG Glmur

Owner PT.IP PT.IP PT.IP PT.IP PT.IP PT.IP PT.PJB PT.PJB PT.PJB Luar PLN PT.IP PT.IP PT.IP PT.IP PT.IP PT.PJB PT.PJB PMT Luar PLN Luar PLN Luar PLN PT.IP PT.IP PT.IP PT.IP PT.IP PT.IP PT.IP PT.IP IPP PT.IP PT.IP PT.IP PT.IP PT.IP PT.IP PT.PJB PT.PJB PT.PJB PT.PJB PT.PJB PT.PJB PT.PJB

For power plant in Java – Bali region, PT.PLN is not the only owner of the power plant, but the ownership is also shared with subsidiaries such 8)

Source: http://hdks.pln-jawa-bali.co.id/app4/

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as PT. Power Indonesia and PT. Pembangkit Jawa Bali. The capacity of PT.Pembangkit Jawa Bali can be seen at APPENDIX D. Based on the data that had been collected, PT.PLN’s daya mampu netto is as big as 20,195.45MW9) till 31 st May 2007.

4.2.7 PLTU in 10,000MW Crash Program The steam-generated power plant is going to be built by PT.PLN to produce electricity. From all ten projects, there are only four of them that have been approved and they are Suralaya Baru, Labuhan, Jabar Utara, and Paiton. Other two contracts are in the process of approval; Teluk Naga and Tanjung Awar-Awar. The table 4.4 below is the details of the planned power plants. Table 4.4 The Power Generator in 10,000 MW Program No.

PLTU

Location

1

Suralaya Baru

Banten

1 x 600

2

Labuhan

Banten

2 x 300

3

Teluk Naga

Banten

3 x 300

4

Jabar Selatan

Jabar

3 x 300

5

Jabar Utara

Jabar

3 x 300

6

Rembang

Jateng

2 x 300

7

Jatim Selatan

Jatim

2 x 300

8

Paiton

Jatim

1 x 600

9

Tanjung Awar-awar

Jatim

2 x 300

Tanjung Jati Baru

Jateng

1 x 600

10

Total

Capacity (MW)

6,900

The background of this crash program is the increase of BBM (Bahan Bakar Minyak) where this things will increase the operational cost in producing electricity, there is also an uncertainty that there is no gas supply to PT.PLN, there is an indication that electrical crisis will happen in 2008. This crash program also supports the diversification primary energy that is transferring the used of BBM for generate the power generator to non-BBM with the goals to reduce the production cost. The non-BBM is coals and gas.

9)

Source: http://hdks.pln-jawa-bali.co.id/app4/

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4.3 Data analysis In analyzing the data of PT.PLN, some methods are used to see the effect of Daya Max Plus regulation to the peak loads and the risk that might happen.

4.3.1 Paired Sample t-Test The paired sample t test is used in this case to analyze the difference between two measurements of the same peak loads due to the situation of before and after the Daya Max Plus is established. The situation of before the Daya Max Plus is assumed increase based on the increasing of the economics and the situation of after the Daya Max Plus is the real situation that happened. (See Table 4.5 below)

Table 4.5 Peak Loads in Java - Bali System Observation 1 2 3 4 5 6 7 8 9 10

Before DMP (MW) January – October 2005 15,107 15,139 15,324 15,387 15,563 15,483 15,324 15,483 15,483 15,642

After DMP (MW) January – October 2006 13,900 14,300 14,325 14,250 14,225 14,420 14,450 14,600 14,850 14,900

Difference (MW) 1,207 839 999 1,137 1,338 1,063 874 883 633 742

The hypothesis of this case is: H 0 : D 0

H1 : D 0 The test is using α= 0.05 level of significance, and assuming that the differences are normally distributed.

The result from SPSS is like in Table 4.6, 4.7, and 4.8.

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Table 4.6 Paired Samples Statistics

Mean N Pair 1 VAR00001 15393.00 10 VAR00002 14422.00 10 VAR00001 = Before DMP VAR00002 = After DMP

Std. Deviation Std. Error Mean 173.54746 54.88053 299.87219 94.82791

Table 4.7 Paired Samples Correlations N Correlation Sig. Pair 1 VAR00001 & VAR00002 10 .696 .025 VAR00001 = Before DMP VAR00002 = After DMP Table 4.8 Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Mean Pair 1 VAR00001 - VAR00002 971.50000 VAR00001 = Before DMP VAR00002 = After DMP

Std. Deviation Std. Error Mean 218.18659

68.99666

Lower 815.41871

T df Sig. (2-tailed) Upper 1127.581 14.080 9 .000

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From Table 4.6 Paired Sample Statistic above, the mean average peak load before DMP is 15,393 and Standard error mean is 54.88053. The mean average peak load after DMP is 14,422 and the standard error mean is 94.82791. Comparing with the before DMP, there is a decrease average peak load in Java – Bali system.

The correlation between peak load before DMP and after DMP can be seen at Table 4.7 Paired Samples Correlation above. The correlation between peak load before and after DMP is 0.696, it means that the correlation positively correlate. The relationship is quite strong because the coefficient is relatively close to +1. The coefficient of correlation is positive, which indicates that the higher the loads before DMP, the higher the loads will happen after the DMP. The significant confident level 95%, because the p-value = 0.0000 < 0.05.

The first decision by using the p-value, the null hypothesis is rejected, because the decision rule is: Reject H 0 if p-value < 0.05 Otherwise do not reject H 0 . Based on the output Table 4.8 Paired Sample Test, the p-value is 0.0000, it means that p-value = 0.0000 < 0.05 . So the decision rejected the null hypothesis. The p-value indicates that the probability that the peak load before the DMP is the same as after the DMP. So, the degree of belief the null hypothesis is minimal because the probability is too small. The conclusion is that the alternative hypothesis is true. It means that the average peak loads before and after the DMP is difference.

By looking at the data, where n =10, so the degree of freedom which n 1 is 9 (see Table 4.8 Paired Sample Test), so the t test is 14.080. For

the t 9 correspondent with 95% confidence level ( 5% , so for two tails t test used

 0.025 ) t 9 2.2622 (see APPENDIX B). 2

The second decision, the null hypothesis is also rejected, because the decision rule is: Reject t test > t table or –t test < -t table 30

Otherwise do not reject H 0 . The observation with the decision rule, 14.080 t t 9 2.2622 . By rejecting the null hypothesis, it means there is evidence that there is a difference between averages before DMP and after DMP.

Beside of that, the difference average between before and after DMP is the average before DMP is larger that after DMP (See Table 4.6). Because the t test indicates that t 14.080 , it means that the difference VAR00001 = Before DMP is higher than VAR00002 = After DMP is true. From this test result can be concluded that DMP give effect in the electric consumption at WBP.

4.3.2 Trend Line Analysis From the figure above about the forecast, the red line describes the situation of peak loads that happened before the DMP is established, and the blue scatter plot describes the situation of peak loads that happened after the DMP is established. From this can analyze that the peak loads after DMP is established, is decreasing but the growth is faster than the growth of the peak loads before the DMP. It is happened because b0 and

b1 of before DMP is smaller than after DMP.

From the above figure, three periods can be described. The first period from April 2004 to November 2005 is the period of time where the DMP is not established. The second period is from November 2005 to May 2007. It is the period of time where the DMP had only just began. The last period is the forecasted period until December 2010.

In December 2010, the demand for electricity (See APPENDIX C) will reach 18,367MW with the enforcement of DMP. Meanwhile on the same month, the need will only reach 18,147MW without DMP regulation. The result advises PT.PLN to keep implementing DMP, because the demand for electricity till October 2009 will be less than it will be if PT.PLN will not implement DMP. At least with DMP regulation, the government can

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stall some months to avoid electricity deficit especially in electricity distribution in Java-Bali region. Forecasting Before and After DMP 18500 18000 17500 y = 54.46x + 13,572

17000

MW

16500 16000 y = 70.533x + 12,440 15500 15000 14500 14000 13500 0

12

24

36

48

60

72

84

time (Month)

Before DMP

After DMP

Forecast Before DMP

Forecast After DMP

Linear Before DMP

Linear After DMP

Figure 4.3 Forecast Before and After DMP

4.3.3 Sensitivity Analysis From the table below, the largest percentage of electricity use at WBP is households for about 50 percents. In this case, Daya Max Plus regulation is more focused for the industry, so in this sensitivity analysis the scenario is only about the growth of industry. Table 4.9 The Percentage of Electricity Consumption at WBP Consumers Households Industries Business Public TOTAL

7)

Percentage of electricity consumption at WBP 49% 28% 17% 6% 100%

This sensitivity is divided into two scenarios; the first scenario is base case scenario with the assumption that there is 4% of growth per year for households electricity demand, 10% of growth per year for business electricity demand, 2.5% of growth per year for public and 8% of growth

7)

Source: http://pln.co.id

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per year for industry8). The second scenario is the worst case where the industry is forecasted to experience a 13% growth and the other is still the same with the base scenario. The result can be seen at figure 4.4 below.

Sensitivity 18500 18000 17500 17000 MW

16500 16000 15500 15000 14500 14000 13500 0

12

24

36

48

60

72

84

time (month) Before DMP Worst Scenario

After DMP Linear (Before DMP)

Base Scenario Linear (After DMP)

Figure 4.4 Sensitivity for the Forecast

With this condition, DMP regulation that supposedly decreases the peak load at WBP will result a little change for short term. In fact, the load with DMP regulation is the same with the load without DMP regulation by April 2009. (See APPENDIX C)

From the figure 4.4, it can be seen that the load forecast for worst case scenario in April 2009 will be higher than the load forecast of the demand both before and after the DMP regulation. It means that PT.PLN should prepare in facing the increase of electricity demand. The base and worst scenario just showed that the increase of electricity demand will be higher because there are still a lot of factor that can influence the growth of demand.

8)

Source: http://djlpe.esdm.go.id

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